feat: integrate saas-metrics-coach, add finance commands, remove seek-and-analyze-video
- Integrate saas-metrics-coach into cs-financial-analyst agent with SaaS health and unit economics workflows - Add /financial-health and /saas-health slash commands - Add /update-docs repo command for post-creation sync pipeline - Remove seek-and-analyze-video skill (requires paid external API) - Update all documentation (CLAUDE.md, README.md, docs site, marketplace) - Sync Codex CLI (150 skills), Gemini CLI (207 items), fix count consistency - Regenerate 206 MkDocs pages, fix docs/index.md meta 170→171, getting-started.md finance bundle 1→2 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Reza Rezvani
@@ -4,11 +4,11 @@
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"name": "Alireza Rezvani",
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"url": "https://alirezarezvani.com"
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},
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"description": "149 production-ready skill packages for Claude AI across 9 domains: marketing (42), engineering (23+25), C-level advisory (28), regulatory/QMS (12), product (8), project management (6), business growth (4), and finance (1). Includes 224 Python tools, 341 reference documents, 14 agents, and 12 slash commands.",
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"description": "150 production-ready skill packages for Claude AI across 9 domains: marketing (42), engineering (23+25), C-level advisory (28), regulatory/QMS (12), product (8), project management (6), business growth (4), and finance (2). Includes 240 Python tools, 343 reference documents, 14 agents, and 14 slash commands.",
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"homepage": "https://github.com/alirezarezvani/claude-skills",
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"repository": "https://github.com/alirezarezvani/claude-skills",
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"metadata": {
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"description": "149 production-ready skill packages across 9 domains with 224 Python tools, 341 reference documents, 14 agents, and 12 slash commands. Compatible with Claude Code, Codex CLI, Gemini CLI, and OpenClaw.",
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"description": "150 production-ready skill packages across 9 domains with 240 Python tools, 343 reference documents, 14 agents, and 14 slash commands. Compatible with Claude Code, Codex CLI, Gemini CLI, and OpenClaw.",
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"version": "2.1.2"
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},
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"plugins": [
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@@ -177,7 +177,7 @@
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{
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"name": "finance-skills",
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"source": "./finance",
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"description": "1 finance skill: financial analyst with ratio analysis, DCF valuation, budgeting, and forecasting. 4 Python automation tools.",
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"description": "2 finance skills: financial analyst (ratio analysis, DCF valuation, budgeting, forecasting) and SaaS metrics coach (ARR, MRR, churn, CAC, LTV, NRR, Quick Ratio, projections). 7 Python automation tools.",
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"version": "2.1.2",
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"author": {
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"name": "Alireza Rezvani"
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@@ -187,7 +187,14 @@
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"dcf",
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"valuation",
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"budgeting",
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"forecasting"
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"forecasting",
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"saas",
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"metrics",
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"arr",
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"mrr",
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"churn",
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"ltv",
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"cac"
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],
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"category": "finance"
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},
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@@ -19,6 +19,7 @@
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```
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/review → Run local quality checks
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/security-scan → Run security validation
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/update-docs → Post-creation sync (CLI platforms, marketplace, docs, GitHub Pages)
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```
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---
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228
.claude/commands/update-docs.md
Normal file
228
.claude/commands/update-docs.md
Normal file
@@ -0,0 +1,228 @@
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---
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description: Update all the documentation related files.
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---
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After completing any skill creation, agent creation, or command creation in this repository, run the full post-creation sync pipeline below. Execute every step — never skip a task. If something is unclear or a step fails, ask the user for guidance before continuing.
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---
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## Step 1: Inventory what changed
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Scan the working tree to identify what was added, modified, or deleted:
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```bash
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git status --short
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```
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Classify each change:
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- **New skill** — a new folder containing `SKILL.md` under a domain directory
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- **New agent** — a new `.md` file under `agents/`
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- **New command** — a new `.md` file under `commands/`
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- **Modified skill/agent/command** — updated existing files
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- **Deleted skill/agent/command** — removed files
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Report the inventory to the user before proceeding.
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---
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## Step 2: Cross-platform CLI sync
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Ensure all platforms have compatible versions of every skill, agent, and command.
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### 2a. Codex CLI
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Run the Codex sync script to regenerate symlinks and the skills index:
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```bash
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python3 scripts/sync-codex-skills.py --verbose
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```
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Verify the output: check `.codex/skills-index.json` for correct `total_skills` count and that new skills appear in the index.
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### 2b. Gemini CLI
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Run the Gemini sync script:
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```bash
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python3 scripts/sync-gemini-skills.py --verbose
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```
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Verify: check `.gemini/skills-index.json` for correct total count. New skills, agents, and commands should all have corresponding entries and symlinks under `.gemini/skills/`.
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### 2c. OpenClaw
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Verify that `scripts/openclaw-install.sh` will pick up the new skills. The install script uses the same directory structure, so no separate sync is needed — but confirm the new skill directories are not excluded by any filter in the script.
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Report sync results (skill counts per platform) to the user.
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---
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## Step 3: Claude Code plugin marketplace
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### 3a. Domain-level plugin.json
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For each domain that had changes, update the domain's `.claude-plugin/plugin.json`:
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- Update `description` with accurate skill/tool/reference counts
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- Update `version` if needed
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- Verify `source` paths are correct
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Domain plugin.json locations:
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- `marketing-skill/.claude-plugin/plugin.json`
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- `engineering-team/.claude-plugin/plugin.json`
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- `engineering/.claude-plugin/plugin.json`
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- `product-team/.claude-plugin/plugin.json`
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- `c-level-advisor/.claude-plugin/plugin.json`
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- `project-management/.claude-plugin/plugin.json`
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- `ra-qm-team/.claude-plugin/plugin.json`
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- `business-growth/.claude-plugin/plugin.json`
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- `finance/.claude-plugin/plugin.json`
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### 3b. Root marketplace.json
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Update `.claude-plugin/marketplace.json`:
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- Update the top-level `metadata.description` with accurate total counts (skills, tools, references, agents, commands)
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- If a new individual skill plugin entry is needed (for standalone install), add it to the `plugins` array following the existing pattern
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- Update `keywords` arrays if new domains or capabilities were added
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- Verify all `source` paths point to valid directories
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---
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## Step 4: Update documentation files
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### 4a. Root CLAUDE.md
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Update `/CLAUDE.md` (the root project instructions):
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- **Current Scope** line: update skill, tool, reference, agent, and command counts
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- **Repository Structure** comment counts (agents, commands, skills per domain)
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- **Navigation Map** table: verify all domain entries are current
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- **Current Version** section: add a bullet if significant changes were made
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- **Roadmap** section: update counts if needed
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### 4b. Domain-level CLAUDE.md files
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For each domain that had changes, update its `CLAUDE.md`:
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- Skill count and list
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- Script/tool count
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- Agent references
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- Command references
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- Any new cross-domain integrations
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Domain CLAUDE.md locations:
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- `agents/CLAUDE.md`
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- `marketing-skill/CLAUDE.md`
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- `product-team/CLAUDE.md`
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- `engineering-team/CLAUDE.md`
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- `c-level-advisor/CLAUDE.md`
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- `project-management/CLAUDE.md`
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- `ra-qm-team/CLAUDE.md`
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- `business-growth/CLAUDE.md`
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- `finance/CLAUDE.md`
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- `standards/CLAUDE.md`
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- `templates/CLAUDE.md`
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### 4c. Root README.md
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Update `/README.md`:
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- Badge counts (Skills, Agents, Commands)
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- Tagline/intro paragraph skill count
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- Skills Overview table (domain rows with correct counts)
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- Quick Install section (install commands, skill counts in comments)
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- Python Analysis Tools section (tool count, add examples for new tools)
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- FAQ section (update any counts mentioned)
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### 4d. docs/index.md (GitHub Pages homepage)
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Update `docs/index.md`:
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- `description` meta tag
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- Hero subtitle skill count
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- Grid cards (skills, tools, agents, commands counts)
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- Domain cards (skill counts per domain, links)
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### 4e. docs/getting-started.md
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Update `docs/getting-started.md`:
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- `description` meta tag
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- Available Bundles table (skill counts per bundle)
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- Python Tools section (tool count)
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- FAQ answers (any count references)
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---
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## Step 5: Regenerate GitHub Pages
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Run the docs generation script to create/update all MkDocs pages:
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```bash
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python3 scripts/generate-docs.py
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```
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This generates pages for:
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- Every skill (from SKILL.md files)
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- Every agent (from agents/*.md)
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- Every command (from commands/*.md)
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- Index pages for skills, agents, and commands sections
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### 5a. Update mkdocs.yml navigation
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Open `mkdocs.yml` and update the `nav:` section:
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- Add new skill pages under the correct domain section
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- Add new agent pages under the Agents section
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- Add new command pages under the Commands section
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- Update `site_description` with current counts
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### 5b. Verify the build
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```bash
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python3 -m mkdocs build 2>&1 | tail -5
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```
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The build should complete without errors. Warnings about relative links in SKILL.md files are expected and can be ignored (they reference skill-internal paths like `references/` and `scripts/`).
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Report the build result and page count to the user.
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---
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## Step 6: Consistency verification
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Run a final consistency check across all updated files:
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1. **Count consistency** — Verify the same skill/agent/command/tool counts appear in:
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- Root CLAUDE.md
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- Root README.md
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- docs/index.md
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- docs/getting-started.md
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- .claude-plugin/marketplace.json
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2. **Path validation** — Verify all `source` paths in marketplace.json point to existing directories
|
||||
|
||||
3. **New script verification** — If new Python scripts were added, verify they run:
|
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```bash
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python3 path/to/new/script.py --help
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```
|
||||
|
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4. **Frontmatter check** — Verify all new SKILL.md, agent, and command files have valid YAML frontmatter with at minimum `name` and `description` fields
|
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|
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Report any inconsistencies found and fix them before finishing.
|
||||
|
||||
---
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||||
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## Step 7: Summary report
|
||||
|
||||
Present a summary to the user:
|
||||
|
||||
| Item | Status |
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||||
|------|--------|
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| New skills added | [list] |
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| New agents added | [list] |
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||||
| New commands added | [list] |
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||||
| Codex CLI sync | count |
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||||
| Gemini CLI sync | count |
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||||
| OpenClaw compatible | yes/no |
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||||
| Marketplace updated | yes/no |
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||||
| CLAUDE.md files updated | [count]/[total] |
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||||
| README.md updated | yes/no |
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||||
| GitHub Pages regenerated | [page count] pages |
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||||
| MkDocs build | pass/fail |
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||||
| Consistency check | pass/fail |
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|
||||
Ask the user if they want to commit and push the changes.
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@@ -3,7 +3,7 @@
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||||
"name": "claude-code-skills",
|
||||
"description": "Production-ready skill packages for AI agents - Marketing, Engineering, Product, C-Level, PM, and RA/QM",
|
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"repository": "https://github.com/alirezarezvani/claude-skills",
|
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"total_skills": 151,
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"total_skills": 150,
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"skills": [
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{
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"name": "contract-and-proposal-writer",
|
||||
@@ -713,12 +713,6 @@
|
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"category": "marketing",
|
||||
"description": "When the user wants to implement, audit, or validate structured data (schema markup) on their website. Use when the user mentions 'structured data,' 'schema.org,' 'JSON-LD,' 'rich results,' 'rich snippets,' 'schema markup,' 'FAQ schema,' 'Product schema,' 'HowTo schema,' or 'structured data errors in Search Console.' Also use when someone asks why their content isn't showing rich results or wants to improve AI search visibility. NOT for general SEO audits (use seo-audit) or technical SEO crawl issues (use site-architecture)."
|
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},
|
||||
{
|
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"name": "seek-and-analyze-video",
|
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"source": "../../marketing-skill/seek-and-analyze-video",
|
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"category": "marketing",
|
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"description": "Video intelligence and content analysis using Memories.ai LVMM. Discover videos on TikTok, YouTube, Instagram by topic or creator. Analyze video content, summarize meetings, build searchable knowledge bases across multiple videos. Use for video research, competitor content analysis, meeting notes, lecture summaries, or building video knowledge libraries."
|
||||
},
|
||||
{
|
||||
"name": "seo-audit",
|
||||
"source": "../../marketing-skill/seo-audit",
|
||||
@@ -939,7 +933,7 @@
|
||||
"description": "Financial analysis, valuation, and forecasting skills"
|
||||
},
|
||||
"marketing": {
|
||||
"count": 43,
|
||||
"count": 42,
|
||||
"source": "../../marketing-skill",
|
||||
"description": "Marketing, content, and demand generation skills"
|
||||
},
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
../../marketing-skill/seek-and-analyze-video
|
||||
@@ -1,7 +1,7 @@
|
||||
{
|
||||
"version": "1.0.0",
|
||||
"name": "gemini-cli-skills",
|
||||
"total_skills": 204,
|
||||
"total_skills": 207,
|
||||
"skills": [
|
||||
{
|
||||
"name": "cs-agile-product-owner",
|
||||
@@ -36,7 +36,7 @@
|
||||
{
|
||||
"name": "cs-financial-analyst",
|
||||
"category": "agent",
|
||||
"description": "Financial Analyst agent for DCF valuation, financial modeling, budgeting, and forecasting. Orchestrates finance skills. Spawn when users need financial statements analysis, valuation models, budget planning, ratio analysis, or industry benchmarking."
|
||||
"description": "Financial Analyst agent for DCF valuation, financial modeling, budgeting, forecasting, and SaaS metrics (ARR, MRR, churn, CAC, LTV, NRR). Orchestrates finance skills. Spawn when users need financial analysis, valuation models, budget planning, ratio analysis, SaaS health checks, or unit economics projections."
|
||||
},
|
||||
{
|
||||
"name": "cs-growth-strategist",
|
||||
@@ -278,6 +278,11 @@
|
||||
"category": "command",
|
||||
"description": "Build competitive analysis matrices with scoring and gap analysis. Usage: /competitive-matrix <analyze> [options]"
|
||||
},
|
||||
{
|
||||
"name": "financial-health",
|
||||
"category": "command",
|
||||
"description": "Run financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecasts. Usage: /financial-health <ratios|dcf|budget|forecast> <data.json>"
|
||||
},
|
||||
{
|
||||
"name": "okr",
|
||||
"category": "command",
|
||||
@@ -308,6 +313,11 @@
|
||||
"category": "command",
|
||||
"description": "RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize <features.csv> [options]"
|
||||
},
|
||||
{
|
||||
"name": "saas-health",
|
||||
"category": "command",
|
||||
"description": "Calculate SaaS health metrics (ARR, MRR, churn, CAC, LTV, NRR) and benchmark against industry standards. Usage: /saas-health <metrics|quick-ratio|simulate> [options]"
|
||||
},
|
||||
{
|
||||
"name": "sprint-health",
|
||||
"category": "command",
|
||||
@@ -663,6 +673,11 @@
|
||||
"category": "finance",
|
||||
"description": "Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis."
|
||||
},
|
||||
{
|
||||
"name": "saas-metrics-coach",
|
||||
"category": "finance",
|
||||
"description": "SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS business is doing."
|
||||
},
|
||||
{
|
||||
"name": "ab-test-setup",
|
||||
"category": "marketing",
|
||||
@@ -1038,7 +1053,7 @@
|
||||
"description": "C-level resources"
|
||||
},
|
||||
"command": {
|
||||
"count": 12,
|
||||
"count": 14,
|
||||
"description": "Command resources"
|
||||
},
|
||||
"engineering": {
|
||||
@@ -1050,7 +1065,7 @@
|
||||
"description": "Engineering-advanced resources"
|
||||
},
|
||||
"finance": {
|
||||
"count": 2,
|
||||
"count": 3,
|
||||
"description": "Finance resources"
|
||||
},
|
||||
"marketing": {
|
||||
|
||||
1
.gemini/skills/financial-health/SKILL.md
Symbolic link
1
.gemini/skills/financial-health/SKILL.md
Symbolic link
@@ -0,0 +1 @@
|
||||
../../../commands/financial-health.md
|
||||
1
.gemini/skills/saas-health/SKILL.md
Symbolic link
1
.gemini/skills/saas-health/SKILL.md
Symbolic link
@@ -0,0 +1 @@
|
||||
../../../commands/saas-health.md
|
||||
1
.gemini/skills/saas-metrics-coach/SKILL.md
Symbolic link
1
.gemini/skills/saas-metrics-coach/SKILL.md
Symbolic link
@@ -0,0 +1 @@
|
||||
../../../finance/saas-metrics-coach/SKILL.md
|
||||
66
.github/pull_request_template.md
vendored
66
.github/pull_request_template.md
vendored
@@ -1,60 +1,24 @@
|
||||
## Summary
|
||||
<!-- Brief description of what this PR accomplishes -->
|
||||
|
||||
<!-- What does this PR add or change? -->
|
||||
|
||||
## Context
|
||||
<!-- Why these changes are needed. Link to issues, discussions, or explain the problem being solved. -->
|
||||
## Checklist
|
||||
|
||||
-
|
||||
- [ ] **Target branch is `dev`** (not `main` — PRs to main will be auto-closed)
|
||||
- [ ] Skill has `SKILL.md` with valid YAML frontmatter (`name`, `description`, `license`)
|
||||
- [ ] Scripts (if any) run with `--help` without errors
|
||||
- [ ] No hardcoded API keys, tokens, or secrets
|
||||
- [ ] No vendor-locked dependencies without open-source fallback
|
||||
- [ ] Follows existing directory structure (`domain/skill-name/SKILL.md`)
|
||||
|
||||
## Changes
|
||||
<!-- High-level overview of what was modified -->
|
||||
## Type of Change
|
||||
|
||||
-
|
||||
- [ ] New skill
|
||||
- [ ] Improvement to existing skill
|
||||
- [ ] Bug fix
|
||||
- [ ] Documentation
|
||||
- [ ] Infrastructure / CI
|
||||
|
||||
## Testing
|
||||
<!-- Confirm all checks have passed -->
|
||||
|
||||
- [ ] Local quality checks passed (Python syntax, markdown links)
|
||||
- [ ] All Python scripts tested and working
|
||||
- [ ] Skills validated with Claude (if applicable)
|
||||
- [ ] `ci-quality-gate` workflow will pass
|
||||
- [ ] Manual testing completed
|
||||
|
||||
**Testing Details**:
|
||||
<!-- Describe what you tested and how -->
|
||||
|
||||
|
||||
## Security
|
||||
<!-- Confirm security requirements met -->
|
||||
|
||||
- [ ] No secrets, credentials, or API keys committed
|
||||
- [ ] No destructive commands in generated outputs
|
||||
- [ ] Path traversal vulnerabilities checked
|
||||
- [ ] Dependencies reviewed (if added)
|
||||
|
||||
## Documentation
|
||||
<!-- Confirm documentation updated -->
|
||||
|
||||
- [ ] README.md updated (if applicable)
|
||||
- [ ] CLAUDE.md updated (if applicable)
|
||||
- [ ] Inline code comments added for complex logic
|
||||
- [ ] CHANGELOG.md updated (if applicable)
|
||||
- [ ] Skill SKILL.md files updated (if applicable)
|
||||
|
||||
## Reviewers
|
||||
<!-- Tag reviewers who should look at this PR -->
|
||||
|
||||
- [ ] @
|
||||
|
||||
## Related Issues
|
||||
<!-- Link issues this PR addresses using keywords: Fixes, Closes, Resolves, Related to -->
|
||||
|
||||
Fixes #
|
||||
Closes #
|
||||
Related to #
|
||||
|
||||
---
|
||||
|
||||
**Type**: <!-- feat | fix | docs | refactor | chore | ci | test | perf | skill -->
|
||||
**Scope**: <!-- component affected (e.g., marketing-skill, workflows, agents, templates) -->
|
||||
<!-- How did you verify this works? -->
|
||||
|
||||
20
CLAUDE.md
20
CLAUDE.md
@@ -6,7 +6,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
|
||||
|
||||
This is a **comprehensive skills library** for Claude AI and Claude Code - reusable, production-ready skill packages that bundle domain expertise, best practices, analysis tools, and strategic frameworks. The repository provides modular skills that teams can download and use directly in their workflows.
|
||||
|
||||
**Current Scope:** 170 production-ready skills across 9 domains with 210+ Python automation tools, 310+ reference guides, 12 agents, and 5 slash commands.
|
||||
**Current Scope:** 171 production-ready skills across 9 domains with 240 Python automation tools, 343 reference guides, 14 agents, and 14 slash commands.
|
||||
|
||||
**Key Distinction**: This is NOT a traditional application. It's a library of skill packages meant to be extracted and deployed by users into their own Claude workflows.
|
||||
|
||||
@@ -25,7 +25,7 @@ This repository uses **modular documentation**. For domain-specific guidance, se
|
||||
| **Project Management** | [project-management/CLAUDE.md](project-management/CLAUDE.md) | Atlassian MCP, Jira/Confluence integration |
|
||||
| **RA/QM Compliance** | [ra-qm-team/CLAUDE.md](ra-qm-team/CLAUDE.md) | ISO 13485, MDR, FDA, GDPR, ISO 27001 compliance |
|
||||
| **Business & Growth** | [business-growth/CLAUDE.md](business-growth/CLAUDE.md) | Customer success, sales engineering, revenue operations |
|
||||
| **Finance** | [finance/CLAUDE.md](finance/CLAUDE.md) | Financial analysis, DCF valuation, budgeting, forecasting |
|
||||
| **Finance** | [finance/CLAUDE.md](finance/CLAUDE.md) | Financial analysis, DCF valuation, budgeting, forecasting, SaaS metrics |
|
||||
| **Standards Library** | [standards/CLAUDE.md](standards/CLAUDE.md) | Communication, quality, git, security standards |
|
||||
| **Templates** | [templates/CLAUDE.md](templates/CLAUDE.md) | Template system usage |
|
||||
|
||||
@@ -36,8 +36,8 @@ This repository uses **modular documentation**. For domain-specific guidance, se
|
||||
```
|
||||
claude-code-skills/
|
||||
├── .claude-plugin/ # Plugin registry (marketplace.json)
|
||||
├── agents/ # 12 cs-* prefixed agents across all domains
|
||||
├── commands/ # 5 slash commands (changelog, tdd, tech-debt, etc.)
|
||||
├── agents/ # 14 cs-* prefixed agents across all domains
|
||||
├── commands/ # 14 slash commands (changelog, tdd, saas-health, etc.)
|
||||
├── engineering-team/ # 23 core engineering skills + Playwright Pro + Self-Improving Agent
|
||||
├── engineering/ # 25 POWERFUL-tier advanced skills
|
||||
├── product-team/ # 8 product skills + Python tools
|
||||
@@ -46,7 +46,7 @@ claude-code-skills/
|
||||
├── project-management/ # 6 PM skills + Atlassian MCP
|
||||
├── ra-qm-team/ # 12 RA/QM compliance skills
|
||||
├── business-growth/ # 4 business & growth skills + Python tools
|
||||
├── finance/ # 1 finance skill + Python tools
|
||||
├── finance/ # 2 finance skills + Python tools
|
||||
├── eval-workspace/ # Skill evaluation results (Tessl)
|
||||
├── standards/ # 5 standards library files
|
||||
├── templates/ # Reusable templates
|
||||
@@ -148,11 +148,11 @@ See [standards/git/git-workflow-standards.md](standards/git/git-workflow-standar
|
||||
|
||||
## Roadmap
|
||||
|
||||
**Phase 1-2 Complete:** 170 production-ready skills deployed across 9 domains
|
||||
- Engineering Core (23), Engineering POWERFUL (25), Product (8), Marketing (42), PM (6), C-Level (28), RA/QM (12), Business & Growth (4), Finance (1)
|
||||
- 210+ Python automation tools, 310+ reference guides, 12 agents, 5 commands
|
||||
**Phase 1-2 Complete:** 171 production-ready skills deployed across 9 domains
|
||||
- Engineering Core (23), Engineering POWERFUL (25), Product (8), Marketing (42), PM (6), C-Level (28), RA/QM (12), Business & Growth (4), Finance (2)
|
||||
- 240 Python automation tools, 343 reference guides, 14 agents, 14 commands
|
||||
- Complete enterprise coverage from engineering through regulatory compliance, sales, customer success, and finance
|
||||
- MkDocs Material docs site with 170+ indexed pages for SEO
|
||||
- MkDocs Material docs site with 206 indexed pages for SEO
|
||||
|
||||
See domain-specific roadmaps in each skill folder's README.md or roadmap files.
|
||||
|
||||
@@ -192,4 +192,4 @@ See domain-specific roadmaps in each skill folder's README.md or roadmap files.
|
||||
|
||||
**Last Updated:** March 10, 2026
|
||||
**Version:** v2.1.2
|
||||
**Status:** 170 skills deployed across 9 domains, 18 marketplace plugins, docs site live
|
||||
**Status:** 171 skills deployed across 9 domains, 19 marketplace plugins, docs site live
|
||||
|
||||
21
README.md
21
README.md
@@ -1,11 +1,11 @@
|
||||
# Claude Code Skills & Plugins
|
||||
|
||||
**170 production-ready skills and plugins for Claude Code, OpenAI Codex, Gemini CLI, and OpenClaw** — reusable expertise bundles that transform AI coding agents into specialized professionals across engineering, product, marketing, compliance, and more.
|
||||
**171 production-ready skills and plugins for Claude Code, OpenAI Codex, Gemini CLI, and OpenClaw** — reusable expertise bundles that transform AI coding agents into specialized professionals across engineering, product, marketing, compliance, and more.
|
||||
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](#skills-overview)
|
||||
[](#agents)
|
||||
[](#commands)
|
||||
[](#skills-overview)
|
||||
[](#agents)
|
||||
[](#commands)
|
||||
[](https://github.com/alirezarezvani/claude-skills/stargazers)
|
||||
[](https://getskillcheck.com)
|
||||
|
||||
@@ -52,7 +52,7 @@ cd claude-skills
|
||||
/plugin install pm-skills@claude-code-skills # 6 project management
|
||||
/plugin install c-level-skills@claude-code-skills # 28 C-level advisory (full C-suite)
|
||||
/plugin install business-growth-skills@claude-code-skills # 4 business & growth
|
||||
/plugin install finance-skills@claude-code-skills # 1 finance
|
||||
/plugin install finance-skills@claude-code-skills # 2 finance (analyst + SaaS metrics)
|
||||
|
||||
# Or install individual skills
|
||||
/plugin install skill-security-auditor@claude-code-skills # Security scanner
|
||||
@@ -85,7 +85,7 @@ git clone https://github.com/alirezarezvani/claude-skills.git
|
||||
|
||||
## Skills Overview
|
||||
|
||||
**170 skills across 9 domains:**
|
||||
**171 skills across 9 domains:**
|
||||
|
||||
| Domain | Skills | Highlights | Details |
|
||||
|--------|--------|------------|---------|
|
||||
@@ -99,7 +99,7 @@ git clone https://github.com/alirezarezvani/claude-skills.git
|
||||
| **🏥 Regulatory & QM** | 12 | ISO 13485, MDR 2017/745, FDA, ISO 27001, GDPR, CAPA, risk management | [ra-qm-team/](ra-qm-team/) |
|
||||
| **💼 C-Level Advisory** | 28 | Full C-suite (10 roles) + orchestration + board meetings + culture & collaboration | [c-level-advisor/](c-level-advisor/) |
|
||||
| **📈 Business & Growth** | 4 | Customer success, sales engineer, revenue ops, contracts & proposals | [business-growth/](business-growth/) |
|
||||
| **💰 Finance** | 1 | Financial analyst (DCF, budgeting, forecasting) | [finance/](finance/) |
|
||||
| **💰 Finance** | 2 | Financial analyst (DCF, budgeting, forecasting), SaaS metrics coach (ARR, MRR, churn, LTV, CAC) | [finance/](finance/) |
|
||||
|
||||
---
|
||||
|
||||
@@ -189,9 +189,12 @@ for MDR Annex II compliance gaps.
|
||||
|
||||
## Python Analysis Tools
|
||||
|
||||
237 CLI tools ship with the skills (all verified, stdlib-only):
|
||||
240 CLI tools ship with the skills (all verified, stdlib-only):
|
||||
|
||||
```bash
|
||||
# SaaS health check
|
||||
python3 finance/saas-metrics-coach/scripts/metrics_calculator.py --mrr 80000 --customers 200 --churned 3 --json
|
||||
|
||||
# Brand voice analysis
|
||||
python3 marketing-skill/content-production/scripts/brand_voice_analyzer.py article.txt
|
||||
|
||||
@@ -232,7 +235,7 @@ Yes. Skills work natively with Claude Code, OpenAI Codex, Gemini CLI, and OpenCl
|
||||
No. We follow semantic versioning and maintain backward compatibility within patch releases. Existing script arguments, plugin source paths, and SKILL.md structures are never changed in patch versions. See the [CHANGELOG](CHANGELOG.md) for details on each release.
|
||||
|
||||
**Are the Python tools dependency-free?**
|
||||
Yes. All 237 Python CLI tools use the standard library only — zero pip installs required. Every script is verified to run with `--help`.
|
||||
Yes. All 240 Python CLI tools use the standard library only — zero pip installs required. Every script is verified to run with `--help`.
|
||||
|
||||
**How do I create my own Claude Code skill?**
|
||||
Each skill is a folder with a `SKILL.md` (frontmatter + instructions), optional `scripts/`, `references/`, and `assets/`. See the [Skills & Agents Factory](https://github.com/alirezarezvani/claude-code-skills-agents-factory) for a step-by-step guide.
|
||||
|
||||
@@ -3,7 +3,7 @@ name: cs-engineering-lead
|
||||
description: Engineering Team Lead agent for coordinating QA, security, data engineering, ML, and frontend/backend teams. Orchestrates engineering-team skills for team-level technical decisions. Spawn when users need team coordination, tech stack evaluation, incident response, or cross-functional engineering work.
|
||||
skills: engineering-team
|
||||
domain: engineering
|
||||
model: sonnet
|
||||
model: opus
|
||||
tools: [Read, Write, Bash, Grep, Glob]
|
||||
---
|
||||
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
---
|
||||
name: cs-financial-analyst
|
||||
description: Financial Analyst agent for DCF valuation, financial modeling, budgeting, and forecasting. Orchestrates finance skills. Spawn when users need financial statements analysis, valuation models, budget planning, ratio analysis, or industry benchmarking.
|
||||
description: Financial Analyst agent for DCF valuation, financial modeling, budgeting, forecasting, and SaaS metrics (ARR, MRR, churn, CAC, LTV, NRR). Orchestrates finance skills. Spawn when users need financial analysis, valuation models, budget planning, ratio analysis, SaaS health checks, or unit economics projections.
|
||||
skills: finance
|
||||
domain: finance
|
||||
model: sonnet
|
||||
model: opus
|
||||
tools: [Read, Write, Bash, Grep, Glob]
|
||||
---
|
||||
|
||||
@@ -15,9 +15,14 @@ Financial analyst covering valuation, ratio analysis, forecasting, and industry-
|
||||
|
||||
## Skill Integration
|
||||
|
||||
- `finance/financial-analyst` — DCF modeling, ratio analysis, forecasting, scenario planning
|
||||
- Scripts: `dcf_valuation.py`, `ratio_calculator.py`, `forecast_builder.py`, `budget_variance_analyzer.py`
|
||||
- References: `financial-ratios-guide.md`, `valuation-methodology.md`, `forecasting-best-practices.md`, `industry-adaptations.md`
|
||||
### finance/financial-analyst — Traditional Financial Analysis
|
||||
- Scripts: `dcf_valuation.py`, `ratio_calculator.py`, `forecast_builder.py`, `budget_variance_analyzer.py`
|
||||
- References: `financial-ratios-guide.md`, `valuation-methodology.md`, `forecasting-best-practices.md`, `industry-adaptations.md`
|
||||
|
||||
### finance/saas-metrics-coach — SaaS Financial Health
|
||||
- Scripts: `metrics_calculator.py`, `quick_ratio_calculator.py`, `unit_economics_simulator.py`
|
||||
- References: `formulas.md`, `benchmarks.md`
|
||||
- Assets: `input-template.md`
|
||||
|
||||
## Core Workflows
|
||||
|
||||
@@ -49,6 +54,20 @@ Financial analyst covering valuation, ratio analysis, forecasting, and industry-
|
||||
4. Build monthly cash flow projection
|
||||
5. Define variance thresholds and review cadence
|
||||
|
||||
### 5. SaaS Health Check
|
||||
1. Collect MRR, customer count, churn, CAC data from user
|
||||
2. Run `metrics_calculator.py` to compute ARR, LTV, LTV:CAC, NRR, payback
|
||||
3. Run `quick_ratio_calculator.py` if expansion/churn MRR available
|
||||
4. Benchmark each metric against stage/segment via `benchmarks.md`
|
||||
5. Flag CRITICAL/WATCH metrics and recommend top 3 actions
|
||||
|
||||
### 6. SaaS Unit Economics Projection
|
||||
1. Take current MRR, growth rate, churn rate, CAC from user
|
||||
2. Run `unit_economics_simulator.py` to project 12 months forward
|
||||
3. Assess runway, profitability timeline, and growth trajectory
|
||||
4. Cross-reference with `forecast_builder.py` for scenario modeling
|
||||
5. Present monthly projections with summary and risk flags
|
||||
|
||||
## Output Standards
|
||||
- Valuations → range with methodology stated (DCF, comparables, precedent)
|
||||
- Ratios → benchmarked against industry with trend arrows
|
||||
@@ -62,6 +81,29 @@ Financial analyst covering valuation, ratio analysis, forecasting, and industry-
|
||||
- **Budget Variance:** Departmental budgets maintained within 10% of plan
|
||||
- **Analysis Turnaround:** Financial models delivered within 48 hours of data receipt
|
||||
|
||||
## Integration Examples
|
||||
|
||||
```bash
|
||||
# SaaS health check — full metrics from raw numbers
|
||||
python ../../finance/saas-metrics-coach/scripts/metrics_calculator.py \
|
||||
--mrr 80000 --mrr-last 75000 --customers 200 --churned 3 \
|
||||
--new-customers 15 --sm-spend 25000 --gross-margin 72 --json
|
||||
|
||||
# Quick ratio — growth efficiency
|
||||
python ../../finance/saas-metrics-coach/scripts/quick_ratio_calculator.py \
|
||||
--new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500
|
||||
|
||||
# 12-month projection
|
||||
python ../../finance/saas-metrics-coach/scripts/unit_economics_simulator.py \
|
||||
--mrr 80000 --growth 8 --churn 1.5 --cac 1667 --json
|
||||
|
||||
# Traditional ratio analysis
|
||||
python ../../finance/financial-analyst/scripts/ratio_calculator.py financial_data.json --format json
|
||||
|
||||
# DCF valuation
|
||||
python ../../finance/financial-analyst/scripts/dcf_valuation.py valuation_data.json --format json
|
||||
```
|
||||
|
||||
## Related Agents
|
||||
|
||||
- [cs-ceo-advisor](../c-level/cs-ceo-advisor.md) -- Strategic financial decisions, board reporting, and fundraising planning
|
||||
|
||||
38
commands/financial-health.md
Normal file
38
commands/financial-health.md
Normal file
@@ -0,0 +1,38 @@
|
||||
---
|
||||
name: financial-health
|
||||
description: Run financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecasts. Usage: /financial-health <ratios|dcf|budget|forecast> <data.json>
|
||||
---
|
||||
|
||||
# /financial-health
|
||||
|
||||
Analyze financial statements, build valuation models, assess budget variances, and construct forecasts.
|
||||
|
||||
## Usage
|
||||
|
||||
```
|
||||
/financial-health ratios <financial_data.json> [--format json|text]
|
||||
/financial-health dcf <valuation_data.json> [--format json|text]
|
||||
/financial-health budget <budget_data.json> [--format json|text]
|
||||
/financial-health forecast <forecast_data.json> [--format json|text]
|
||||
```
|
||||
|
||||
## Examples
|
||||
|
||||
```
|
||||
/financial-health ratios quarterly_financials.json --format json
|
||||
/financial-health dcf acme_valuation.json
|
||||
/financial-health budget q1_budget.json --format json
|
||||
/financial-health forecast revenue_history.json
|
||||
```
|
||||
|
||||
## Scripts
|
||||
- `finance/financial-analyst/scripts/ratio_calculator.py` — Profitability, liquidity, leverage, efficiency, valuation ratios
|
||||
- `finance/financial-analyst/scripts/dcf_valuation.py` — DCF enterprise and equity valuation with sensitivity analysis
|
||||
- `finance/financial-analyst/scripts/budget_variance_analyzer.py` — Actual vs budget vs prior year variance analysis
|
||||
- `finance/financial-analyst/scripts/forecast_builder.py` — Driver-based revenue forecasting with scenario modeling
|
||||
|
||||
## Skill Reference
|
||||
→ `finance/financial-analyst/SKILL.md`
|
||||
|
||||
## Related Commands
|
||||
- `/saas-health` — SaaS-specific metrics (ARR, MRR, churn, CAC, LTV, Quick Ratio)
|
||||
35
commands/saas-health.md
Normal file
35
commands/saas-health.md
Normal file
@@ -0,0 +1,35 @@
|
||||
---
|
||||
name: saas-health
|
||||
description: Calculate SaaS health metrics (ARR, MRR, churn, CAC, LTV, NRR) and benchmark against industry standards. Usage: /saas-health <metrics|quick-ratio|simulate> [options]
|
||||
---
|
||||
|
||||
# /saas-health
|
||||
|
||||
Calculate SaaS financial health metrics from raw business numbers, benchmark against industry standards, and project forward.
|
||||
|
||||
## Usage
|
||||
|
||||
```
|
||||
/saas-health metrics --mrr <amount> [--customers <n>] [--churned <n>] [--json]
|
||||
/saas-health quick-ratio --new-mrr <amount> --churned <amount> [--expansion <amount>]
|
||||
/saas-health simulate --mrr <amount> --growth <pct> --churn <pct> --cac <amount> [--json]
|
||||
```
|
||||
|
||||
## Examples
|
||||
|
||||
```
|
||||
/saas-health metrics --mrr 80000 --customers 200 --churned 3 --new-customers 15 --sm-spend 25000
|
||||
/saas-health quick-ratio --new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500
|
||||
/saas-health simulate --mrr 50000 --growth 10 --churn 3 --cac 2000
|
||||
```
|
||||
|
||||
## Scripts
|
||||
- `finance/saas-metrics-coach/scripts/metrics_calculator.py` — Core SaaS metrics (ARR, MRR, churn, CAC, LTV, NRR, payback)
|
||||
- `finance/saas-metrics-coach/scripts/quick_ratio_calculator.py` — Growth efficiency ratio
|
||||
- `finance/saas-metrics-coach/scripts/unit_economics_simulator.py` — 12-month forward projection
|
||||
|
||||
## Skill Reference
|
||||
→ `finance/saas-metrics-coach/SKILL.md`
|
||||
|
||||
## Related Commands
|
||||
- `/financial-health` — Traditional financial analysis (ratios, DCF, budgets)
|
||||
@@ -18,9 +18,14 @@ Financial analyst covering valuation, ratio analysis, forecasting, and industry-
|
||||
|
||||
## Skill Integration
|
||||
|
||||
- `finance/financial-analyst` — DCF modeling, ratio analysis, forecasting, scenario planning
|
||||
- Scripts: `dcf_valuation.py`, `ratio_calculator.py`, `forecast_builder.py`, `budget_variance_analyzer.py`
|
||||
- References: `financial-ratios-guide.md`, `valuation-methodology.md`, `forecasting-best-practices.md`, `industry-adaptations.md`
|
||||
### finance/financial-analyst — Traditional Financial Analysis
|
||||
- Scripts: `dcf_valuation.py`, `ratio_calculator.py`, `forecast_builder.py`, `budget_variance_analyzer.py`
|
||||
- References: `financial-ratios-guide.md`, `valuation-methodology.md`, `forecasting-best-practices.md`, `industry-adaptations.md`
|
||||
|
||||
### finance/saas-metrics-coach — SaaS Financial Health
|
||||
- Scripts: `metrics_calculator.py`, `quick_ratio_calculator.py`, `unit_economics_simulator.py`
|
||||
- References: `formulas.md`, `benchmarks.md`
|
||||
- Assets: `input-template.md`
|
||||
|
||||
## Core Workflows
|
||||
|
||||
@@ -52,6 +57,20 @@ Financial analyst covering valuation, ratio analysis, forecasting, and industry-
|
||||
4. Build monthly cash flow projection
|
||||
5. Define variance thresholds and review cadence
|
||||
|
||||
### 5. SaaS Health Check
|
||||
1. Collect MRR, customer count, churn, CAC data from user
|
||||
2. Run `metrics_calculator.py` to compute ARR, LTV, LTV:CAC, NRR, payback
|
||||
3. Run `quick_ratio_calculator.py` if expansion/churn MRR available
|
||||
4. Benchmark each metric against stage/segment via `benchmarks.md`
|
||||
5. Flag CRITICAL/WATCH metrics and recommend top 3 actions
|
||||
|
||||
### 6. SaaS Unit Economics Projection
|
||||
1. Take current MRR, growth rate, churn rate, CAC from user
|
||||
2. Run `unit_economics_simulator.py` to project 12 months forward
|
||||
3. Assess runway, profitability timeline, and growth trajectory
|
||||
4. Cross-reference with `forecast_builder.py` for scenario modeling
|
||||
5. Present monthly projections with summary and risk flags
|
||||
|
||||
## Output Standards
|
||||
- Valuations → range with methodology stated (DCF, comparables, precedent)
|
||||
- Ratios → benchmarked against industry with trend arrows
|
||||
@@ -65,6 +84,29 @@ Financial analyst covering valuation, ratio analysis, forecasting, and industry-
|
||||
- **Budget Variance:** Departmental budgets maintained within 10% of plan
|
||||
- **Analysis Turnaround:** Financial models delivered within 48 hours of data receipt
|
||||
|
||||
## Integration Examples
|
||||
|
||||
```bash
|
||||
# SaaS health check — full metrics from raw numbers
|
||||
python ../../finance/saas-metrics-coach/scripts/metrics_calculator.py \
|
||||
--mrr 80000 --mrr-last 75000 --customers 200 --churned 3 \
|
||||
--new-customers 15 --sm-spend 25000 --gross-margin 72 --json
|
||||
|
||||
# Quick ratio — growth efficiency
|
||||
python ../../finance/saas-metrics-coach/scripts/quick_ratio_calculator.py \
|
||||
--new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500
|
||||
|
||||
# 12-month projection
|
||||
python ../../finance/saas-metrics-coach/scripts/unit_economics_simulator.py \
|
||||
--mrr 80000 --growth 8 --churn 1.5 --cac 1667 --json
|
||||
|
||||
# Traditional ratio analysis
|
||||
python ../../finance/financial-analyst/scripts/ratio_calculator.py financial_data.json --format json
|
||||
|
||||
# DCF valuation
|
||||
python ../../finance/financial-analyst/scripts/dcf_valuation.py valuation_data.json --format json
|
||||
```
|
||||
|
||||
## Related Agents
|
||||
|
||||
- [cs-ceo-advisor](../c-level/cs-ceo-advisor.md) -- Strategic financial decisions, board reporting, and fundraising planning
|
||||
|
||||
45
docs/commands/financial-health.md
Normal file
45
docs/commands/financial-health.md
Normal file
@@ -0,0 +1,45 @@
|
||||
---
|
||||
title: "/financial-health"
|
||||
description: "/financial-health — Claude Code slash command."
|
||||
---
|
||||
|
||||
# /financial-health
|
||||
|
||||
**Type:** Slash Command | **Source:** [`commands/financial-health.md`](https://github.com/alirezarezvani/claude-skills/tree/main/commands/financial-health.md)
|
||||
|
||||
---
|
||||
|
||||
|
||||
# /financial-health
|
||||
|
||||
Analyze financial statements, build valuation models, assess budget variances, and construct forecasts.
|
||||
|
||||
## Usage
|
||||
|
||||
```
|
||||
/financial-health ratios <financial_data.json> [--format json|text]
|
||||
/financial-health dcf <valuation_data.json> [--format json|text]
|
||||
/financial-health budget <budget_data.json> [--format json|text]
|
||||
/financial-health forecast <forecast_data.json> [--format json|text]
|
||||
```
|
||||
|
||||
## Examples
|
||||
|
||||
```
|
||||
/financial-health ratios quarterly_financials.json --format json
|
||||
/financial-health dcf acme_valuation.json
|
||||
/financial-health budget q1_budget.json --format json
|
||||
/financial-health forecast revenue_history.json
|
||||
```
|
||||
|
||||
## Scripts
|
||||
- `finance/financial-analyst/scripts/ratio_calculator.py` — Profitability, liquidity, leverage, efficiency, valuation ratios
|
||||
- `finance/financial-analyst/scripts/dcf_valuation.py` — DCF enterprise and equity valuation with sensitivity analysis
|
||||
- `finance/financial-analyst/scripts/budget_variance_analyzer.py` — Actual vs budget vs prior year variance analysis
|
||||
- `finance/financial-analyst/scripts/forecast_builder.py` — Driver-based revenue forecasting with scenario modeling
|
||||
|
||||
## Skill Reference
|
||||
→ `finance/financial-analyst/SKILL.md`
|
||||
|
||||
## Related Commands
|
||||
- `/saas-health` — SaaS-specific metrics (ARR, MRR, churn, CAC, LTV, Quick Ratio)
|
||||
@@ -1,22 +1,24 @@
|
||||
---
|
||||
title: "Commands"
|
||||
description: "All 12 slash commands for quick access to common operations."
|
||||
description: "All 14 slash commands for quick access to common operations."
|
||||
---
|
||||
|
||||
# Slash Commands
|
||||
|
||||
12 commands for quick access to common operations.
|
||||
14 commands for quick access to common operations.
|
||||
|
||||
| Command | Description |
|
||||
|---------|-------------|
|
||||
| [`/changelog`](changelog.md) | Generate Keep a Changelog entries from git history and validate commit message format. |
|
||||
| [`/competitive-matrix`](competitive-matrix.md) | Build competitive matrices with weighted scoring, gap analysis, and market positioning insights. |
|
||||
| [`/financial-health`](financial-health.md) | Analyze financial statements, build valuation models, assess budget variances, and construct forecasts. |
|
||||
| [`/okr`](okr.md) | Generate cascaded OKR frameworks from company-level strategy down to team-level key results. |
|
||||
| [`/persona`](persona.md) | Generate structured user personas with demographics, goals, pain points, and behavioral patterns. |
|
||||
| [`/pipeline`](pipeline.md) | Detect project stack and generate CI/CD pipeline configurations for GitHub Actions or GitLab CI. |
|
||||
| [`/project-health`](project-health.md) | Generate portfolio health dashboards and risk matrices for project oversight. |
|
||||
| [`/retro`](retro.md) | Analyze retrospective data for recurring themes, sentiment trends, and action item effectiveness. |
|
||||
| [`/rice`](rice.md) | Prioritize features using RICE scoring (Reach, Impact, Confidence, Effort) with optional capacity constraints. |
|
||||
| [`/saas-health`](saas-health.md) | Calculate SaaS financial health metrics from raw business numbers, benchmark against industry standards, and project forward. |
|
||||
| [`/sprint-health`](sprint-health.md) | Score sprint health across delivery, quality, and team metrics with velocity trend analysis. |
|
||||
| [`/tdd`](tdd.md) | Generate tests, analyze coverage, and validate test quality using the TDD Guide skill. |
|
||||
| [`/tech-debt`](tech-debt.md) | Scan codebases for technical debt, score severity, and generate prioritized remediation plans. |
|
||||
|
||||
42
docs/commands/saas-health.md
Normal file
42
docs/commands/saas-health.md
Normal file
@@ -0,0 +1,42 @@
|
||||
---
|
||||
title: "/saas-health"
|
||||
description: "/saas-health — Claude Code slash command."
|
||||
---
|
||||
|
||||
# /saas-health
|
||||
|
||||
**Type:** Slash Command | **Source:** [`commands/saas-health.md`](https://github.com/alirezarezvani/claude-skills/tree/main/commands/saas-health.md)
|
||||
|
||||
---
|
||||
|
||||
|
||||
# /saas-health
|
||||
|
||||
Calculate SaaS financial health metrics from raw business numbers, benchmark against industry standards, and project forward.
|
||||
|
||||
## Usage
|
||||
|
||||
```
|
||||
/saas-health metrics --mrr <amount> [--customers <n>] [--churned <n>] [--json]
|
||||
/saas-health quick-ratio --new-mrr <amount> --churned <amount> [--expansion <amount>]
|
||||
/saas-health simulate --mrr <amount> --growth <pct> --churn <pct> --cac <amount> [--json]
|
||||
```
|
||||
|
||||
## Examples
|
||||
|
||||
```
|
||||
/saas-health metrics --mrr 80000 --customers 200 --churned 3 --new-customers 15 --sm-spend 25000
|
||||
/saas-health quick-ratio --new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500
|
||||
/saas-health simulate --mrr 50000 --growth 10 --churn 3 --cac 2000
|
||||
```
|
||||
|
||||
## Scripts
|
||||
- `finance/saas-metrics-coach/scripts/metrics_calculator.py` — Core SaaS metrics (ARR, MRR, churn, CAC, LTV, NRR, payback)
|
||||
- `finance/saas-metrics-coach/scripts/quick_ratio_calculator.py` — Growth efficiency ratio
|
||||
- `finance/saas-metrics-coach/scripts/unit_economics_simulator.py` — 12-month forward projection
|
||||
|
||||
## Skill Reference
|
||||
→ `finance/saas-metrics-coach/SKILL.md`
|
||||
|
||||
## Related Commands
|
||||
- `/financial-health` — Traditional financial analysis (ratios, DCF, budgets)
|
||||
@@ -78,7 +78,7 @@ Choose your platform and follow the steps:
|
||||
| **Project Management** | `/plugin install pm-skills@claude-code-skills` | 6 |
|
||||
| **C-Level Advisory** | `/plugin install c-level-skills@claude-code-skills` | 28 |
|
||||
| **Business & Growth** | `/plugin install business-growth-skills@claude-code-skills` | 4 |
|
||||
| **Finance** | `/plugin install finance-skills@claude-code-skills` | 1 |
|
||||
| **Finance** | `/plugin install finance-skills@claude-code-skills` | 2 |
|
||||
|
||||
Or install individual skills: `/plugin install skill-name@claude-code-skills`
|
||||
|
||||
@@ -112,7 +112,7 @@ AI-augmented development. Optimize for SEO.
|
||||
|
||||
## Python Tools
|
||||
|
||||
All 237 tools use the standard library only — zero pip installs, all verified.
|
||||
All 240 tools use the standard library only — zero pip installs, all verified.
|
||||
|
||||
```bash
|
||||
# Security audit a skill before installing
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
title: Claude Code Skills & Plugins
|
||||
description: "170 production-ready skills, 14 agents, and 12 commands for Claude Code, OpenAI Codex, Gemini CLI, and OpenClaw."
|
||||
description: "171 production-ready skills, 14 agents, and 14 commands for Claude Code, OpenAI Codex, Gemini CLI, and OpenClaw."
|
||||
hide:
|
||||
- toc
|
||||
- edit
|
||||
@@ -14,7 +14,7 @@ hide:
|
||||
|
||||
# Claude Code Skills
|
||||
|
||||
170 production-ready skills that transform AI coding agents into specialized professionals.
|
||||
171 production-ready skills that transform AI coding agents into specialized professionals.
|
||||
{ .hero-subtitle }
|
||||
|
||||
**Claude Code** | **OpenAI Codex** | **Gemini CLI** | **OpenClaw**
|
||||
@@ -27,13 +27,13 @@ hide:
|
||||
|
||||
<div class="grid cards" markdown>
|
||||
|
||||
- :material-counter:{ .lg .middle } **170**
|
||||
- :material-counter:{ .lg .middle } **171**
|
||||
|
||||
---
|
||||
|
||||
Production-ready skills
|
||||
|
||||
- :material-language-python:{ .lg .middle } **237**
|
||||
- :material-language-python:{ .lg .middle } **240**
|
||||
|
||||
---
|
||||
|
||||
@@ -45,7 +45,7 @@ hide:
|
||||
|
||||
[Multi-skill agents](agents/)
|
||||
|
||||
- :material-console:{ .lg .middle } **12**
|
||||
- :material-console:{ .lg .middle } **14**
|
||||
|
||||
---
|
||||
|
||||
@@ -127,9 +127,9 @@ hide:
|
||||
|
||||
---
|
||||
|
||||
Financial analyst — DCF valuation, budgeting, forecasting, ratio analysis
|
||||
Financial analyst, SaaS metrics coach — DCF valuation, budgeting, forecasting, ratio analysis, ARR/MRR/churn/LTV
|
||||
|
||||
[:octicons-arrow-right-24: 1 skill](skills/finance/)
|
||||
[:octicons-arrow-right-24: 2 skills](skills/finance/)
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
@@ -5,9 +5,10 @@ description: "All Finance skills for Claude Code, OpenAI Codex, and OpenClaw."
|
||||
|
||||
# Finance Skills
|
||||
|
||||
2 skills in this domain.
|
||||
3 skills in this domain.
|
||||
|
||||
| Skill | Description |
|
||||
|-------|-------------|
|
||||
| [Finance Skills](finance.md) | `finance` |
|
||||
| [Financial Analyst Skill](financial-analyst.md) | `financial-analyst` |
|
||||
| [SaaS Metrics Coach](saas-metrics-coach.md) | `saas-metrics-coach` |
|
||||
|
||||
159
docs/skills/finance/saas-metrics-coach.md
Normal file
159
docs/skills/finance/saas-metrics-coach.md
Normal file
@@ -0,0 +1,159 @@
|
||||
---
|
||||
title: "SaaS Metrics Coach"
|
||||
description: "SaaS Metrics Coach - Claude Code skill from the Finance domain."
|
||||
---
|
||||
|
||||
# SaaS Metrics Coach
|
||||
|
||||
**Domain:** Finance | **Skill:** `saas-metrics-coach` | **Source:** [`finance/saas-metrics-coach/SKILL.md`](https://github.com/alirezarezvani/claude-skills/tree/main/finance/saas-metrics-coach/SKILL.md)
|
||||
|
||||
---
|
||||
|
||||
|
||||
# SaaS Metrics Coach
|
||||
|
||||
Act as a senior SaaS CFO advisor. Take raw business numbers, calculate key health metrics, benchmark against industry standards, and give prioritized actionable advice in plain English.
|
||||
|
||||
## Step 1 — Collect Inputs
|
||||
|
||||
If not already provided, ask for these in a single grouped request:
|
||||
|
||||
- Revenue: current MRR, MRR last month, expansion MRR, churned MRR
|
||||
- Customers: total active, new this month, churned this month
|
||||
- Costs: sales and marketing spend, gross margin %
|
||||
|
||||
Work with partial data. Be explicit about what is missing and what assumptions are being made.
|
||||
|
||||
## Step 2 — Calculate Metrics
|
||||
|
||||
Run `scripts/metrics_calculator.py` with the user's inputs. If the script is unavailable, use the formulas in `references/formulas.md`.
|
||||
|
||||
Always attempt to compute: ARR, MRR growth %, monthly churn rate, CAC, LTV, LTV:CAC ratio, CAC payback period, NRR.
|
||||
|
||||
**Additional Analysis Tools:**
|
||||
- Use `scripts/quick_ratio_calculator.py` when expansion/churn MRR data is available
|
||||
- Use `scripts/unit_economics_simulator.py` for forward-looking projections
|
||||
|
||||
## Step 3 — Benchmark Each Metric
|
||||
|
||||
Load `references/benchmarks.md`. For each metric show:
|
||||
- The calculated value
|
||||
- The relevant benchmark range for the user's segment and stage
|
||||
- A plain status label: HEALTHY / WATCH / CRITICAL
|
||||
|
||||
Match the benchmark tier to the user's market segment (Enterprise / Mid-Market / SMB / PLG) and company stage (Early / Growth / Scale). Ask if unclear.
|
||||
|
||||
## Step 4 — Prioritize and Recommend
|
||||
|
||||
Identify the top 2-3 metrics at WATCH or CRITICAL status. For each one state:
|
||||
- What is happening (one sentence, plain English)
|
||||
- Why it matters to the business
|
||||
- Two or three specific actions to take this month
|
||||
|
||||
Order by impact — address the most damaging problem first.
|
||||
|
||||
## Step 5 — Output Format
|
||||
|
||||
Always use this exact structure:
|
||||
|
||||
```
|
||||
# SaaS Health Report — [Month Year]
|
||||
|
||||
## Metrics at a Glance
|
||||
| Metric | Your Value | Benchmark | Status |
|
||||
|--------|------------|-----------|--------|
|
||||
|
||||
## Overall Picture
|
||||
[2-3 sentences, plain English summary]
|
||||
|
||||
## Priority Issues
|
||||
|
||||
### 1. [Metric Name]
|
||||
What is happening: ...
|
||||
Why it matters: ...
|
||||
Fix it this month: ...
|
||||
|
||||
### 2. [Metric Name]
|
||||
...
|
||||
|
||||
## What is Working
|
||||
[1-2 genuine strengths, no padding]
|
||||
|
||||
## 90-Day Focus
|
||||
[Single metric to move + specific numeric target]
|
||||
```
|
||||
|
||||
## Examples
|
||||
|
||||
**Example 1 — Partial data**
|
||||
|
||||
Input: "MRR is $80k, we have 200 customers, about 3 cancel each month."
|
||||
|
||||
Expected output: Calculates ARPA ($400), monthly churn (1.5%), ARR ($960k), LTV estimate. Flags CAC and growth rate as missing. Asks one focused follow-up question for the most impactful missing input.
|
||||
|
||||
**Example 2 — Critical scenario**
|
||||
|
||||
Input: "MRR $22k (was $23.5k), 80 customers, lost 9, gained 6, spent $15k on ads, 65% gross margin."
|
||||
|
||||
Expected output: Flags negative MoM growth (-6.4%), critical churn (11.25%), and LTV:CAC of 0.64:1 as CRITICAL. Recommends churn reduction as the single highest-priority action before any further growth spend.
|
||||
|
||||
## Key Principles
|
||||
|
||||
- Be direct. If a metric is bad, say it is bad.
|
||||
- Explain every metric in one sentence before showing the number.
|
||||
- Cap priority issues at three. More than three paralyzes action.
|
||||
- Context changes benchmarks. Five percent churn is catastrophic for Enterprise SaaS but normal for SMB/PLG. Always confirm the user's target market before scoring.
|
||||
|
||||
## Reference Files
|
||||
|
||||
- `references/formulas.md` — All metric formulas with worked examples
|
||||
- `references/benchmarks.md` — Industry benchmark ranges by stage and segment
|
||||
- `assets/input-template.md` — Blank input form to share with users
|
||||
- `scripts/metrics_calculator.py` — Core metrics calculator (ARR, MRR, churn, CAC, LTV, NRR)
|
||||
- `scripts/quick_ratio_calculator.py` — Growth efficiency metric (Quick Ratio)
|
||||
- `scripts/unit_economics_simulator.py` — 12-month forward projection
|
||||
|
||||
## Tools
|
||||
|
||||
### 1. Metrics Calculator (`scripts/metrics_calculator.py`)
|
||||
Core SaaS metrics from raw business numbers.
|
||||
|
||||
```bash
|
||||
# Interactive mode
|
||||
python scripts/metrics_calculator.py
|
||||
|
||||
# CLI mode
|
||||
python scripts/metrics_calculator.py --mrr 50000 --customers 100 --churned 5 --json
|
||||
```
|
||||
|
||||
### 2. Quick Ratio Calculator (`scripts/quick_ratio_calculator.py`)
|
||||
Growth efficiency metric: (New MRR + Expansion) / (Churned + Contraction)
|
||||
|
||||
```bash
|
||||
python scripts/quick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500
|
||||
python scripts/quick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --json
|
||||
```
|
||||
|
||||
**Benchmarks:**
|
||||
- < 1.0 = CRITICAL (losing faster than gaining)
|
||||
- 1-2 = WATCH (marginal growth)
|
||||
- 2-4 = HEALTHY (good efficiency)
|
||||
- \> 4 = EXCELLENT (strong growth)
|
||||
|
||||
### 3. Unit Economics Simulator (`scripts/unit_economics_simulator.py`)
|
||||
Project metrics forward 12 months based on growth/churn assumptions.
|
||||
|
||||
```bash
|
||||
python scripts/unit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000
|
||||
python scripts/unit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000 --json
|
||||
```
|
||||
|
||||
**Use for:**
|
||||
- "What if we grow at X% per month?"
|
||||
- Runway projections
|
||||
- Scenario planning (best/base/worst case)
|
||||
|
||||
## Related Skills
|
||||
|
||||
- **financial-analyst**: Use for DCF valuation, budget variance analysis, and traditional financial modeling. NOT for SaaS-specific metrics like CAC, LTV, or churn.
|
||||
- **business-growth/customer-success**: Use for retention strategies and customer health scoring. Complements this skill when churn is flagged as CRITICAL.
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "finance-skills",
|
||||
"description": "1 finance skill: financial analyst with ratio analysis, DCF valuation, budgeting, and forecasting. 4 Python automation tools",
|
||||
"description": "2 finance skills: financial analyst (ratio analysis, DCF valuation, budgeting, forecasting) and SaaS metrics coach (ARR, MRR, churn, CAC, LTV, NRR, Quick Ratio, 12-month projections). 7 Python automation tools",
|
||||
"version": "2.1.2",
|
||||
"author": {
|
||||
"name": "Alireza Rezvani",
|
||||
|
||||
@@ -1,13 +1,16 @@
|
||||
# Finance Skills - Claude Code Guidance
|
||||
|
||||
This guide covers the finance skill and its Python automation tools.
|
||||
This guide covers the finance skills and their Python automation tools.
|
||||
|
||||
## Finance Skills Overview
|
||||
|
||||
**Available Skills:**
|
||||
1. **financial-analyst/** - Financial statement analysis, ratio analysis, DCF valuation, budgeting, forecasting (4 Python tools)
|
||||
2. **saas-metrics-coach/** - SaaS financial health: ARR, MRR, churn, CAC, LTV, NRR, Quick Ratio, 12-month projections (3 Python tools)
|
||||
|
||||
**Total Tools:** 4 Python automation tools, 3 knowledge bases, 5 templates
|
||||
**Total Tools:** 7 Python automation tools, 5 knowledge bases, 6 templates
|
||||
|
||||
**Commands:** 2 (`/financial-health`, `/saas-health`)
|
||||
|
||||
## Python Automation Tools
|
||||
|
||||
@@ -97,9 +100,7 @@ python financial-analyst/scripts/forecast_builder.py forecast_data.json --format
|
||||
|
||||
---
|
||||
|
||||
**Last Updated:** February 2026
|
||||
**Skills Deployed:** 1/1 finance skills production-ready
|
||||
**Total Tools:** 4 Python automation tools
|
||||
|
||||
## saas-metrics-coach
|
||||
SaaS financial health advisor. Calculates ARR, MRR, churn, CAC, LTV, NRR, Quick Ratio. Benchmarks against industry standards. Includes 12-month projection simulator.
|
||||
**Last Updated:** March 2026
|
||||
**Skills Deployed:** 2/2 finance skills production-ready
|
||||
**Total Tools:** 7 Python automation tools
|
||||
**Commands:** /financial-health, /saas-health
|
||||
|
||||
@@ -1,393 +0,0 @@
|
||||
---
|
||||
name: seek-and-analyze-video
|
||||
description: Video intelligence and content analysis using Memories.ai LVMM. Discover videos on TikTok, YouTube, Instagram by topic or creator. Analyze video content, summarize meetings, build searchable knowledge bases across multiple videos. Use for video research, competitor content analysis, meeting notes, lecture summaries, or building video knowledge libraries.
|
||||
license: MIT
|
||||
metadata:
|
||||
version: 1.0.0
|
||||
author: Kenny Zheng
|
||||
category: marketing-skill
|
||||
updated: 2026-03-09
|
||||
triggers:
|
||||
- analyze video
|
||||
- video content analysis
|
||||
- summarize video
|
||||
- meeting notes from video
|
||||
- search TikTok videos
|
||||
- search YouTube videos
|
||||
- video knowledge base
|
||||
- competitor video analysis
|
||||
- extract video insights
|
||||
- video research
|
||||
- video intelligence
|
||||
- cross-video search
|
||||
---
|
||||
|
||||
# Seek and Analyze Video
|
||||
|
||||
You are an expert in video intelligence and content analysis. Your goal is to help users discover, analyze, and build knowledge from video content across social platforms using Memories.ai's Large Visual Memory Model (LVMM).
|
||||
|
||||
## Before Starting
|
||||
|
||||
**Check for context first:**
|
||||
If `marketing-context.md` exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
|
||||
|
||||
**API Setup Required:**
|
||||
This skill requires a Memories.ai API key. Guide users to:
|
||||
1. Visit https://memories.ai to create an account
|
||||
2. Get API key from dashboard (free tier: 100 credits, Plus: $15/month for 5,000 credits)
|
||||
3. Set environment variable: `export MEMORIES_API_KEY=your_key_here`
|
||||
|
||||
Gather this context (ask if not provided):
|
||||
|
||||
### 1. Current State
|
||||
- What video content do they need to analyze?
|
||||
- What platforms are they researching? (YouTube, TikTok, Instagram, Vimeo)
|
||||
- Do they have existing video libraries or starting fresh?
|
||||
|
||||
### 2. Goals
|
||||
- What insights are they extracting? (summaries, action items, competitive analysis)
|
||||
- Do they need one-time analysis or persistent knowledge base?
|
||||
- Are they analyzing individual videos or building cross-video research?
|
||||
|
||||
### 3. Video-Specific Context
|
||||
- What topics, hashtags, or creators are they tracking?
|
||||
- What's their use case? (competitor research, content strategy, meeting notes, training materials)
|
||||
- Do they need organized namespaces for team collaboration?
|
||||
|
||||
## How This Skill Works
|
||||
|
||||
This skill supports 5 primary modes:
|
||||
|
||||
### Mode 1: Quick Video Analysis
|
||||
When you need one-time video analysis without persistent storage.
|
||||
- Use `caption_video` for instant summaries
|
||||
- Best for: ad-hoc analysis, quick insights, testing content
|
||||
|
||||
### Mode 2: Social Media Research
|
||||
When discovering and analyzing videos across platforms.
|
||||
- Search by topic, hashtag, or creator
|
||||
- Import and analyze in bulk
|
||||
- Best for: competitor analysis, trend research, content inspiration
|
||||
|
||||
### Mode 3: Knowledge Base Building
|
||||
When creating searchable libraries from video content.
|
||||
- Index videos with semantic search
|
||||
- Query across multiple videos simultaneously
|
||||
- Best for: training materials, research repositories, content archives
|
||||
|
||||
### Mode 4: Meeting & Lecture Notes
|
||||
When extracting structured notes from recordings.
|
||||
- Generate transcripts with visual descriptions
|
||||
- Extract action items and key points
|
||||
- Best for: meeting summaries, educational content, presentations
|
||||
|
||||
### Mode 5: Memory Management
|
||||
When organizing text insights and cross-video knowledge.
|
||||
- Store notes with tags for retrieval
|
||||
- Search across videos and text memories
|
||||
- Best for: research notes, insights collection, knowledge management
|
||||
|
||||
## Core Workflows
|
||||
|
||||
### Workflow 1: Analyze a Video URL
|
||||
|
||||
**When to use:** User provides a YouTube, TikTok, Instagram, or Vimeo URL
|
||||
|
||||
**Process:**
|
||||
1. Validate URL format and platform support
|
||||
2. Choose analysis mode:
|
||||
- **Quick analysis:** `caption_video(url)` - instant summary, no storage
|
||||
- **Persistent analysis:** `import_video(url)` - index for future queries
|
||||
3. Extract key information (summary, transcript, action items)
|
||||
4. Generate structured output (see Output Artifacts)
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
# Quick analysis (no storage)
|
||||
result = caption_video("https://youtube.com/watch?v=...")
|
||||
|
||||
# Persistent indexing (builds knowledge base)
|
||||
video_id = import_video("https://youtube.com/watch?v=...")
|
||||
summary = query_video(video_id, "Summarize the key points")
|
||||
```
|
||||
|
||||
### Workflow 2: Social Media Video Research
|
||||
|
||||
**When to use:** User wants to find and analyze videos by topic, hashtag, or creator
|
||||
|
||||
**Process:**
|
||||
1. Define search parameters:
|
||||
- Platform: tiktok, youtube, instagram
|
||||
- Query: topic, hashtag, or creator handle
|
||||
- Count: number of videos to analyze
|
||||
2. Execute search: `search_social(platform, query, count)`
|
||||
3. Import discovered videos for deep analysis
|
||||
4. Generate competitive insights or trend report
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
# Find competitor content
|
||||
videos = search_social("tiktok", "#SaaSmarketing", count=20)
|
||||
|
||||
# Analyze top performers
|
||||
for video in videos[:5]:
|
||||
import_video(video['url'])
|
||||
|
||||
# Cross-video analysis
|
||||
insights = chat_personal("What content themes are working?")
|
||||
```
|
||||
|
||||
### Workflow 3: Build Video Knowledge Base
|
||||
|
||||
**When to use:** User needs searchable library across multiple videos
|
||||
|
||||
**Process:**
|
||||
1. Import videos with tags for organization
|
||||
2. Store supplementary text memories (notes, insights)
|
||||
3. Enable cross-video semantic search
|
||||
4. Query entire library for insights
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
# Import video library with tags
|
||||
import_video(url1, tags=["product-demo", "Q1-2026"])
|
||||
import_video(url2, tags=["product-demo", "Q2-2026"])
|
||||
|
||||
# Store text insights
|
||||
create_memory("Key insight from demos...", tags=["product-demo"])
|
||||
|
||||
# Query across all tagged content
|
||||
insights = chat_personal("Compare Q1 vs Q2 product demos")
|
||||
```
|
||||
|
||||
### Workflow 4: Extract Meeting Notes
|
||||
|
||||
**When to use:** User needs structured notes from recorded meetings or lectures
|
||||
|
||||
**Process:**
|
||||
1. Import meeting recording
|
||||
2. Request structured extraction:
|
||||
- Action items with owners
|
||||
- Key decisions made
|
||||
- Discussion topics
|
||||
- Timestamps for important moments
|
||||
3. Format as meeting minutes
|
||||
4. Store for future reference
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
video_id = import_video("meeting_recording.mp4")
|
||||
notes = query_video(video_id, """
|
||||
Extract:
|
||||
1. Action items with owners
|
||||
2. Key decisions
|
||||
3. Discussion topics
|
||||
4. Important timestamps
|
||||
""")
|
||||
```
|
||||
|
||||
### Workflow 5: Competitor Content Analysis
|
||||
|
||||
**When to use:** Analyzing competitor video strategies across platforms
|
||||
|
||||
**Process:**
|
||||
1. Search for competitor content by creator handle
|
||||
2. Import their top-performing videos
|
||||
3. Analyze patterns:
|
||||
- Content themes and formats
|
||||
- Messaging strategies
|
||||
- Production quality
|
||||
- Engagement tactics
|
||||
4. Generate competitive intelligence report
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
# Find competitor videos
|
||||
competitor_videos = search_social("youtube", "@competitor_handle", count=30)
|
||||
|
||||
# Import for analysis
|
||||
for video in competitor_videos:
|
||||
import_video(video['url'], tags=["competitor-X"])
|
||||
|
||||
# Extract insights
|
||||
analysis = chat_personal("Analyze competitor-X content strategy and gaps")
|
||||
```
|
||||
|
||||
## Command Reference
|
||||
|
||||
### Video Operations
|
||||
|
||||
| Command | Purpose | Storage |
|
||||
|---------|---------|---------|
|
||||
| `caption_video(url)` | Quick video summary | No |
|
||||
| `import_video(url, tags=[])` | Index video for queries | Yes |
|
||||
| `query_video(video_id, question)` | Ask about specific video | - |
|
||||
| `list_videos(tags=[])` | List indexed videos | - |
|
||||
| `delete_video(video_id)` | Remove from library | - |
|
||||
|
||||
### Social Media Search
|
||||
|
||||
| Command | Purpose |
|
||||
|---------|---------|
|
||||
| `search_social(platform, query, count)` | Find videos by topic/creator |
|
||||
| `search_personal(query, filters={})` | Search your indexed videos |
|
||||
|
||||
Platforms: `tiktok`, `youtube`, `instagram`
|
||||
|
||||
### Memory Management
|
||||
|
||||
| Command | Purpose |
|
||||
|---------|---------|
|
||||
| `create_memory(text, tags=[])` | Store text insight |
|
||||
| `search_memories(query)` | Find stored memories |
|
||||
| `list_memories(tags=[])` | List all memories |
|
||||
| `delete_memory(memory_id)` | Remove memory |
|
||||
|
||||
### Cross-Content Queries
|
||||
|
||||
| Command | Purpose |
|
||||
|---------|---------|
|
||||
| `chat_personal(question)` | Query across ALL videos and memories |
|
||||
| `chat_video(video_id, question)` | Focus on specific video |
|
||||
|
||||
### Vision Tasks
|
||||
|
||||
| Command | Purpose |
|
||||
|---------|---------|
|
||||
| `caption_image(image_url)` | Describe image using AI vision |
|
||||
| `import_image(image_url, tags=[])` | Index image for queries |
|
||||
|
||||
## Proactive Triggers
|
||||
|
||||
Surface these issues WITHOUT being asked when you notice them in context:
|
||||
|
||||
- **User requests video analysis without API key** → Guide them to memories.ai setup
|
||||
- **Repeated similar queries across videos** → Suggest building knowledge base instead
|
||||
- **Analyzing competitor content** → Recommend systematic tracking with tags
|
||||
- **Meeting recording shared** → Offer structured note extraction
|
||||
- **Multiple one-off analyses** → Suggest import_video for persistent reference
|
||||
- **Large video libraries without tags** → Recommend tag organization strategy
|
||||
|
||||
## Output Artifacts
|
||||
|
||||
| When you ask for... | You get... |
|
||||
|---------------------|------------|
|
||||
| "Analyze this video" | Structured summary with key points, themes, action items, and timestamps |
|
||||
| "Competitor content research" | Competitive analysis report with content themes, gaps, and recommendations |
|
||||
| "Meeting notes from recording" | Meeting minutes with action items, decisions, discussion topics, and owners |
|
||||
| "Video knowledge base" | Searchable library with semantic search across videos and memories |
|
||||
| "Social media video research" | Platform research report with top videos, trends, and content insights |
|
||||
|
||||
## Communication
|
||||
|
||||
All output follows the structured communication standard:
|
||||
- **Bottom line first** — answer before explanation
|
||||
- **What + Why + How** — every finding has all three
|
||||
- **Actions have owners and deadlines** — no "we should consider"
|
||||
- **Confidence tagging** — 🟢 verified / 🟡 medium / 🔴 assumed
|
||||
|
||||
**Example output format:**
|
||||
|
||||
```
|
||||
BOTTOM LINE: Competitor X focuses on product demos (60%) and customer stories (30%)
|
||||
|
||||
WHAT:
|
||||
• 18/30 videos are product demos with detailed walkthroughs — 🟢 verified
|
||||
• 9/30 videos are customer success stories with ROI metrics — 🟢 verified
|
||||
• Average video length: 3:24 (demos), 2:15 (stories) — 🟢 verified
|
||||
• Consistent posting: 2-3 videos/week on Tuesday/Thursday — 🟢 verified
|
||||
|
||||
WHY THIS MATTERS:
|
||||
They're driving bottom-of-funnel conversions with proof over awareness content.
|
||||
Your current 80% thought leadership leaves conversion gap.
|
||||
|
||||
HOW TO ACT:
|
||||
1. Create 10 product demo videos → [Owner] → [2 weeks]
|
||||
2. Record 5 customer case studies → [Owner] → [3 weeks]
|
||||
3. Test demo video performance vs current content → [Owner] → [4 weeks]
|
||||
|
||||
YOUR DECISION:
|
||||
Option A: Match their demo focus — higher conversion, lower reach
|
||||
Option B: Hybrid approach (50% demos, 50% thought leadership) — balanced
|
||||
```
|
||||
|
||||
## Technical Details
|
||||
|
||||
**Repository:** https://github.com/kennyzheng-builds/seek-and-analyze-video
|
||||
|
||||
**Requirements:**
|
||||
- Python 3.8+
|
||||
- Memories.ai API key (free tier or $15/month Plus)
|
||||
- Environment variable: `MEMORIES_API_KEY`
|
||||
|
||||
**Installation:**
|
||||
```bash
|
||||
# Via Claude Code
|
||||
claude skill install kennyzheng-builds/seek-and-analyze-video
|
||||
|
||||
# Or manual
|
||||
git clone https://github.com/kennyzheng-builds/seek-and-analyze-video.git
|
||||
export MEMORIES_API_KEY=your_key_here
|
||||
```
|
||||
|
||||
**Pricing:**
|
||||
- Free tier: 100 credits (testing and light use)
|
||||
- Plus: $15/month for 5,000 credits (power users)
|
||||
|
||||
**Supported Platforms:**
|
||||
- YouTube (all public videos)
|
||||
- TikTok (public videos)
|
||||
- Instagram (public videos and reels)
|
||||
- Vimeo (public videos)
|
||||
|
||||
## Key Differentiators
|
||||
|
||||
**vs ChatGPT/Gemini Video Analysis:**
|
||||
- Persistent memory (query anytime, not just during upload)
|
||||
- Cross-video search (query 100s of videos simultaneously)
|
||||
- Social media discovery (find videos, don't just analyze provided URLs)
|
||||
- Knowledge base building (organize with tags, semantic search)
|
||||
|
||||
**vs Manual Video Research:**
|
||||
- 40x faster video analysis
|
||||
- Automatic transcript + visual description
|
||||
- Semantic search across libraries
|
||||
- Scalable to hundreds of videos
|
||||
|
||||
**vs Traditional Video Tools:**
|
||||
- AI-native queries (ask questions vs manual review)
|
||||
- Cross-platform support (TikTok, YouTube, Instagram unified)
|
||||
- Zero-dependency Python client (works across Claude Code, OpenClaw, HappyCapy)
|
||||
- Workflow automation (upload → analyze → store in one command)
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Tagging Strategy
|
||||
- Use consistent tag naming (kebab-case recommended)
|
||||
- Tag by: content-type, date-range, platform, topic, campaign
|
||||
- Example: `["competitor-analysis", "Q1-2026", "tiktok", "product-demo"]`
|
||||
|
||||
### Credit Management
|
||||
- Quick analysis (`caption_video`): ~2 credits per video
|
||||
- Import + indexing (`import_video`): ~5 credits per video
|
||||
- Queries (`chat_personal`, `query_video`): ~1 credit per query
|
||||
- Plan accordingly based on tier (free: 100, Plus: 5,000/month)
|
||||
|
||||
### Query Optimization
|
||||
- Be specific in questions (better results, same credits)
|
||||
- Use filtered searches when possible (faster, more relevant)
|
||||
- Batch similar queries (analyze pattern, then ask once)
|
||||
|
||||
### Organization
|
||||
- Create namespace strategy for teams (use tags for isolation)
|
||||
- Archive old content (delete unused videos to reduce noise)
|
||||
- Document video IDs for important content (VI... identifiers)
|
||||
|
||||
## Related Skills
|
||||
|
||||
- **social-media-analyzer**: For quantitative social media metrics. Use this skill for qualitative video content analysis.
|
||||
- **content-strategy**: For planning content themes. Use this skill to research what's working in your niche.
|
||||
- **competitor-alternatives**: For competitive positioning. Use this skill for competitor content intelligence.
|
||||
- **marketing-context**: Provides audience and brand context. Use before running video research.
|
||||
- **content-production**: For creating content. Use this skill to research successful formats first.
|
||||
- **campaign-analytics**: For campaign performance data. Combine with this skill for qualitative video insights.
|
||||
@@ -1,251 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Example workflow demonstrating seek-and-analyze-video skill capabilities.
|
||||
Shows competitive video analysis pipeline with Memories.ai LVMM.
|
||||
|
||||
Usage:
|
||||
python example-workflow.py --mode [quick|full]
|
||||
|
||||
Modes:
|
||||
quick: Run with demo data (no API calls)
|
||||
full: Execute full workflow (requires MEMORIES_API_KEY)
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from datetime import datetime
|
||||
from typing import Dict, List
|
||||
|
||||
|
||||
def validate_api_key() -> bool:
|
||||
"""Check if API key is configured."""
|
||||
api_key = os.getenv("MEMORIES_API_KEY")
|
||||
if not api_key:
|
||||
print("❌ MEMORIES_API_KEY not set")
|
||||
print("\nSetup instructions:")
|
||||
print("1. Visit https://memories.ai and create account")
|
||||
print("2. Get API key from dashboard")
|
||||
print("3. Run: export MEMORIES_API_KEY=your_key_here")
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def demo_mode():
|
||||
"""Run demonstration with mock data (no API calls)."""
|
||||
print("🎬 Running in DEMO mode (no API calls)")
|
||||
print("=" * 60)
|
||||
|
||||
# Mock competitor discovery
|
||||
print("\n📍 Stage 1: Discovering competitor content...")
|
||||
mock_videos = [
|
||||
{
|
||||
"url": "https://youtube.com/watch?v=demo1",
|
||||
"title": "Competitor A - Product Demo",
|
||||
"views": 125000,
|
||||
"likes": 8500,
|
||||
"creator": "@competitor_a",
|
||||
},
|
||||
{
|
||||
"url": "https://youtube.com/watch?v=demo2",
|
||||
"title": "Competitor A - Pricing Guide",
|
||||
"views": 98000,
|
||||
"likes": 6200,
|
||||
"creator": "@competitor_a",
|
||||
},
|
||||
{
|
||||
"url": "https://youtube.com/watch?v=demo3",
|
||||
"title": "Competitor A - Customer Success Story",
|
||||
"views": 156000,
|
||||
"likes": 12000,
|
||||
"creator": "@competitor_a",
|
||||
},
|
||||
]
|
||||
|
||||
print(f"Found {len(mock_videos)} videos")
|
||||
for video in mock_videos:
|
||||
print(f" - {video['title']} ({video['views']:,} views)")
|
||||
|
||||
# Mock import
|
||||
print("\n📥 Stage 2: Importing top performers...")
|
||||
for video in mock_videos:
|
||||
mock_video_id = f"VI_{video['title'][:10].replace(' ', '_')}"
|
||||
print(f" ✓ Imported: {video['title']} → {mock_video_id}")
|
||||
|
||||
# Mock content analysis
|
||||
print("\n🔬 Stage 3: Analyzing content patterns...")
|
||||
mock_analysis = {
|
||||
"content_themes": {
|
||||
"product_demos": "60%",
|
||||
"customer_stories": "30%",
|
||||
"thought_leadership": "10%",
|
||||
},
|
||||
"average_length": "3:24",
|
||||
"hook_patterns": [
|
||||
"Here's what nobody tells you about...",
|
||||
"3 mistakes I see founders make...",
|
||||
"Watch this before choosing...",
|
||||
],
|
||||
"posting_frequency": "2-3 videos per week (Tuesday/Thursday)",
|
||||
}
|
||||
|
||||
print(json.dumps(mock_analysis, indent=2))
|
||||
|
||||
# Mock messaging analysis
|
||||
print("\n💬 Stage 4: Extracting messaging...")
|
||||
mock_messaging = {
|
||||
"core_pillars": [
|
||||
"ROI in first 90 days",
|
||||
"Enterprise-grade security",
|
||||
"No-code setup",
|
||||
],
|
||||
"pain_points_addressed": [
|
||||
"Manual workflows wasting time",
|
||||
"Security compliance complexity",
|
||||
"Integration headaches",
|
||||
],
|
||||
"proof_elements": [
|
||||
"Customer logos (Fortune 500)",
|
||||
"ROI calculators with real data",
|
||||
"Case studies with metrics",
|
||||
],
|
||||
}
|
||||
|
||||
print(json.dumps(mock_messaging, indent=2))
|
||||
|
||||
# Mock gap identification
|
||||
print("\n🎯 Stage 5: Identifying opportunities...")
|
||||
mock_gaps = {
|
||||
"uncovered_topics": [
|
||||
"Migration from legacy systems (high search volume)",
|
||||
"Team training and onboarding",
|
||||
"Advanced API usage",
|
||||
],
|
||||
"missed_angles": [
|
||||
"Product demos focus on features, not workflows",
|
||||
"Customer stories lack technical depth",
|
||||
"No content for technical evaluators",
|
||||
],
|
||||
"format_opportunities": [
|
||||
"Short-form TikTok/Reels (competitors use YouTube only)",
|
||||
"Live Q&A sessions (no one doing this)",
|
||||
"Comparison videos (avoided by competitors)",
|
||||
],
|
||||
}
|
||||
|
||||
print(json.dumps(mock_gaps, indent=2))
|
||||
|
||||
# Mock recommendations
|
||||
print("\n📋 Stage 6: Generating recommendations...")
|
||||
mock_recommendations = {
|
||||
"quick_wins": [
|
||||
{
|
||||
"action": "Create 3 short-form product demos for TikTok/Reels",
|
||||
"rationale": "Competitors only on YouTube, capture short-form audience",
|
||||
"timeline": "2 weeks",
|
||||
},
|
||||
{
|
||||
"action": "Record migration guide video",
|
||||
"rationale": "High search demand, zero competition",
|
||||
"timeline": "1 week",
|
||||
},
|
||||
],
|
||||
"strategic_bets": [
|
||||
{
|
||||
"action": "Launch weekly live Q&A series",
|
||||
"rationale": "Build community, no competitors doing this",
|
||||
"timeline": "Q2 2026",
|
||||
},
|
||||
{
|
||||
"action": "Create technical deep-dive series for evaluators",
|
||||
"rationale": "Gap in competitor content, address technical audience",
|
||||
"timeline": "Q2 2026",
|
||||
},
|
||||
],
|
||||
"avoid": [
|
||||
"Generic thought leadership (saturated)",
|
||||
"Feature-focused demos without use cases (not resonating)",
|
||||
],
|
||||
"differentiation": [
|
||||
"Lead with workflow outcomes, not features",
|
||||
"Show migration path from specific competitors",
|
||||
"Target technical evaluators ignored by competitors",
|
||||
],
|
||||
}
|
||||
|
||||
print(json.dumps(mock_recommendations, indent=2))
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("✅ Demo complete!")
|
||||
print("\nTo run with real data:")
|
||||
print("1. Set MEMORIES_API_KEY environment variable")
|
||||
print("2. Run: python example-workflow.py --mode full")
|
||||
|
||||
|
||||
def full_mode():
|
||||
"""Execute full workflow with actual API calls."""
|
||||
if not validate_api_key():
|
||||
return
|
||||
|
||||
print("🚀 Running FULL workflow with Memories.ai API")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n⚠️ This will consume API credits:")
|
||||
print(" - Discovery: ~1 credit per 10 videos")
|
||||
print(" - Import: ~5 credits per video")
|
||||
print(" - Queries: ~1-5 credits per query")
|
||||
print("\nEstimated total: ~50-100 credits")
|
||||
|
||||
response = input("\nProceed? (yes/no): ").strip().lower()
|
||||
if response != "yes":
|
||||
print("Cancelled.")
|
||||
return
|
||||
|
||||
print("\n📍 Stage 1: Discovering competitor content...")
|
||||
print("(Implementation would call Memories.ai API here)")
|
||||
|
||||
# In real implementation, would import and use the Memories.ai client
|
||||
# from seek_and_analyze_video import search_social, import_video, chat_personal
|
||||
|
||||
print("\nFull implementation requires:")
|
||||
print("1. Clone: https://github.com/kennyzheng-builds/seek-and-analyze-video")
|
||||
print("2. Import client from skill repository")
|
||||
print("3. Execute workflow with actual API calls")
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point."""
|
||||
mode = "quick"
|
||||
|
||||
# Parse arguments
|
||||
if len(sys.argv) > 1:
|
||||
if sys.argv[1] == "--mode" and len(sys.argv) > 2:
|
||||
mode = sys.argv[2]
|
||||
elif sys.argv[1] in ["--help", "-h"]:
|
||||
print(__doc__)
|
||||
return
|
||||
|
||||
if mode not in ["quick", "full"]:
|
||||
print(f"❌ Invalid mode: {mode}")
|
||||
print("Valid modes: quick, full")
|
||||
print("\nRun with --help for usage information")
|
||||
return
|
||||
|
||||
print(f"""
|
||||
╔════════════════════════════════════════════════════════════╗
|
||||
║ Seek and Analyze Video - Example Workflow ║
|
||||
║ Competitive Video Analysis ║
|
||||
╚════════════════════════════════════════════════════════════╝
|
||||
|
||||
Mode: {mode.upper()}
|
||||
Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
|
||||
""")
|
||||
|
||||
if mode == "quick":
|
||||
demo_mode()
|
||||
else:
|
||||
full_mode()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,445 +0,0 @@
|
||||
# Memories.ai API Command Reference
|
||||
|
||||
Complete reference for all 21 API commands available through the Memories.ai LVMM.
|
||||
|
||||
---
|
||||
|
||||
## Video Operations
|
||||
|
||||
### caption_video(url: str) → dict
|
||||
Quick video analysis without persistent storage. Best for one-time summaries.
|
||||
|
||||
**Parameters:**
|
||||
- `url`: Video URL (YouTube, TikTok, Instagram, Vimeo)
|
||||
|
||||
**Returns:**
|
||||
```python
|
||||
{
|
||||
"summary": "Video summary text",
|
||||
"duration": "3:24",
|
||||
"platform": "youtube"
|
||||
}
|
||||
```
|
||||
|
||||
**Credits:** ~2 per video
|
||||
|
||||
**Use when:** Ad-hoc analysis, testing content, no need for future queries
|
||||
|
||||
---
|
||||
|
||||
### import_video(url: str, tags: list = []) → str
|
||||
Index video for persistent queries. Returns video ID (VI...) for future reference.
|
||||
|
||||
**Parameters:**
|
||||
- `url`: Video URL
|
||||
- `tags`: Optional list of organization tags (e.g., `["competitor", "Q1-2026"]`)
|
||||
|
||||
**Returns:** Video ID string (e.g., `"VI_abc123def456"`)
|
||||
|
||||
**Credits:** ~5 per video
|
||||
|
||||
**Use when:** Building knowledge base, need cross-video search, repeated queries
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
video_id = import_video(
|
||||
"https://youtube.com/watch?v=dQw4w9WgXcQ",
|
||||
tags=["product-demo", "competitor-A", "2026-03"]
|
||||
)
|
||||
# Returns: "VI_abc123def456"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### query_video(video_id: str, question: str) → str
|
||||
Ask questions about a specific indexed video.
|
||||
|
||||
**Parameters:**
|
||||
- `video_id`: Video ID from import_video
|
||||
- `question`: Natural language question
|
||||
|
||||
**Returns:** Answer text
|
||||
|
||||
**Credits:** ~1 per query
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
answer = query_video("VI_abc123def456", "What are the main action items?")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### list_videos(tags: list = []) → list
|
||||
List all indexed videos, optionally filtered by tags.
|
||||
|
||||
**Parameters:**
|
||||
- `tags`: Optional filter tags (returns videos matching ANY tag)
|
||||
|
||||
**Returns:**
|
||||
```python
|
||||
[
|
||||
{
|
||||
"video_id": "VI_abc123",
|
||||
"url": "https://youtube.com/...",
|
||||
"imported_at": "2026-03-09T10:30:00Z",
|
||||
"tags": ["product-demo", "competitor-A"]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Credits:** 0 (metadata only)
|
||||
|
||||
---
|
||||
|
||||
### delete_video(video_id: str) → bool
|
||||
Remove video from your library. Cannot be undone.
|
||||
|
||||
**Parameters:**
|
||||
- `video_id`: Video ID to delete
|
||||
|
||||
**Returns:** `True` if successful
|
||||
|
||||
**Credits:** 0
|
||||
|
||||
---
|
||||
|
||||
## Social Media Search
|
||||
|
||||
### search_social(platform: str, query: str, count: int = 10) → list
|
||||
Discover public videos by topic, hashtag, or creator.
|
||||
|
||||
**Parameters:**
|
||||
- `platform`: `"tiktok"`, `"youtube"`, or `"instagram"`
|
||||
- `query`: Topic, hashtag (with #), or creator handle (with @)
|
||||
- `count`: Number of results (default: 10, max: 50)
|
||||
|
||||
**Returns:**
|
||||
```python
|
||||
[
|
||||
{
|
||||
"url": "https://tiktok.com/@creator/video/123",
|
||||
"title": "Video title",
|
||||
"creator": "@creator",
|
||||
"views": 125000,
|
||||
"likes": 8500,
|
||||
"published": "2026-03-08"
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Credits:** ~1 per 10 videos
|
||||
|
||||
**Examples:**
|
||||
```python
|
||||
# Topic search
|
||||
videos = search_social("youtube", "SaaS pricing strategies", count=20)
|
||||
|
||||
# Hashtag search
|
||||
videos = search_social("tiktok", "#contentmarketing", count=30)
|
||||
|
||||
# Creator search
|
||||
videos = search_social("instagram", "@competitor_handle", count=15)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### search_personal(query: str, filters: dict = {}) → list
|
||||
Search your indexed videos with semantic search.
|
||||
|
||||
**Parameters:**
|
||||
- `query`: Natural language search query
|
||||
- `filters`: Optional filters (`{"tags": ["tag1"], "date_from": "2026-01-01"}`)
|
||||
|
||||
**Returns:**
|
||||
```python
|
||||
[
|
||||
{
|
||||
"video_id": "VI_abc123",
|
||||
"relevance_score": 0.92,
|
||||
"snippet": "...relevant content snippet...",
|
||||
"tags": ["product-demo"]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Credits:** ~1 per query
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
results = search_personal(
|
||||
"product pricing discussions",
|
||||
filters={"tags": ["competitor-A"], "date_from": "2026-03-01"}
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Memory Management
|
||||
|
||||
### create_memory(text: str, tags: list = []) → str
|
||||
Store text insights for future retrieval.
|
||||
|
||||
**Parameters:**
|
||||
- `text`: Note or insight text
|
||||
- `tags`: Optional organization tags
|
||||
|
||||
**Returns:** Memory ID (e.g., `"MEM_xyz789"`)
|
||||
|
||||
**Credits:** ~1 per memory
|
||||
|
||||
**Use when:** Storing research notes, insights, key quotes not directly in videos
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
memory_id = create_memory(
|
||||
"Competitor A focuses on enterprise pricing tier, starts at $99/seat",
|
||||
tags=["competitor-A", "pricing", "insight"]
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### search_memories(query: str) → list
|
||||
Search stored text memories with semantic search.
|
||||
|
||||
**Parameters:**
|
||||
- `query`: Natural language search query
|
||||
|
||||
**Returns:**
|
||||
```python
|
||||
[
|
||||
{
|
||||
"memory_id": "MEM_xyz789",
|
||||
"text": "Memory content...",
|
||||
"relevance_score": 0.88,
|
||||
"tags": ["pricing", "insight"],
|
||||
"created_at": "2026-03-09T10:30:00Z"
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Credits:** ~1 per query
|
||||
|
||||
---
|
||||
|
||||
### list_memories(tags: list = []) → list
|
||||
List all stored memories, optionally filtered by tags.
|
||||
|
||||
**Parameters:**
|
||||
- `tags`: Optional filter tags
|
||||
|
||||
**Returns:** List of memory objects (same structure as search_memories)
|
||||
|
||||
**Credits:** 0 (metadata only)
|
||||
|
||||
---
|
||||
|
||||
### delete_memory(memory_id: str) → bool
|
||||
Delete stored memory. Cannot be undone.
|
||||
|
||||
**Parameters:**
|
||||
- `memory_id`: Memory ID to delete
|
||||
|
||||
**Returns:** `True` if successful
|
||||
|
||||
**Credits:** 0
|
||||
|
||||
---
|
||||
|
||||
## Cross-Content Queries
|
||||
|
||||
### chat_personal(question: str) → str
|
||||
Query across ALL indexed videos and memories simultaneously.
|
||||
|
||||
**Parameters:**
|
||||
- `question`: Natural language question
|
||||
|
||||
**Returns:** Answer synthesized from entire knowledge base
|
||||
|
||||
**Credits:** ~2-5 depending on complexity
|
||||
|
||||
**Use when:** Asking questions that require cross-video analysis
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
insight = chat_personal("""
|
||||
Compare competitor A and B's pricing strategies.
|
||||
What are the key differences and which approach is more effective?
|
||||
""")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### chat_video(video_id: str, question: str) → str
|
||||
Interactive chat focused on specific video (alternative to query_video).
|
||||
|
||||
**Parameters:**
|
||||
- `video_id`: Video ID
|
||||
- `question`: Natural language question
|
||||
|
||||
**Returns:** Answer text
|
||||
|
||||
**Credits:** ~1 per query
|
||||
|
||||
**Note:** Functionally similar to `query_video`, use interchangeably.
|
||||
|
||||
---
|
||||
|
||||
## Vision Tasks
|
||||
|
||||
### caption_image(image_url: str) → str
|
||||
Describe image content using AI vision.
|
||||
|
||||
**Parameters:**
|
||||
- `image_url`: Public image URL (JPEG, PNG, WebP)
|
||||
|
||||
**Returns:** Image description text
|
||||
|
||||
**Credits:** ~1 per image
|
||||
|
||||
**Use when:** Analyzing thumbnails, screenshots, visual content
|
||||
|
||||
**Example:**
|
||||
```python
|
||||
description = caption_image("https://example.com/thumbnail.jpg")
|
||||
# Returns: "A person presenting a pricing slide with three tiers..."
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### import_image(image_url: str, tags: list = []) → str
|
||||
Index image for persistent queries (similar to import_video for images).
|
||||
|
||||
**Parameters:**
|
||||
- `image_url`: Public image URL
|
||||
- `tags`: Optional organization tags
|
||||
|
||||
**Returns:** Image ID (e.g., `"IMG_def456"`)
|
||||
|
||||
**Credits:** ~2 per image
|
||||
|
||||
**Use when:** Building visual libraries, need repeated queries on images
|
||||
|
||||
---
|
||||
|
||||
## Advanced Usage Patterns
|
||||
|
||||
### Pattern 1: Bulk Import with Error Handling
|
||||
|
||||
```python
|
||||
def import_video_batch(urls, tag_prefix):
|
||||
"""Import multiple videos with error handling"""
|
||||
results = []
|
||||
for idx, url in enumerate(urls):
|
||||
try:
|
||||
video_id = import_video(url, tags=[tag_prefix, f"batch-{idx}"])
|
||||
results.append({"url": url, "video_id": video_id, "status": "success"})
|
||||
except Exception as e:
|
||||
results.append({"url": url, "error": str(e), "status": "failed"})
|
||||
return results
|
||||
```
|
||||
|
||||
### Pattern 2: Smart Tag Organization
|
||||
|
||||
```python
|
||||
# Hierarchical tagging strategy
|
||||
tags = [
|
||||
f"{platform}", # youtube, tiktok, instagram
|
||||
f"{content_type}", # product-demo, tutorial, case-study
|
||||
f"{date_range}", # Q1-2026, 2026-03
|
||||
f"{campaign}", # launch-campaign-X
|
||||
f"{source_type}" # competitor, internal, partner
|
||||
]
|
||||
|
||||
video_id = import_video(url, tags=tags)
|
||||
```
|
||||
|
||||
### Pattern 3: Progressive Research
|
||||
|
||||
```python
|
||||
# Stage 1: Discover
|
||||
videos = search_social("youtube", "@competitor", count=50)
|
||||
|
||||
# Stage 2: Import top performers (by views/likes)
|
||||
top_videos = sorted(videos, key=lambda x: x['views'], reverse=True)[:10]
|
||||
for video in top_videos:
|
||||
import_video(video['url'], tags=["competitor", "top-performer"])
|
||||
|
||||
# Stage 3: Cross-video analysis
|
||||
insights = chat_personal("What makes their top 10 videos successful?")
|
||||
```
|
||||
|
||||
### Pattern 4: Meeting Intelligence
|
||||
|
||||
```python
|
||||
# Import meeting recording
|
||||
meeting_id = import_video(recording_url, tags=["team-meeting", "2026-03-09"])
|
||||
|
||||
# Extract structured data
|
||||
action_items = query_video(meeting_id, "List all action items with owners")
|
||||
decisions = query_video(meeting_id, "What decisions were made?")
|
||||
topics = query_video(meeting_id, "What were the main discussion topics?")
|
||||
|
||||
# Store supplementary notes
|
||||
create_memory(f"Meeting {date}: Key outcomes and next steps",
|
||||
tags=["team-meeting", "summary"])
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Credit Usage Guidelines
|
||||
|
||||
| Operation | Credits | Recommendation |
|
||||
|-----------|---------|----------------|
|
||||
| Quick caption | 2 | Use for testing/one-off |
|
||||
| Import video | 5 | Build library strategically |
|
||||
| Query (simple) | 1 | Ask specific questions |
|
||||
| Cross-video query | 2-5 | Batch similar questions |
|
||||
| Image caption | 1 | Use sparingly |
|
||||
| Social search | 0.1/video | Discover before importing |
|
||||
| Memory operations | 1 | Store key insights only |
|
||||
|
||||
**Free Tier Strategy (100 credits):**
|
||||
- Import ~15 key videos (75 credits)
|
||||
- Query ~25 times (25 credits)
|
||||
|
||||
**Plus Tier Strategy (5,000 credits/month):**
|
||||
- Import ~800 videos (4,000 credits)
|
||||
- Query ~1,000 times (1,000 credits)
|
||||
|
||||
---
|
||||
|
||||
## Error Handling
|
||||
|
||||
Common errors and solutions:
|
||||
|
||||
**InvalidAPIKey**
|
||||
- Check `MEMORIES_API_KEY` environment variable is set
|
||||
- Verify key is active on memories.ai dashboard
|
||||
|
||||
**UnsupportedPlatform**
|
||||
- Only YouTube, TikTok, Instagram, Vimeo supported
|
||||
- Ensure URL is public (not private/unlisted)
|
||||
|
||||
**CreditLimitExceeded**
|
||||
- Check usage on memories.ai dashboard
|
||||
- Upgrade to Plus tier or wait for monthly reset
|
||||
|
||||
**VideoNotFound**
|
||||
- Video may be deleted, private, or region-restricted
|
||||
- Verify URL is accessible in browser
|
||||
|
||||
**RateLimitExceeded**
|
||||
- Slow down request rate (max ~10 requests/second)
|
||||
- Consider batching operations
|
||||
|
||||
---
|
||||
|
||||
## API Changelog
|
||||
|
||||
**v1.0.0 (Current)**
|
||||
- 21 commands across 6 categories
|
||||
- Support for YouTube, TikTok, Instagram, Vimeo
|
||||
- Semantic search across videos and memories
|
||||
- Tag-based organization system
|
||||
- Cross-video chat functionality
|
||||
@@ -1,638 +0,0 @@
|
||||
# Use Cases and Examples
|
||||
|
||||
Real-world applications of video intelligence with Memories.ai LVMM.
|
||||
|
||||
---
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Competitor Content Intelligence](#competitor-content-intelligence)
|
||||
- [Content Strategy Research](#content-strategy-research)
|
||||
- [Meeting and Training Intelligence](#meeting-and-training-intelligence)
|
||||
- [Social Media Monitoring](#social-media-monitoring)
|
||||
- [Knowledge Base Management](#knowledge-base-management)
|
||||
- [Creator and Influencer Research](#creator-and-influencer-research)
|
||||
|
||||
---
|
||||
|
||||
## Competitor Content Intelligence
|
||||
|
||||
### Use Case: Analyze Competitor Video Strategy
|
||||
|
||||
**Scenario:** You want to understand how Competitor X uses video content to drive conversions.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Stage 1: Discover their content
|
||||
videos = search_social("youtube", "@competitor_x", count=50)
|
||||
|
||||
# Stage 2: Import their library
|
||||
for video in videos:
|
||||
import_video(video['url'], tags=["competitor-x", "analysis-2026-q1"])
|
||||
|
||||
# Stage 3: Content pattern analysis
|
||||
themes = chat_personal("""
|
||||
Tags: competitor-x
|
||||
Question: What are the main content themes and formats?
|
||||
Break down by frequency and video type.
|
||||
""")
|
||||
|
||||
# Stage 4: Messaging analysis
|
||||
messaging = chat_personal("""
|
||||
Tags: competitor-x
|
||||
Question: What value propositions do they emphasize?
|
||||
What pain points do they address?
|
||||
""")
|
||||
|
||||
# Stage 5: Production insights
|
||||
production = chat_personal("""
|
||||
Tags: competitor-x
|
||||
Question: What's their production quality level?
|
||||
Average video length? Consistent branding elements?
|
||||
""")
|
||||
|
||||
# Stage 6: Identify gaps
|
||||
gaps = chat_personal("""
|
||||
Compare competitor-x videos to our content library (tag: our-content).
|
||||
What topics do they cover that we don't?
|
||||
What angles are they using successfully?
|
||||
""")
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- Content theme breakdown (60% product demos, 30% customer stories, 10% thought leadership)
|
||||
- Key messaging pillars (ROI, ease of use, enterprise security)
|
||||
- Production specs (3:24 avg length, professional editing, consistent intro/outro)
|
||||
- Content gaps in your strategy
|
||||
|
||||
**ROI:** 20 hours of manual analysis → 2 hours automated
|
||||
|
||||
---
|
||||
|
||||
### Use Case: Competitive Pricing Intelligence
|
||||
|
||||
**Scenario:** Extract pricing information from competitor product videos.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Import competitor product demo videos
|
||||
competitor_demos = search_social("youtube", "competitor pricing demo", count=20)
|
||||
for video in competitor_demos[:10]:
|
||||
import_video(video['url'], tags=["competitor-pricing"])
|
||||
|
||||
# Extract pricing mentions
|
||||
pricing_data = chat_personal("""
|
||||
Tags: competitor-pricing
|
||||
Question: Extract all pricing information mentioned.
|
||||
Include: tiers, price points, billing cycles, discounts, enterprise pricing.
|
||||
""")
|
||||
|
||||
# Analyze pricing strategy
|
||||
strategy = chat_personal("""
|
||||
Tags: competitor-pricing
|
||||
Question: What pricing strategy are they using?
|
||||
Value-based, cost-plus, competition-based, penetration?
|
||||
How do they position their tiers?
|
||||
""")
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- Pricing tier structure (Starter $49, Pro $99, Enterprise custom)
|
||||
- Positioning strategy (value-based with ROI calculators)
|
||||
- Competitive differentiation (monthly vs annual pricing emphasis)
|
||||
|
||||
---
|
||||
|
||||
## Content Strategy Research
|
||||
|
||||
### Use Case: Identify High-Performing Content Formats
|
||||
|
||||
**Scenario:** Research what video formats are working in your niche.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Search for top content in your niche
|
||||
niche_videos = search_social("tiktok", "#SaaSmarketing", count=100)
|
||||
|
||||
# Import top performers (by engagement)
|
||||
top_50 = sorted(niche_videos, key=lambda x: x['likes'] + x['views'], reverse=True)[:50]
|
||||
for video in top_50:
|
||||
import_video(video['url'], tags=["niche-research", "top-performer"])
|
||||
|
||||
# Analyze successful patterns
|
||||
format_analysis = chat_personal("""
|
||||
Tags: top-performer
|
||||
Question: What video formats are most successful?
|
||||
Break down by: length, hook style, content structure, CTA approach.
|
||||
""")
|
||||
|
||||
# Identify successful hooks
|
||||
hooks = chat_personal("""
|
||||
Tags: top-performer
|
||||
Question: Extract the first 3 seconds (hook) from each video.
|
||||
What patterns make them effective?
|
||||
""")
|
||||
|
||||
# Production requirements
|
||||
production = chat_personal("""
|
||||
Tags: top-performer
|
||||
Question: What's the production quality distribution?
|
||||
Can successful content be made with smartphone + basic editing?
|
||||
""")
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- Winning formats (60-second problem-solution, 15-second quick tips)
|
||||
- Hook patterns ("Here's what nobody tells you about...", "3 mistakes I made...")
|
||||
- Production level (70% smartphone-quality acceptable, 30% professional)
|
||||
|
||||
**ROI:** Validate content strategy before investing in production
|
||||
|
||||
---
|
||||
|
||||
### Use Case: Topic Gap Analysis
|
||||
|
||||
**Scenario:** Find content opportunities your competitors aren't covering.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Import your content and competitor content
|
||||
# (Assume already done with tags: "our-content", "competitor-a", "competitor-b")
|
||||
|
||||
# Identify covered topics
|
||||
competitor_topics = chat_personal("""
|
||||
Tags: competitor-a, competitor-b
|
||||
Question: List all topics covered. Group by category.
|
||||
""")
|
||||
|
||||
# Find gaps
|
||||
gaps = chat_personal("""
|
||||
Compare topics from competitors (tags: competitor-a, competitor-b)
|
||||
vs audience questions (tag: customer-questions)
|
||||
What topics are customers asking about that competitors haven't covered?
|
||||
""")
|
||||
|
||||
# Opportunity sizing
|
||||
opportunities = chat_personal("""
|
||||
For each gap identified, search social platforms:
|
||||
How many searches/hashtags exist for that topic?
|
||||
Is there existing demand?
|
||||
""")
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- 15 topic gaps with high demand, low competition
|
||||
- Prioritized by search volume and strategic fit
|
||||
- Content angle recommendations
|
||||
|
||||
---
|
||||
|
||||
## Meeting and Training Intelligence
|
||||
|
||||
### Use Case: Extract Action Items from Meetings
|
||||
|
||||
**Scenario:** Convert recorded meetings into structured action items.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Import meeting recording
|
||||
meeting_id = import_video(
|
||||
"internal_recording.mp4",
|
||||
tags=["team-meeting", "product-planning", "2026-03-09"]
|
||||
)
|
||||
|
||||
# Extract action items
|
||||
action_items = query_video(meeting_id, """
|
||||
Extract all action items mentioned in the meeting.
|
||||
Format as:
|
||||
- [ ] Action item description | Owner: Name | Due: Date | Context: Why needed
|
||||
""")
|
||||
|
||||
# Extract decisions
|
||||
decisions = query_video(meeting_id, """
|
||||
List all decisions made during the meeting.
|
||||
Format as:
|
||||
DECISION: [Description]
|
||||
RATIONALE: [Why]
|
||||
OWNER: [Who's accountable]
|
||||
IMPACT: [What changes]
|
||||
""")
|
||||
|
||||
# Generate meeting summary
|
||||
summary = query_video(meeting_id, """
|
||||
Create executive summary:
|
||||
1. Key topics discussed
|
||||
2. Decisions made
|
||||
3. Action items (grouped by owner)
|
||||
4. Blockers identified
|
||||
5. Next meeting agenda items
|
||||
""")
|
||||
|
||||
# Store for future reference
|
||||
create_memory(
|
||||
f"Meeting Summary {date}: {summary}",
|
||||
tags=["meeting-summary", "product-planning"]
|
||||
)
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
```
|
||||
ACTION ITEMS:
|
||||
- [ ] Update pricing page with new tier | Owner: Sarah | Due: 2026-03-15 | Context: Launch prep
|
||||
- [ ] Schedule user interviews | Owner: Mike | Due: 2026-03-12 | Context: Validate feature priority
|
||||
|
||||
DECISIONS:
|
||||
- Push mobile app launch to Q2 (Rationale: Backend infrastructure not ready)
|
||||
- Focus Q1 on enterprise features (Rationale: 3 pilot customers waiting)
|
||||
```
|
||||
|
||||
**ROI:** 30 minutes of manual note-taking → 2 minutes automated
|
||||
|
||||
---
|
||||
|
||||
### Use Case: Training Material Knowledge Base
|
||||
|
||||
**Scenario:** Build searchable library from training videos and courses.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Import all training videos
|
||||
training_videos = [
|
||||
"onboarding_day1.mp4",
|
||||
"onboarding_day2.mp4",
|
||||
"product_training_basics.mp4",
|
||||
"product_training_advanced.mp4",
|
||||
"sales_process_training.mp4"
|
||||
]
|
||||
|
||||
for video_url in training_videos:
|
||||
import_video(video_url, tags=["training", "onboarding"])
|
||||
|
||||
# Create searchable knowledge base
|
||||
# New employees can now ask questions:
|
||||
answer = chat_personal("How do I handle objections about pricing?")
|
||||
answer = chat_personal("What's our product positioning vs competitors?")
|
||||
answer = chat_personal("Walk me through the sales process step by step")
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- Instant answers to onboarding questions
|
||||
- Reference to specific training video timestamps
|
||||
- Consistent knowledge across team
|
||||
|
||||
**ROI:** Reduce onboarding time 40%, improve knowledge retention
|
||||
|
||||
---
|
||||
|
||||
## Social Media Monitoring
|
||||
|
||||
### Use Case: Track Brand Mentions Across Platforms
|
||||
|
||||
**Scenario:** Monitor videos mentioning your brand or product.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Search across platforms
|
||||
tiktok_mentions = search_social("tiktok", "#YourBrand", count=50)
|
||||
youtube_mentions = search_social("youtube", "YourBrand review", count=50)
|
||||
instagram_mentions = search_social("instagram", "@yourbrand", count=50)
|
||||
|
||||
# Import for analysis
|
||||
all_mentions = tiktok_mentions + youtube_mentions + instagram_mentions
|
||||
for video in all_mentions:
|
||||
import_video(video['url'], tags=["brand-mention", video['platform']])
|
||||
|
||||
# Sentiment analysis
|
||||
sentiment = chat_personal("""
|
||||
Tags: brand-mention
|
||||
Question: Analyze sentiment across all brand mentions.
|
||||
Positive, neutral, negative breakdown.
|
||||
Common praise points and complaints.
|
||||
""")
|
||||
|
||||
# Feature requests
|
||||
requests = chat_personal("""
|
||||
Tags: brand-mention
|
||||
Question: Extract all feature requests or improvement suggestions.
|
||||
Rank by frequency mentioned.
|
||||
""")
|
||||
|
||||
# Competitive comparisons
|
||||
comparisons = chat_personal("""
|
||||
Tags: brand-mention
|
||||
Question: When creators compare us to competitors, what do they say?
|
||||
What are our perceived strengths and weaknesses?
|
||||
""")
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- Sentiment: 70% positive, 20% neutral, 10% negative
|
||||
- Top feature requests: Mobile app (15 mentions), API access (12 mentions)
|
||||
- Competitive position: "Easier to use than X, but lacks Y feature"
|
||||
|
||||
**ROI:** Real-time feedback loop, inform product roadmap
|
||||
|
||||
---
|
||||
|
||||
### Use Case: Influencer Partnership Research
|
||||
|
||||
**Scenario:** Identify and vet potential influencer partners.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Find creators in your niche
|
||||
creators = search_social("youtube", "SaaS founder", count=100)
|
||||
|
||||
# Filter to top performers
|
||||
top_creators = sorted(creators, key=lambda x: x['views'], reverse=True)[:20]
|
||||
|
||||
# Import their content
|
||||
for creator in top_creators:
|
||||
videos = search_social("youtube", f"@{creator['handle']}", count=10)
|
||||
for video in videos:
|
||||
import_video(video['url'], tags=["influencer-research", creator['handle']])
|
||||
|
||||
# Analyze each creator
|
||||
for creator in top_creators:
|
||||
profile = chat_personal(f"""
|
||||
Tags: {creator['handle']}
|
||||
Question: Analyze this creator's content:
|
||||
- Main topics covered
|
||||
- Audience demographic (based on comments/content)
|
||||
- Brand alignment with our values
|
||||
- Engagement quality (comments depth)
|
||||
- Partnership potential (do they do sponsorships?)
|
||||
""")
|
||||
|
||||
create_memory(profile, tags=["influencer-profile", creator['handle']])
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- Vetted list of 5 high-fit influencers
|
||||
- Audience alignment scores
|
||||
- Estimated reach and engagement
|
||||
- Partnership readiness assessment
|
||||
|
||||
---
|
||||
|
||||
## Knowledge Base Management
|
||||
|
||||
### Use Case: Customer Research Repository
|
||||
|
||||
**Scenario:** Build searchable library of customer interviews and feedback videos.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Import customer interview recordings
|
||||
interviews = [
|
||||
"customer_interview_acme_corp.mp4",
|
||||
"customer_interview_tech_startup.mp4",
|
||||
"user_testing_session_1.mp4"
|
||||
]
|
||||
|
||||
for video_url in interviews:
|
||||
import_video(video_url, tags=["customer-research", "interview"])
|
||||
|
||||
# Import product feedback videos
|
||||
feedback_videos = search_social("youtube", "ProductName feedback", count=30)
|
||||
for video in feedback_videos:
|
||||
import_video(video['url'], tags=["customer-research", "feedback"])
|
||||
|
||||
# Cross-interview insights
|
||||
pain_points = chat_personal("""
|
||||
Tags: customer-research
|
||||
Question: What are the top pain points mentioned across all interviews?
|
||||
Rank by frequency and severity.
|
||||
""")
|
||||
|
||||
feature_value = chat_personal("""
|
||||
Tags: customer-research
|
||||
Question: Which features do customers mention as most valuable?
|
||||
What outcomes do they achieve?
|
||||
""")
|
||||
|
||||
use_cases = chat_personal("""
|
||||
Tags: customer-research
|
||||
Question: What are the main use cases customers describe?
|
||||
Group by industry or company size.
|
||||
""")
|
||||
|
||||
# Store insights
|
||||
create_memory(f"Customer Research Synthesis {date}: {pain_points}",
|
||||
tags=["research-insight", "product-roadmap"])
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- Top 10 pain points ranked
|
||||
- Feature value hierarchy
|
||||
- Use case taxonomy
|
||||
- Product roadmap implications
|
||||
|
||||
**ROI:** Centralize customer knowledge, inform product decisions
|
||||
|
||||
---
|
||||
|
||||
### Use Case: Competitive Intelligence Database
|
||||
|
||||
**Scenario:** Maintain up-to-date competitive intelligence from video sources.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Weekly competitor monitoring (automate with cron)
|
||||
competitors = ["@competitor_a", "@competitor_b", "@competitor_c"]
|
||||
|
||||
for competitor in competitors:
|
||||
# Search for new videos
|
||||
new_videos = search_social("youtube", competitor, count=10)
|
||||
|
||||
# Import only videos from last 7 days
|
||||
recent = [v for v in new_videos if is_within_last_week(v['published'])]
|
||||
|
||||
for video in recent:
|
||||
import_video(video['url'], tags=["competitive-intel", competitor, "2026-q1"])
|
||||
|
||||
# Weekly intelligence report
|
||||
report = chat_personal("""
|
||||
Tags: competitive-intel, 2026-q1
|
||||
Filter: last 7 days
|
||||
Question: Generate competitive intelligence summary:
|
||||
1. New product announcements or features
|
||||
2. Pricing changes
|
||||
3. Marketing message shifts
|
||||
4. Partnership announcements
|
||||
5. Strategic moves (funding, acquisitions, etc.)
|
||||
""")
|
||||
|
||||
# Send to stakeholders
|
||||
create_memory(f"Weekly Competitive Intel {date}: {report}",
|
||||
tags=["intelligence-report", "weekly"])
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- Automated weekly competitive briefing
|
||||
- Early detection of competitive moves
|
||||
- Strategic planning inputs
|
||||
|
||||
---
|
||||
|
||||
## Creator and Influencer Research
|
||||
|
||||
### Use Case: Content Creator Trend Analysis
|
||||
|
||||
**Scenario:** Identify emerging content trends in your industry.
|
||||
|
||||
**Workflow:**
|
||||
```python
|
||||
# Search across platforms for industry hashtags
|
||||
hashtags = ["#SaaSmarketing", "#ProductManagement", "#StartupTips"]
|
||||
all_videos = []
|
||||
|
||||
for tag in hashtags:
|
||||
tiktok = search_social("tiktok", tag, count=100)
|
||||
youtube = search_social("youtube", tag.replace("#", ""), count=100)
|
||||
all_videos.extend(tiktok + youtube)
|
||||
|
||||
# Import recent content (last 30 days)
|
||||
recent_videos = [v for v in all_videos if is_recent(v['published'], days=30)]
|
||||
for video in recent_videos:
|
||||
import_video(video['url'], tags=["trend-research", "2026-q1"])
|
||||
|
||||
# Trend analysis
|
||||
trends = chat_personal("""
|
||||
Tags: trend-research, 2026-q1
|
||||
Question: What are the emerging content trends?
|
||||
Look for:
|
||||
- Topics gaining traction (mentioned in 5+ videos)
|
||||
- Format innovations (new video structures)
|
||||
- Messaging shifts (new angles on old topics)
|
||||
- Platform-specific trends (what works on TikTok vs YouTube)
|
||||
""")
|
||||
|
||||
# Validate trend strength
|
||||
validation = chat_personal("""
|
||||
Tags: trend-research
|
||||
Question: For each identified trend, assess:
|
||||
- Growth trajectory (increasing or peak?)
|
||||
- Audience engagement (comments, shares)
|
||||
- Creator adoption (how many creators using this trend?)
|
||||
- Longevity prediction (fad or sustainable?)
|
||||
""")
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- 5-10 emerging trends with growth metrics
|
||||
- Format innovations to test
|
||||
- Timing recommendations (early mover vs wait and see)
|
||||
|
||||
---
|
||||
|
||||
## Advanced Workflows
|
||||
|
||||
### Multi-Stage Research Pipeline
|
||||
|
||||
**Complete competitive research workflow:**
|
||||
|
||||
```python
|
||||
# Stage 1: Discovery
|
||||
print("🔍 Stage 1: Discovering competitor content...")
|
||||
competitors = ["@competitor_a", "@competitor_b"]
|
||||
all_videos = []
|
||||
|
||||
for comp in competitors:
|
||||
videos = search_social("youtube", comp, count=50)
|
||||
all_videos.extend([(v, comp) for v in videos])
|
||||
|
||||
print(f"Found {len(all_videos)} videos")
|
||||
|
||||
# Stage 2: Import top performers
|
||||
print("📥 Stage 2: Importing top performers...")
|
||||
top_videos = sorted(all_videos, key=lambda x: x[0]['views'], reverse=True)[:30]
|
||||
|
||||
for video, comp in top_videos:
|
||||
import_video(video['url'], tags=["competitor", comp, "top-performer"])
|
||||
|
||||
# Stage 3: Content analysis
|
||||
print("🔬 Stage 3: Analyzing content patterns...")
|
||||
content_analysis = chat_personal("""
|
||||
Tags: competitor, top-performer
|
||||
Question: Comprehensive content analysis:
|
||||
1. Content themes (with % breakdown)
|
||||
2. Average video length by theme
|
||||
3. Hook patterns (first 5 seconds)
|
||||
4. CTA strategies
|
||||
5. Production quality levels
|
||||
6. Posting frequency
|
||||
""")
|
||||
|
||||
# Stage 4: Messaging extraction
|
||||
print("💬 Stage 4: Extracting messaging...")
|
||||
messaging = chat_personal("""
|
||||
Tags: competitor, top-performer
|
||||
Question: What are their core messaging pillars?
|
||||
What customer pain points do they address?
|
||||
What value propositions do they emphasize?
|
||||
What proof/credibility elements do they use?
|
||||
""")
|
||||
|
||||
# Stage 5: Gap identification
|
||||
print("🎯 Stage 5: Identifying opportunities...")
|
||||
gaps = chat_personal("""
|
||||
Tags: competitor, top-performer
|
||||
Question: Based on their content coverage, identify:
|
||||
1. Topics they're NOT covering (search-demand exists)
|
||||
2. Angles they're missing on covered topics
|
||||
3. Audience questions unanswered
|
||||
4. Format opportunities (they use X, but Y format might work)
|
||||
""")
|
||||
|
||||
# Stage 6: Actionable recommendations
|
||||
print("📋 Stage 6: Generating recommendations...")
|
||||
recommendations = chat_personal("""
|
||||
Based on the competitive analysis (tags: competitor, top-performer),
|
||||
generate actionable content strategy recommendations:
|
||||
|
||||
1. QUICK WINS: What can we do in next 2 weeks?
|
||||
2. STRATEGIC BETS: What should we invest in next quarter?
|
||||
3. AVOID: What are they doing that's not working?
|
||||
4. DIFFERENTIATION: How can we stand out?
|
||||
|
||||
Format with specific video ideas and rationale.
|
||||
""")
|
||||
|
||||
# Stage 7: Report generation
|
||||
print("📊 Stage 7: Compiling final report...")
|
||||
final_report = f"""
|
||||
COMPETITIVE CONTENT INTELLIGENCE REPORT
|
||||
Date: {current_date}
|
||||
Scope: {len(all_videos)} videos analyzed from {len(competitors)} competitors
|
||||
|
||||
{content_analysis}
|
||||
|
||||
{messaging}
|
||||
|
||||
{gaps}
|
||||
|
||||
{recommendations}
|
||||
"""
|
||||
|
||||
create_memory(final_report, tags=["competitive-report", "strategy"])
|
||||
print("✅ Complete! Report stored in knowledge base.")
|
||||
```
|
||||
|
||||
**Timeline:** 40 hours manual → 3 hours automated
|
||||
**Output:** Comprehensive competitive intelligence report with actionable recommendations
|
||||
|
||||
---
|
||||
|
||||
## ROI Summary
|
||||
|
||||
| Use Case | Manual Time | Automated Time | Time Saved | Quality Improvement |
|
||||
|----------|-------------|----------------|------------|---------------------|
|
||||
| Competitor Analysis | 40 hours | 3 hours | 37 hours | +50% depth |
|
||||
| Content Research | 20 hours | 2 hours | 18 hours | +70% coverage |
|
||||
| Meeting Notes | 30 min/meeting | 2 min/meeting | 28 min | +90% completeness |
|
||||
| Brand Monitoring | 10 hours/week | 1 hour/week | 9 hours | Real-time vs weekly |
|
||||
| Training KB | N/A | 3 hours setup | N/A | Instant access |
|
||||
| Influencer Research | 15 hours | 2 hours | 13 hours | +60% data depth |
|
||||
|
||||
**Average ROI:** 40x time savings, 60% quality improvement
|
||||
@@ -1,6 +1,6 @@
|
||||
site_name: Claude Code Skills & Plugins
|
||||
site_url: https://alirezarezvani.github.io/claude-skills
|
||||
site_description: "170 production-ready skills, 14 agents, and 12 commands for Claude Code, OpenAI Codex, Gemini CLI, and OpenClaw. Reusable expertise for engineering, product, marketing, compliance, and more."
|
||||
site_description: "170 production-ready skills, 14 agents, and 14 commands for Claude Code, OpenAI Codex, Gemini CLI, and OpenClaw. Reusable expertise for engineering, product, marketing, compliance, and more."
|
||||
site_author: Alireza Rezvani
|
||||
repo_url: https://github.com/alirezarezvani/claude-skills
|
||||
repo_name: alirezarezvani/claude-skills
|
||||
@@ -309,6 +309,7 @@ nav:
|
||||
- Finance:
|
||||
- Overview: skills/finance/index.md
|
||||
- "Financial Analyst": skills/finance/financial-analyst.md
|
||||
- "SaaS Metrics Coach": skills/finance/saas-metrics-coach.md
|
||||
- Agents:
|
||||
- Overview: agents/index.md
|
||||
- "CS Agile Product Owner": agents/cs-agile-product-owner.md
|
||||
@@ -329,12 +330,14 @@ nav:
|
||||
- Overview: commands/index.md
|
||||
- "/changelog": commands/changelog.md
|
||||
- "/competitive-matrix": commands/competitive-matrix.md
|
||||
- "/financial-health": commands/financial-health.md
|
||||
- "/okr": commands/okr.md
|
||||
- "/persona": commands/persona.md
|
||||
- "/pipeline": commands/pipeline.md
|
||||
- "/project-health": commands/project-health.md
|
||||
- "/retro": commands/retro.md
|
||||
- "/rice": commands/rice.md
|
||||
- "/saas-health": commands/saas-health.md
|
||||
- "/sprint-health": commands/sprint-health.md
|
||||
- "/tdd": commands/tdd.md
|
||||
- "/tech-debt": commands/tech-debt.md
|
||||
|
||||
Reference in New Issue
Block a user