Dev (#269)
* docs: restructure README.md — 2,539 → 209 lines (#247) - Cut from 2,539 lines / 73 sections to 209 lines / 18 sections - Consolidated 4 install methods into one unified section - Moved all skill details to domain-level READMEs (linked from table) - Front-loaded value prop and keywords for SEO - Added POWERFUL tier highlight section - Added skill-security-auditor showcase section - Removed stale Q4 2025 roadmap, outdated ROI claims, duplicate content - Fixed all internal links - Clean heading hierarchy (H2 for main sections only) Closes #233 Co-authored-by: Leo <leo@openclaw.ai> * fix: enhance 5 skills with scripts, references, and Anthropic best practices (#248) * fix(skill): enhance git-worktree-manager with scripts, references, and Anthropic best practices * fix(skill): enhance mcp-server-builder with scripts, references, and Anthropic best practices * fix(skill): enhance changelog-generator with scripts, references, and Anthropic best practices * fix(skill): enhance ci-cd-pipeline-builder with scripts, references, and Anthropic best practices * fix(skill): enhance prompt-engineer-toolkit with scripts, references, and Anthropic best practices * docs: update README, CHANGELOG, and plugin metadata * fix: correct marketing plugin count, expand thin references --------- Co-authored-by: Leo <leo@openclaw.ai> * ci: Add VirusTotal security scan for skills (#252) * Dev (#231) * Improve senior-fullstack skill description and workflow validation - Expand frontmatter description with concrete actions and trigger clauses - Add validation steps to scaffolding workflow (verify scaffold succeeded) - Add re-run verification step to audit workflow (confirm P0 fixes) * chore: sync codex skills symlinks [automated] * fix(skill): normalize senior-fullstack frontmatter to inline format Normalize YAML description from block scalar (>) to inline single-line format matching all other 50+ skills. Align frontmatter trigger phrases with the body's Trigger Phrases section to eliminate duplication. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix(ci): add GITHUB_TOKEN to checkout + restore corrupted skill descriptions - Add token: ${{ secrets.GITHUB_TOKEN }} to actions/checkout@v4 in sync-codex-skills.yml so git-auto-commit-action can push back to branch (fixes: fatal: could not read Username, exit 128) - Restore correct description for incident-commander (was: 'Skill from engineering-team') - Restore correct description for senior-fullstack (was: '>') * fix(ci): pass PROJECTS_TOKEN to fix automated commits + remove duplicate checkout Fixes PROJECTS_TOKEN passthrough for git-auto-commit-action and removes duplicate checkout step in pr-issue-auto-close workflow. * fix(ci): remove stray merge conflict marker in sync-codex-skills.yml (#221) Co-authored-by: Leo <leo@leo-agent-server> * fix(ci): fix workflow errors + add OpenClaw support (#222) * feat: add 20 new practical skills for professional Claude Code users New skills across 5 categories: Engineering (12): - git-worktree-manager: Parallel dev with port isolation & env sync - ci-cd-pipeline-builder: Generate GitHub Actions/GitLab CI from stack analysis - mcp-server-builder: Build MCP servers from OpenAPI specs - changelog-generator: Conventional commits to structured changelogs - pr-review-expert: Blast radius analysis & security scan for PRs - api-test-suite-builder: Auto-generate test suites from API routes - env-secrets-manager: .env management, leak detection, rotation workflows - database-schema-designer: Requirements to migrations & types - codebase-onboarding: Auto-generate onboarding docs from codebase - performance-profiler: Node/Python/Go profiling & optimization - runbook-generator: Operational runbooks from codebase analysis - monorepo-navigator: Turborepo/Nx/pnpm workspace management Engineering Team (2): - stripe-integration-expert: Subscriptions, webhooks, billing patterns - email-template-builder: React Email/MJML transactional email systems Product Team (3): - saas-scaffolder: Full SaaS project generation from product brief - landing-page-generator: High-converting landing pages with copy frameworks - competitive-teardown: Structured competitive product analysis Business Growth (1): - contract-and-proposal-writer: Contracts, SOWs, NDAs per jurisdiction Marketing (1): - prompt-engineer-toolkit: Systematic prompt development & A/B testing Designed for daily professional use and commercial distribution. * chore: sync codex skills symlinks [automated] * docs: update README with 20 new skills, counts 65→86, new skills section * docs: add commercial distribution plan (Stan Store + Gumroad) * docs: rewrite CHANGELOG.md with v2.0.0 release (65 skills, 9 domains) (#226) * docs: rewrite CHANGELOG.md with v2.0.0 release (65 skills, 9 domains) - Consolidate 191 commits since v1.0.2 into proper v2.0.0 entry - Document 12 POWERFUL-tier skills, 37 refactored skills - Add new domains: business-growth, finance - Document Codex support and marketplace integration - Update version history summary table - Clean up [Unreleased] to only planned work * docs: add 24 POWERFUL-tier skills to plugin, fix counts to 85 across all docs - Add engineering-advanced-skills plugin (24 POWERFUL-tier skills) to marketplace.json - Add 13 missing skills to CHANGELOG v2.0.0 (agent-workflow-designer, api-test-suite-builder, changelog-generator, ci-cd-pipeline-builder, codebase-onboarding, database-schema-designer, env-secrets-manager, git-worktree-manager, mcp-server-builder, monorepo-navigator, performance-profiler, pr-review-expert, runbook-generator) - Fix skill count: 86→85 (excl sample-skill) across README, CHANGELOG, marketplace.json - Fix stale 53→85 references in README - Add engineering-advanced-skills install command to README - Update marketplace.json version to 2.0.0 --------- Co-authored-by: Leo <leo@openclaw.ai> * feat: add skill-security-auditor POWERFUL-tier skill (#230) Security audit and vulnerability scanner for AI agent skills before installation. Scans for: - Code execution risks (eval, exec, os.system, subprocess shell injection) - Data exfiltration (outbound HTTP, credential harvesting, env var extraction) - Prompt injection in SKILL.md (system override, role hijack, safety bypass) - Dependency supply chain (typosquatting, unpinned versions, runtime installs) - File system abuse (boundary violations, binaries, symlinks, hidden files) - Privilege escalation (sudo, SUID, cron manipulation, shell config writes) - Obfuscation (base64, hex encoding, chr chains, codecs) Produces clear PASS/WARN/FAIL verdict with per-finding remediation guidance. Supports local dirs, git repo URLs, JSON output, strict mode, and CI/CD integration. Includes: - scripts/skill_security_auditor.py (1049 lines, zero dependencies) - references/threat-model.md (complete attack vector documentation) - SKILL.md with usage guide and report format Tested against: rag-architect (PASS), agent-designer (PASS), senior-secops (FAIL - correctly flagged eval/exec patterns). Co-authored-by: Leo <leo@openclaw.ai> * docs: add skill-security-auditor to marketplace, README, and CHANGELOG - Add standalone plugin entry for skill-security-auditor in marketplace.json - Update engineering-advanced-skills plugin description to include it - Update skill counts: 85→86 across README, CHANGELOG, marketplace - Add install command to README Quick Install section - Add to CHANGELOG [Unreleased] section --------- Co-authored-by: Baptiste Fernandez <fernandez.baptiste1@gmail.com> Co-authored-by: alirezarezvani <5697919+alirezarezvani@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: Leo <leo@leo-agent-server> Co-authored-by: Leo <leo@openclaw.ai> * Dev (#249) * docs: restructure README.md — 2,539 → 209 lines (#247) - Cut from 2,539 lines / 73 sections to 209 lines / 18 sections - Consolidated 4 install methods into one unified section - Moved all skill details to domain-level READMEs (linked from table) - Front-loaded value prop and keywords for SEO - Added POWERFUL tier highlight section - Added skill-security-auditor showcase section - Removed stale Q4 2025 roadmap, outdated ROI claims, duplicate content - Fixed all internal links - Clean heading hierarchy (H2 for main sections only) Closes #233 Co-authored-by: Leo <leo@openclaw.ai> * fix: enhance 5 skills with scripts, references, and Anthropic best practices (#248) * fix(skill): enhance git-worktree-manager with scripts, references, and Anthropic best practices * fix(skill): enhance mcp-server-builder with scripts, references, and Anthropic best practices * fix(skill): enhance changelog-generator with scripts, references, and Anthropic best practices * fix(skill): enhance ci-cd-pipeline-builder with scripts, references, and Anthropic best practices * fix(skill): enhance prompt-engineer-toolkit with scripts, references, and Anthropic best practices * docs: update README, CHANGELOG, and plugin metadata * fix: correct marketing plugin count, expand thin references --------- Co-authored-by: Leo <leo@openclaw.ai> --------- Co-authored-by: Leo <leo@openclaw.ai> * Dev (#250) * docs: restructure README.md — 2,539 → 209 lines (#247) - Cut from 2,539 lines / 73 sections to 209 lines / 18 sections - Consolidated 4 install methods into one unified section - Moved all skill details to domain-level READMEs (linked from table) - Front-loaded value prop and keywords for SEO - Added POWERFUL tier highlight section - Added skill-security-auditor showcase section - Removed stale Q4 2025 roadmap, outdated ROI claims, duplicate content - Fixed all internal links - Clean heading hierarchy (H2 for main sections only) Closes #233 Co-authored-by: Leo <leo@openclaw.ai> * fix: enhance 5 skills with scripts, references, and Anthropic best practices (#248) * fix(skill): enhance git-worktree-manager with scripts, references, and Anthropic best practices * fix(skill): enhance mcp-server-builder with scripts, references, and Anthropic best practices * fix(skill): enhance changelog-generator with scripts, references, and Anthropic best practices * fix(skill): enhance ci-cd-pipeline-builder with scripts, references, and Anthropic best practices * fix(skill): enhance prompt-engineer-toolkit with scripts, references, and Anthropic best practices * docs: update README, CHANGELOG, and plugin metadata * fix: correct marketing plugin count, expand thin references --------- Co-authored-by: Leo <leo@openclaw.ai> --------- Co-authored-by: Leo <leo@openclaw.ai> * ci: add VirusTotal security scan for skills - Scans changed skill directories on PRs to dev/main - Scans all skills on release publish - Posts scan results as PR comment with analysis links - Rate-limited to 4 req/min (free tier compatible) - Appends VirusTotal links to release body on publish * fix: resolve YAML lint errors in virustotal workflow - Add document start marker (---) - Quote 'on' key for truthy lint rule - Remove trailing spaces - Break long lines under 160 char limit --------- Co-authored-by: Baptiste Fernandez <fernandez.baptiste1@gmail.com> Co-authored-by: alirezarezvani <5697919+alirezarezvani@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: Leo <leo@leo-agent-server> Co-authored-by: Leo <leo@openclaw.ai> * feat: add playwright-pro plugin — production-grade Playwright testing toolkit (#254) Complete Claude Code plugin with: - 9 skills (/pw:init, generate, review, fix, migrate, coverage, testrail, browserstack, report) - 3 specialized agents (test-architect, test-debugger, migration-planner) - 55 test case templates across 11 categories (auth, CRUD, checkout, search, forms, dashboard, settings, onboarding, notifications, API, accessibility) - TestRail MCP server (TypeScript) — 8 tools for bidirectional sync - BrowserStack MCP server (TypeScript) — 7 tools for cross-browser testing - Smart hooks (auto-validate tests, auto-detect Playwright projects) - 6 curated reference docs (golden rules, locators, assertions, fixtures, pitfalls, flaky tests) - Leverages Claude Code built-ins (/batch, /debug, Explore subagent) - Zero-config for core features; TestRail/BrowserStack via env vars - Both TypeScript and JavaScript support throughout Co-authored-by: Leo <leo@openclaw.ai> * feat: add playwright-pro to marketplace registry (#256) - New plugin: playwright-pro (9 skills, 3 agents, 55 templates, 2 MCP servers) - Install: /plugin install playwright-pro@claude-code-skills - Total marketplace plugins: 17 Co-authored-by: Leo <leo@openclaw.ai> * fix: integrate playwright-pro across all platforms (#258) - Add root SKILL.md for OpenClaw and ClawHub compatibility - Add to README: Skills Overview table, install section, badge count - Regenerate .codex/skills-index.json with playwright-pro entry - Add .codex/skills/playwright-pro symlink for Codex CLI - Fix YAML frontmatter (single-line description for index parsing) Platforms verified: - Claude Code: marketplace.json ✅ (merged in PR #256) - Codex CLI: symlink + skills-index.json ✅ - OpenClaw: SKILL.md auto-discovered by install script ✅ - ClawHub: published as playwright-pro@1.1.0 ✅ Co-authored-by: Leo <leo@openclaw.ai> * docs: update CLAUDE.md — reflect 87 skills across 9 domains Sync CLAUDE.md with actual repository state: add Engineering POWERFUL tier (25 skills), update all skill counts, add plugin registry references, and replace stale sprint section with v2.0.0 version info. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs: mention Claude Code in project description Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add self-improving-agent plugin — auto-memory curation for Claude Code (#260) New plugin: engineering-team/self-improving-agent/ - 5 skills: /si:review, /si:promote, /si:extract, /si:status, /si:remember - 2 agents: memory-analyst, skill-extractor - 1 hook: PostToolUse error capture (zero overhead on success) - 3 reference docs: memory architecture, promotion rules, rules directory patterns - 2 templates: rule template, skill template - 20 files, 1,829 lines Integrates natively with Claude Code's auto-memory (v2.1.32+). Reads from ~/.claude/projects/<path>/memory/ — no duplicate storage. Promotes proven patterns from MEMORY.md to CLAUDE.md or .claude/rules/. Also: - Added to marketplace.json (18 plugins total) - Added to README (Skills Overview + install section) - Updated badge count to 88+ - Regenerated .codex/skills-index.json + symlink Co-authored-by: Leo <leo@openclaw.ai> * feat: C-Suite expansion — 8 new executive advisory roles (2→10) (#264) * feat: C-Suite expansion — 8 new executive advisory roles Add COO, CPO, CMO, CFO, CRO, CISO, CHRO advisors and Executive Mentor. Expands C-level advisory from 2 to 10 roles with 74 total files. Each role includes: - SKILL.md (lean, <5KB, ~1200 tokens for context efficiency) - Reference docs (loaded on demand, not at startup) - Python analysis scripts (stdlib only, runnable CLI) Executive Mentor features /em: slash commands (challenge, board-prep, hard-call, stress-test, postmortem) with devil's advocate agent. 21 Python tools, 24 reference frameworks, 28,379 total lines. All SKILL.md files combined: ~17K tokens (8.5% of 200K context window). Badge: 88 → 116 skills * feat: C-Suite orchestration layer + 18 complementary skills ORCHESTRATION (new): - cs-onboard: Founder interview → company-context.md - chief-of-staff: Routing, synthesis, inter-agent orchestration - board-meeting: 6-phase multi-agent deliberation protocol - decision-logger: Two-layer memory (raw transcripts + approved decisions) - agent-protocol: Inter-agent invocation with loop prevention - context-engine: Company context loading + anonymization CROSS-CUTTING CAPABILITIES (new): - board-deck-builder: Board/investor update assembly - scenario-war-room: Cascading multi-variable what-if modeling - competitive-intel: Systematic competitor tracking + battlecards - org-health-diagnostic: Cross-functional health scoring (8 dimensions) - ma-playbook: M&A strategy (acquiring + being acquired) - intl-expansion: International market entry frameworks CULTURE & COLLABORATION (new): - culture-architect: Values → behaviors, culture code, health assessment - company-os: EOS/Scaling Up operating system selection + implementation - founder-coach: Founder development, delegation, blind spots - strategic-alignment: Strategy cascade, silo detection, alignment scoring - change-management: ADKAR-based change rollout framework - internal-narrative: One story across employees/investors/customers UPGRADES TO EXISTING ROLES: - All 10 roles get reasoning technique directives - All 10 roles get company-context.md integration - All 10 roles get board meeting isolation rules - CEO gets stage-adaptive temporal horizons (seed→C) Key design decisions: - Two-layer memory prevents hallucinated consensus from rejected ideas - Phase 2 isolation: agents think independently before cross-examination - Executive Mentor (The Critic) sees all perspectives, others don't - 25 Python tools total (stdlib only, no dependencies) 52 new files, 10 modified, 10,862 new lines. Total C-suite ecosystem: 134 files, 39,131 lines. * fix: connect all dots — Chief of Staff routes to all 28 skills - Added complementary skills registry to routing-matrix.md - Chief of Staff SKILL.md now lists all 28 skills in ecosystem - Added integration tables to scenario-war-room and competitive-intel - Badge: 116 → 134 skills - README: C-Level Advisory count 10 → 28 Quality audit passed: ✅ All 10 roles: company-context, reasoning, isolation, invocation ✅ All 6 phases in board meeting ✅ Two-layer memory with DO_NOT_RESURFACE ✅ Loop prevention (no self-invoke, max depth 2, no circular) ✅ All /em: commands present ✅ All complementary skills cross-reference roles ✅ Chief of Staff routes to every skill in ecosystem * refactor: CEO + CTO advisors upgraded to C-suite parity Both roles now match the structural standard of all new roles: - CEO: 11.7KB → 6.8KB SKILL.md (heavy content stays in references) - CTO: 10KB → 7.2KB SKILL.md (heavy content stays in references) Added to both: - Integration table (who they work with and when) - Key diagnostic questions - Structured metrics dashboard table - Consistent section ordering (Keywords → Quick Start → Responsibilities → Questions → Metrics → Red Flags → Integration → Reasoning → Context) CEO additions: - Stage-adaptive temporal horizons (seed=3m/6m/12m → B+=1y/3y/5y) - Cross-references to culture-architect and board-deck-builder CTO additions: - Key Questions section (7 diagnostic questions) - Structured metrics table (DORA + debt + team + architecture + cost) - Cross-references to all peer roles All 10 roles now pass structural parity: ✅ Keywords ✅ QuickStart ✅ Questions ✅ Metrics ✅ RedFlags ✅ Integration * feat: add proactive triggers + output artifacts to all 10 roles Every C-suite role now specifies: - Proactive Triggers: 'surface these without being asked' — context-driven early warnings that make advisors proactive, not reactive - Output Artifacts: concrete deliverables per request type (what you ask → what you get) CEO: runway alerts, board prep triggers, strategy review nudges CTO: deploy frequency monitoring, tech debt thresholds, bus factor flags COO: blocker detection, scaling threshold warnings, cadence gaps CPO: retention curve monitoring, portfolio dog detection, research gaps CMO: CAC trend monitoring, positioning gaps, budget staleness CFO: runway forecasting, burn multiple alerts, scenario planning gaps CRO: NRR monitoring, pipeline coverage, pricing review triggers CISO: audit overdue alerts, compliance gaps, vendor risk CHRO: retention risk, comp band gaps, org scaling thresholds Executive Mentor: board prep triggers, groupthink detection, hard call surfacing This transforms the C-suite from reactive advisors into proactive partners. * feat: User Communication Standard — structured output for all roles Defines 3 output formats in agent-protocol/SKILL.md: 1. Standard Output: Bottom Line → What → Why → How to Act → Risks → Your Decision 2. Proactive Alert: What I Noticed → Why It Matters → Action → Urgency (🔴🟡⚪) 3. Board Meeting: Decision Required → Perspectives → Agree/Disagree → Critic → Action Items 10 non-negotiable rules: - Bottom line first, always - Results and decisions only (no process narration) - What + Why + How for every finding - Actions have owners and deadlines ('we should consider' is banned) - Decisions framed as options with trade-offs - Founder is the highest authority — roles recommend, founder decides - Risks are concrete (if X → Y, costs $Z) - Max 5 bullets per section - No jargon without explanation - Silence over fabricated updates All 10 roles reference this standard. Chief of Staff enforces it as a quality gate. Board meeting Phase 4 uses the Board Meeting Output format. * feat: Internal Quality Loop — verification before delivery No role presents to the founder without passing verification: Step 1: Self-Verification (every role, every time) - Source attribution: where did each data point come from? - Assumption audit: [VERIFIED] vs [ASSUMED] tags on every finding - Confidence scoring: 🟢 high / 🟡 medium / 🔴 low per finding - Contradiction check against company-context + decision log - 'So what?' test: every finding needs a business consequence Step 2: Peer Verification (cross-functional) - Financial claims → CFO validates math - Revenue projections → CRO validates pipeline backing - Technical feasibility → CTO validates - People/hiring impact → CHRO validates - Skip for single-domain, low-stakes questions Step 3: Critic Pre-Screen (high-stakes only) - Irreversible decisions, >20% runway impact, strategy changes - Executive Mentor finds weakest point before founder sees it - Suspicious consensus triggers mandatory pre-screen Step 4: Course Correction (after founder feedback) - Approve → log + assign actions - Modify → re-verify changed parts - Reject → DO_NOT_RESURFACE + learn why - 30/60/90 day post-decision review Board meeting contributions now require self-verified format with confidence tags and source attribution on every finding. * fix: resolve PR review issues 1, 4, and minor observation Issue 1: c-level-advisor/CLAUDE.md — completely rewritten - Was: 2 skills (CEO, CTO only), dated Nov 2025 - Now: full 28-skill ecosystem map with architecture diagram, all roles/orchestration/cross-cutting/culture skills listed, design decisions, integration with other domains Issue 4: Root CLAUDE.md — updated all stale counts - 87 → 134 skills across all 3 references - C-Level: 2 → 33 (10 roles + 5 mentor commands + 18 complementary) - Tool count: 160+ → 185+ - Reference count: 200+ → 250+ Minor observation: Documented plugin.json convention - Explained in c-level-advisor/CLAUDE.md that only executive-mentor has plugin.json because only it has slash commands (/em: namespace) - Other skills are invoked by name through Chief of Staff or directly Also fixed: README.md 88+ → 134 in two places (first line + skills section) * fix: update all plugin/index registrations for 28-skill C-suite 1. c-level-advisor/.claude-plugin/plugin.json — v2.0.0 - Was: 2 skills, generic description - Now: all 28 skills listed with descriptions, all 25 scripts, namespace 'cs', full ecosystem description 2. .codex/skills-index.json — added 18 complementary skills - Was: 10 roles only - Now: 28 total c-level entries (10 roles + 6 orchestration + 6 cross-cutting + 6 culture) - Each with full description for skill discovery 3. .claude-plugin/marketplace.json — updated c-level-skills entry - Was: generic 2-skill description - Now: v2.0.0, full 28-skill ecosystem description, skills_count: 28, scripts_count: 25 * feat: add root SKILL.md for c-level-advisor ClawHub package --------- Co-authored-by: Leo <leo@openclaw.ai> * chore: sync codex skills symlinks [automated] * feat: Marketing Division expansion — 7 → 42 skills (#266) * feat: Skill Authoring Standard + Marketing Expansion plans SKILL-AUTHORING-STANDARD.md — the DNA of every skill in this repo: 10 universal patterns codified from C-Suite innovations + Corey Haines' marketingskills patterns: 1. Context-First: check domain context, ask only for gaps 2. Practitioner Voice: expert persona, goal-oriented, not textbook 3. Multi-Mode Workflows: build from scratch / optimize existing / situation-specific 4. Related Skills Navigation: when to use, when NOT to, bidirectional 5. Reference Separation: SKILL.md lean (≤10KB), refs deep 6. Proactive Triggers: surface issues without being asked 7. Output Artifacts: request → specific deliverable mapping 8. Quality Loop: self-verify, confidence tagging 9. Communication Standard: bottom line first, structured output 10. Python Tools: stdlib-only, CLI-first, JSON output, sample data Marketing expansion plans for 40-skill marketing division build. * feat: marketing foundation — context + ops router + authoring standard marketing-context/: Foundation skill every marketing skill reads first - SKILL.md: 3 modes (auto-draft, guided interview, update) - templates/marketing-context-template.md: 14 sections covering product, audience, personas, pain points, competitive landscape, differentiation, objections, switching dynamics, customer language (verbatim), brand voice, style guide, proof points, SEO context, goals - scripts/context_validator.py: Scores completeness 0-100, section-by-section marketing-ops/: Central router for 40-skill marketing ecosystem - Full routing matrix: 7 pods + cross-domain routing to 6 skills in business-growth, product-team, engineering-team, c-level-advisor - Campaign orchestration sequences (launch, content, CRO sprint) - Quality gate matching C-Suite standard - scripts/campaign_tracker.py: Campaign status tracking with progress, overdue detection, pod coverage, blocker identification SKILL-AUTHORING-STANDARD.md: Universal DNA for all skills - 10 patterns: context-first, practitioner voice, multi-mode workflows, related skills navigation, reference separation, proactive triggers, output artifacts, quality loop, communication standard, python tools - Quality checklist for skill completion verification - Domain context file mapping for all 5 domains * feat: import 20 workspace marketing skills + standard sections Imported 20 marketing skills from OpenClaw workspace into repo: Content Pod (5): content-strategy, copywriting, copy-editing, social-content, marketing-ideas SEO Pod (2): seo-audit (+ references enriched by subagent), programmatic-seo (+ refs) CRO Pod (5): page-cro, form-cro, signup-flow-cro, onboarding-cro, popup-cro, paywall-upgrade-cro Channels Pod (2): email-sequence, paid-ads Growth + Intel + GTM (5): ab-test-setup, competitor-alternatives, marketing-psychology, launch-strategy, brand-guidelines All 29 skills now have standard sections per SKILL-AUTHORING-STANDARD.md: ✅ Proactive Triggers (4-5 per skill) ✅ Output Artifacts table ✅ Communication standard reference ✅ Related Skills with WHEN/NOT disambiguation Subagents enriched 8 skills with additional reference docs: seo-audit, programmatic-seo, page-cro, form-cro, onboarding-cro, popup-cro, paywall-upgrade-cro, email-sequence 43 files, 10,566 lines added. * feat: build 13 new marketing skills + social-media-manager upgrade All skills are 100% original work — inspired by industry best practices, written from scratch in our own voice following SKILL-AUTHORING-STANDARD.md. NEW Content Pod (2): content-production — full research→draft→optimize pipeline, content_scorer.py content-humanizer — AI pattern detection + voice injection, humanizer_scorer.py NEW SEO Pod (3): ai-seo — AI search optimization (AEO/GEO/LLMO), entirely new category schema-markup — JSON-LD structured data, schema_validator.py site-architecture — URL structure + internal linking, sitemap_analyzer.py NEW Channels Pod (2): cold-email — B2B outreach (distinct from email-sequence lifecycle) ad-creative — bulk ad generation + platform specs, ad_copy_validator.py NEW Growth Pod (3): churn-prevention — cancel flows + save offers + dunning, churn_impact_calculator.py referral-program — referral + affiliate programs free-tool-strategy — engineering as marketing NEW Intelligence Pod (1): analytics-tracking — GA4/GTM setup + event taxonomy, tracking_plan_generator.py NEW Sales Pod (1): pricing-strategy — pricing, packaging, monetization UPGRADED: social-media-analyzer → social-media-manager (strategy, calendar, community) Totals: 42 skills, 27 Python scripts, 60 reference docs, 163 files, 43,265 lines * feat: update index, marketplace, README for 42 marketing skills - skills-index.json: 89 → 124 skills (42 marketing entries) - marketplace.json: marketing-skills v2.0.0 (42 skills, 27 tools) - README.md: badge 134 → 169, marketing row updated - prompt-engineer-toolkit: added YAML frontmatter - Removed build logs from repo - Parity check: 42/42 passed (YAML + Related + Proactive + Output + Communication) * fix: merge content-creator into content-production, split marketing-psychology Quality audit fixes: 1. content-creator → DEPRECATED redirect - Scripts (brand_voice_analyzer.py, seo_optimizer.py) moved to content-production - SKILL.md replaced with redirect to content-production + content-strategy - Eliminates duplicate routing confusion 2. marketing-psychology → 24KB split to 6.8KB + reference - 70+ mental models moved to references/mental-models-catalog.md (397 lines) - SKILL.md now lean: categories overview, most-used models, quick reference - Saves ~4,300 tokens per invocation * feat: add plugin configs, Codex/OpenClaw compatibility, ClawHub packaging - marketing-skill/SKILL.md: ClawHub-compatible root with Quick Start for Claude Code, Codex CLI, OpenClaw - marketing-skill/CLAUDE.md: Agent instructions (routing, context, anti-patterns) - marketing-skill/.codex/instructions.md: Codex CLI skill routing - .claude-plugin/marketplace.json: deduplicated, marketing-skills v2.0.0 - .codex/skills-index.json: content-creator marked deprecated, psychology updated - Total: 42 skills, 27 Python tools, 60 references, 18 plugins * feat: add 16 Python tools to knowledge-only skills Enriched 12 previously tool-less skills with practical Python scripts: - seo-audit/seo_checker.py — HTML on-page SEO analysis (0-100) - copywriting/headline_scorer.py — headline quality scoring (0-100) - copy-editing/readability_scorer.py — Flesch + passive + filler detection - content-strategy/topic_cluster_mapper.py — keyword clustering - page-cro/conversion_audit.py — HTML CRO signal analysis (0-100) - paid-ads/roas_calculator.py — ROAS/CPA/CPL calculator - email-sequence/sequence_analyzer.py — email sequence scoring (0-100) - form-cro/form_field_analyzer.py — form field CRO audit (0-100) - onboarding-cro/activation_funnel_analyzer.py — funnel drop-off analysis - programmatic-seo/url_pattern_generator.py — URL pattern planning - ab-test-setup/sample_size_calculator.py — statistical sample sizing - signup-flow-cro/funnel_drop_analyzer.py — signup funnel analysis - launch-strategy/launch_readiness_scorer.py — launch checklist scoring - competitor-alternatives/comparison_matrix_builder.py — feature comparison - social-media-manager/social_calendar_generator.py — content calendar - readability_scorer.py — fixed demo mode for non-TTY execution All 43/43 scripts pass execution. All stdlib-only, zero pip installs. Total: 42 skills, 43 Python tools, 60+ reference docs. * feat: add 3 more Python tools + improve 6 existing scripts New tools from build agent: - email-sequence/scripts/sequence_analyzer.py — email sequence scoring (91/100 demo) - paid-ads/scripts/roas_calculator.py — ROAS/CPA/CPL/break-even calculator - competitor-alternatives/scripts/comparison_matrix_builder.py — feature matrix Improved scripts (better demo modes, fuller analysis): - seo_checker.py, headline_scorer.py, readability_scorer.py, conversion_audit.py, topic_cluster_mapper.py, launch_readiness_scorer.py Total: 42 skills, 47 Python tools, all passing. * fix: remove duplicate scripts from deprecated content-creator Scripts already live in content-production/scripts/. The content-creator directory is now a pure redirect (SKILL.md only + legacy assets/refs). * fix: scope VirusTotal scan to executable files only Skip scanning .md, .py, .json, .yml — they're plain text files that VirusTotal can't meaningfully analyze. This prevents 429 rate limit errors on PRs with many text file changes (like 42 marketing skills). Scan still covers: .js, .ts, .sh, .mjs, .cjs, .exe, .dll, .so, .bin, .wasm --------- Co-authored-by: Leo <leo@openclaw.ai> * chore: sync codex skills symlinks [automated] --------- Co-authored-by: Leo <leo@openclaw.ai> Co-authored-by: Baptiste Fernandez <fernandez.baptiste1@gmail.com> Co-authored-by: alirezarezvani <5697919+alirezarezvani@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: Leo <leo@leo-agent-server>
This commit is contained in:
Alireza Rezvani
GitHub
@@ -0,0 +1,406 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
referral_roi_calculator.py — Calculates referral program ROI.
|
||||
|
||||
Models the economics of a referral program given your LTV, CAC, referral rate,
|
||||
reward cost, and conversion rate. Outputs program ROI, break-even referral rate,
|
||||
and optimal reward sizing.
|
||||
|
||||
Usage:
|
||||
python3 referral_roi_calculator.py # runs embedded sample
|
||||
python3 referral_roi_calculator.py params.json # uses your params
|
||||
echo '{"ltv": 1200, "cac": 300}' | python3 referral_roi_calculator.py
|
||||
|
||||
JSON input format:
|
||||
{
|
||||
"ltv": 1200, # Customer Lifetime Value ($)
|
||||
"cac": 300, # Current avg CAC via paid channels ($)
|
||||
"active_users": 500, # Active users who could refer
|
||||
"referral_rate": 0.05, # % of active users who refer each month (0.05 = 5%)
|
||||
"referrals_per_referrer": 2.5, # Avg referrals sent per active referrer
|
||||
"referral_conversion_rate": 0.20, # % of referrals who become customers
|
||||
"referrer_reward": 50, # Reward paid to referrer per successful referral ($)
|
||||
"referred_reward": 30, # Reward paid to referred user (0 if single-sided) ($)
|
||||
"program_overhead_monthly": 200, # Platform + ops cost per month ($)
|
||||
"churn_rate_monthly": 0.03, # Monthly churn rate (used for LTV validation)
|
||||
"months_to_model": 12 # How many months to project
|
||||
}
|
||||
"""
|
||||
|
||||
import json
|
||||
import sys
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Core calculation functions
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def calculate_referrals_per_month(params):
|
||||
"""How many successful referrals per month?"""
|
||||
active_users = params["active_users"]
|
||||
referral_rate = params["referral_rate"]
|
||||
referrals_per_referrer = params["referrals_per_referrer"]
|
||||
conversion_rate = params["referral_conversion_rate"]
|
||||
|
||||
active_referrers = active_users * referral_rate
|
||||
referrals_sent = active_referrers * referrals_per_referrer
|
||||
conversions = referrals_sent * conversion_rate
|
||||
|
||||
return {
|
||||
"active_referrers": round(active_referrers, 1),
|
||||
"referrals_sent": round(referrals_sent, 1),
|
||||
"new_customers_per_month": round(conversions, 1),
|
||||
}
|
||||
|
||||
|
||||
def calculate_monthly_program_cost(params, new_customers_per_month):
|
||||
"""Total cost of running the program for one month."""
|
||||
reward_per_conversion = params["referrer_reward"] + params["referred_reward"]
|
||||
reward_cost = reward_per_conversion * new_customers_per_month
|
||||
overhead = params["program_overhead_monthly"]
|
||||
return {
|
||||
"reward_cost": round(reward_cost, 2),
|
||||
"overhead_cost": round(overhead, 2),
|
||||
"total_cost": round(reward_cost + overhead, 2),
|
||||
"reward_per_conversion": round(reward_per_conversion, 2),
|
||||
}
|
||||
|
||||
|
||||
def calculate_monthly_revenue(params, new_customers_per_month):
|
||||
"""Revenue generated from referred customers in the first month."""
|
||||
# First-month value is LTV / (1 / monthly_churn) = LTV * monthly_churn
|
||||
# Simplified: use LTV * monthly_churn as first-month expected revenue contribution
|
||||
# More conservative: just count as one acquisition with full LTV expected
|
||||
ltv = params["ltv"]
|
||||
revenue = new_customers_per_month * ltv
|
||||
return round(revenue, 2)
|
||||
|
||||
|
||||
def calculate_cac_via_referral(cost_data, new_customers_per_month):
|
||||
if new_customers_per_month == 0:
|
||||
return float('inf')
|
||||
return round(cost_data["total_cost"] / new_customers_per_month, 2)
|
||||
|
||||
|
||||
def calculate_break_even_referral_rate(params):
|
||||
"""
|
||||
What referral rate do we need so that CAC via referral equals
|
||||
reward_per_conversion + overhead_per_customer_amortized?
|
||||
|
||||
We want: total_cost / new_customers = cac_target
|
||||
Solving for referral_rate where cac_target = 50% of paid CAC (our target)
|
||||
"""
|
||||
target_cac = params["cac"] * 0.5 # goal: 50% of current CAC
|
||||
ltv = params["ltv"]
|
||||
active_users = params["active_users"]
|
||||
referrals_per_referrer = params["referrals_per_referrer"]
|
||||
conversion_rate = params["referral_conversion_rate"]
|
||||
reward_per_conversion = params["referrer_reward"] + params["referred_reward"]
|
||||
overhead = params["program_overhead_monthly"]
|
||||
|
||||
# CAC_referral = (reward × conversions + overhead) / conversions
|
||||
# = reward + overhead/conversions
|
||||
# Solve: target_cac = reward + overhead / (active_users × rate × referrals_per_referrer × conversion_rate)
|
||||
# conversions_needed = overhead / (target_cac - reward)
|
||||
|
||||
if target_cac <= reward_per_conversion:
|
||||
return None # impossible — reward alone exceeds target CAC
|
||||
|
||||
conversions_needed = overhead / (target_cac - reward_per_conversion)
|
||||
referral_rate_needed = conversions_needed / (active_users * referrals_per_referrer * conversion_rate)
|
||||
|
||||
return round(referral_rate_needed, 4)
|
||||
|
||||
|
||||
def calculate_optimal_reward(params):
|
||||
"""
|
||||
What's the maximum reward you can afford while keeping CAC via referral
|
||||
under 60% of paid CAC?
|
||||
|
||||
max_total_reward = 0.60 × paid_CAC (using conversion-amortized overhead)
|
||||
"""
|
||||
target_cac = params["cac"] * 0.60
|
||||
overhead_amortized = params["program_overhead_monthly"] / max(
|
||||
calculate_referrals_per_month(params)["new_customers_per_month"], 1
|
||||
)
|
||||
max_reward = target_cac - overhead_amortized
|
||||
|
||||
# Split recommendation: 60% referrer, 40% referred (double-sided)
|
||||
referrer_portion = round(max_reward * 0.60, 2)
|
||||
referred_portion = round(max_reward * 0.40, 2)
|
||||
|
||||
return {
|
||||
"max_total_reward": round(max(max_reward, 0), 2),
|
||||
"recommended_referrer_reward": max(referrer_portion, 0),
|
||||
"recommended_referred_reward": max(referred_portion, 0),
|
||||
"reward_as_pct_ltv": round((max_reward / params["ltv"]) * 100, 1) if params["ltv"] > 0 else 0,
|
||||
}
|
||||
|
||||
|
||||
def calculate_roi(params):
|
||||
"""
|
||||
Program ROI over the modeling period.
|
||||
ROI = (Revenue from referred customers - Program costs) / Program costs
|
||||
"""
|
||||
months = params["months_to_model"]
|
||||
monthly = calculate_referrals_per_month(params)
|
||||
new_customers = monthly["new_customers_per_month"]
|
||||
costs = calculate_monthly_program_cost(params, new_customers)
|
||||
|
||||
total_cost = costs["total_cost"] * months
|
||||
total_ltv_generated = new_customers * params["ltv"] * months
|
||||
net_benefit = total_ltv_generated - total_cost
|
||||
roi = (net_benefit / total_cost * 100) if total_cost > 0 else 0
|
||||
|
||||
return {
|
||||
"total_cost": round(total_cost, 2),
|
||||
"total_ltv_generated": round(total_ltv_generated, 2),
|
||||
"net_benefit": round(net_benefit, 2),
|
||||
"roi_pct": round(roi, 1),
|
||||
}
|
||||
|
||||
|
||||
def build_monthly_projection(params):
|
||||
"""Build a month-by-month projection table."""
|
||||
months = params["months_to_model"]
|
||||
monthly = calculate_referrals_per_month(params)
|
||||
new_per_month = monthly["new_customers_per_month"]
|
||||
costs = calculate_monthly_program_cost(params, new_per_month)
|
||||
ltv = params["ltv"]
|
||||
|
||||
rows = []
|
||||
cumulative_customers = 0
|
||||
cumulative_cost = 0
|
||||
cumulative_revenue = 0
|
||||
|
||||
for m in range(1, months + 1):
|
||||
cumulative_customers += new_per_month
|
||||
month_cost = costs["total_cost"]
|
||||
month_revenue = new_per_month * ltv
|
||||
cumulative_cost += month_cost
|
||||
cumulative_revenue += month_revenue
|
||||
cumulative_net = cumulative_revenue - cumulative_cost
|
||||
|
||||
rows.append({
|
||||
"month": m,
|
||||
"new_customers": round(new_per_month, 1),
|
||||
"cumulative_customers": round(cumulative_customers, 1),
|
||||
"monthly_cost": round(month_cost, 2),
|
||||
"cumulative_cost": round(cumulative_cost, 2),
|
||||
"monthly_ltv": round(month_revenue, 2),
|
||||
"cumulative_net": round(cumulative_net, 2),
|
||||
})
|
||||
|
||||
return rows
|
||||
|
||||
|
||||
def find_break_even_month(projection):
|
||||
for row in projection:
|
||||
if row["cumulative_net"] >= 0:
|
||||
return row["month"]
|
||||
return None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Formatting
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def format_currency(value):
|
||||
return f"${value:,.2f}"
|
||||
|
||||
|
||||
def format_pct(value):
|
||||
return f"{value:.1f}%"
|
||||
|
||||
|
||||
def print_report(params, results):
|
||||
monthly = results["monthly_referrals"]
|
||||
costs = results["monthly_costs"]
|
||||
cac = results["cac_via_referral"]
|
||||
roi = results["roi"]
|
||||
break_even_rate = results["break_even_referral_rate"]
|
||||
optimal_reward = results["optimal_reward"]
|
||||
projection = results["monthly_projection"]
|
||||
break_even_month = results["break_even_month"]
|
||||
|
||||
paid_cac = params["cac"]
|
||||
ltv = params["ltv"]
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("REFERRAL PROGRAM ROI CALCULATOR")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n📊 INPUT PARAMETERS")
|
||||
print(f" LTV per customer: {format_currency(ltv)}")
|
||||
print(f" Current paid CAC: {format_currency(paid_cac)}")
|
||||
print(f" Active users: {params['active_users']:,}")
|
||||
print(f" Referral rate (monthly): {format_pct(params['referral_rate'] * 100)}")
|
||||
print(f" Referrals per referrer: {params['referrals_per_referrer']}")
|
||||
print(f" Referral conversion rate: {format_pct(params['referral_conversion_rate'] * 100)}")
|
||||
print(f" Referrer reward: {format_currency(params['referrer_reward'])}")
|
||||
print(f" Referred user reward: {format_currency(params['referred_reward'])}")
|
||||
print(f" Program overhead/month: {format_currency(params['program_overhead_monthly'])}")
|
||||
|
||||
print("\n📈 MONTHLY PERFORMANCE (STEADY STATE)")
|
||||
print(f" Active referrers/month: {monthly['active_referrers']}")
|
||||
print(f" Referrals sent/month: {monthly['referrals_sent']}")
|
||||
print(f" New customers/month: {monthly['new_customers_per_month']}")
|
||||
print(f" Monthly program cost: {format_currency(costs['total_cost'])}")
|
||||
print(f" ↳ Reward cost: {format_currency(costs['reward_cost'])}")
|
||||
print(f" ↳ Overhead: {format_currency(costs['overhead_cost'])}")
|
||||
print(f" CAC via referral: {format_currency(cac)}")
|
||||
print(f" Paid CAC: {format_currency(paid_cac)}")
|
||||
savings_pct = ((paid_cac - cac) / paid_cac * 100) if paid_cac > 0 else 0
|
||||
savings_label = f"{savings_pct:.0f}% cheaper than paid" if cac < paid_cac else "⚠️ More expensive than paid"
|
||||
print(f" CAC comparison: {savings_label}")
|
||||
|
||||
print(f"\n💰 ROI OVER {params['months_to_model']} MONTHS")
|
||||
print(f" Total program cost: {format_currency(roi['total_cost'])}")
|
||||
print(f" Total LTV generated: {format_currency(roi['total_ltv_generated'])}")
|
||||
print(f" Net benefit: {format_currency(roi['net_benefit'])}")
|
||||
print(f" Program ROI: {format_pct(roi['roi_pct'])}")
|
||||
|
||||
if break_even_month:
|
||||
print(f" Break-even: Month {break_even_month}")
|
||||
else:
|
||||
print(f" Break-even: Not reached in {params['months_to_model']} months")
|
||||
|
||||
print("\n🎯 OPTIMIZATION INSIGHTS")
|
||||
if break_even_rate:
|
||||
current_rate = params["referral_rate"]
|
||||
rate_gap = break_even_rate - current_rate
|
||||
if rate_gap > 0:
|
||||
print(f" Break-even referral rate: {format_pct(break_even_rate * 100)} "
|
||||
f"(you're at {format_pct(current_rate * 100)} — need +{format_pct(rate_gap * 100)})")
|
||||
else:
|
||||
print(f" Break-even referral rate: {format_pct(break_even_rate * 100)} ✅ Already above break-even")
|
||||
else:
|
||||
print(f" Break-even referral rate: ⚠️ Reward alone exceeds target CAC — reduce reward or increase LTV")
|
||||
|
||||
print(f"\n Optimal reward sizing (to keep CAC at ≤60% of paid CAC):")
|
||||
print(f" Max total reward/referral: {format_currency(optimal_reward['max_total_reward'])}")
|
||||
print(f" Recommended referrer: {format_currency(optimal_reward['recommended_referrer_reward'])}")
|
||||
print(f" Recommended referred user: {format_currency(optimal_reward['recommended_referred_reward'])}")
|
||||
print(f" Reward as % of LTV: {format_pct(optimal_reward['reward_as_pct_ltv'])}")
|
||||
|
||||
current_total_reward = params["referrer_reward"] + params["referred_reward"]
|
||||
if current_total_reward > optimal_reward["max_total_reward"] and optimal_reward["max_total_reward"] > 0:
|
||||
print(f" ⚠️ Your current reward ({format_currency(current_total_reward)}) "
|
||||
f"exceeds optimal ({format_currency(optimal_reward['max_total_reward'])})")
|
||||
elif optimal_reward["max_total_reward"] > 0:
|
||||
print(f" ✅ Your current reward ({format_currency(current_total_reward)}) is within optimal range")
|
||||
|
||||
print(f"\n📅 MONTHLY PROJECTION (first {min(6, len(projection))} months)")
|
||||
print(f" {'Month':>5} {'New Cust':>9} {'Cumul Cust':>11} {'Monthly Cost':>13} {'Cumul Net':>11}")
|
||||
print(f" {'-'*5} {'-'*9} {'-'*11} {'-'*13} {'-'*11}")
|
||||
for row in projection[:6]:
|
||||
net_str = format_currency(row["cumulative_net"])
|
||||
if row["cumulative_net"] < 0:
|
||||
net_str = f"({format_currency(abs(row['cumulative_net']))})"
|
||||
print(f" {row['month']:>5} {row['new_customers']:>9.1f} {row['cumulative_customers']:>11.1f} "
|
||||
f"{format_currency(row['monthly_cost']):>13} {net_str:>11}")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Default parameters + sample
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
DEFAULT_PARAMS = {
|
||||
"ltv": 1200,
|
||||
"cac": 350,
|
||||
"active_users": 800,
|
||||
"referral_rate": 0.06,
|
||||
"referrals_per_referrer": 2.0,
|
||||
"referral_conversion_rate": 0.20,
|
||||
"referrer_reward": 50,
|
||||
"referred_reward": 30,
|
||||
"program_overhead_monthly": 200,
|
||||
"churn_rate_monthly": 0.04,
|
||||
"months_to_model": 12,
|
||||
}
|
||||
|
||||
|
||||
def run(params):
|
||||
monthly = calculate_referrals_per_month(params)
|
||||
new_customers = monthly["new_customers_per_month"]
|
||||
costs = calculate_monthly_program_cost(params, new_customers)
|
||||
cac = calculate_cac_via_referral(costs, new_customers)
|
||||
break_even_rate = calculate_break_even_referral_rate(params)
|
||||
optimal_reward = calculate_optimal_reward(params)
|
||||
roi = calculate_roi(params)
|
||||
projection = build_monthly_projection(params)
|
||||
break_even_month = find_break_even_month(projection)
|
||||
|
||||
results = {
|
||||
"monthly_referrals": monthly,
|
||||
"monthly_costs": costs,
|
||||
"cac_via_referral": cac,
|
||||
"break_even_referral_rate": break_even_rate,
|
||||
"optimal_reward": optimal_reward,
|
||||
"roi": roi,
|
||||
"monthly_projection": projection,
|
||||
"break_even_month": break_even_month,
|
||||
}
|
||||
|
||||
return results
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def main():
|
||||
params = None
|
||||
|
||||
if len(sys.argv) > 1:
|
||||
try:
|
||||
with open(sys.argv[1]) as f:
|
||||
params = json.load(f)
|
||||
except Exception as e:
|
||||
print(f"Error reading file: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
elif not sys.stdin.isatty():
|
||||
raw = sys.stdin.read().strip()
|
||||
if raw:
|
||||
try:
|
||||
params = json.loads(raw)
|
||||
except Exception as e:
|
||||
print(f"Error reading stdin: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
else:
|
||||
print("No input provided — running with sample parameters.\n")
|
||||
params = DEFAULT_PARAMS
|
||||
else:
|
||||
print("No input provided — running with sample parameters.\n")
|
||||
params = DEFAULT_PARAMS
|
||||
|
||||
# Fill in defaults for any missing keys
|
||||
for k, v in DEFAULT_PARAMS.items():
|
||||
params.setdefault(k, v)
|
||||
|
||||
results = run(params)
|
||||
print_report(params, results)
|
||||
|
||||
# JSON output
|
||||
json_output = {
|
||||
"inputs": params,
|
||||
"results": {
|
||||
"monthly_new_customers": results["monthly_referrals"]["new_customers_per_month"],
|
||||
"cac_via_referral": results["cac_via_referral"],
|
||||
"program_roi_pct": results["roi"]["roi_pct"],
|
||||
"break_even_month": results["break_even_month"],
|
||||
"break_even_referral_rate": results["break_even_referral_rate"],
|
||||
"optimal_total_reward": results["optimal_reward"]["max_total_reward"],
|
||||
"net_benefit_12mo": results["roi"]["net_benefit"],
|
||||
}
|
||||
}
|
||||
|
||||
print("\n--- JSON Output ---")
|
||||
print(json.dumps(json_output, indent=2))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user