* 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>
9.4 KiB
Content Patterns for AI Citability
Ready-to-use block templates for each content pattern that AI search engines reliably extract and cite. Copy, adapt, and embed in your pages.
Why Patterns Matter
AI systems don't read pages the way humans do. They scan for extractable chunks — self-contained passages that can be pulled out and quoted without losing meaning.
The patterns below are structured to be self-contained by design. If the AI pulls paragraph 3 without paragraph 2, the citation should still make sense.
Pattern 1: Definition Block
Used for: "What is X" queries — the most common AI Overview trigger.
Requirements:
- First sentence: direct definition
- Second sentence: why it matters or how it works
- Third sentence (optional): example or context
- Placed in first 300 words of the page
Template:
**[Term]** is [precise definition — what it is, what it does, who uses it].
[One sentence on why it matters or what problem it solves].
[Optional: one sentence example — "For example, a SaaS company might use X to..."].
Example:
**Churn rate** is the percentage of customers who cancel or stop using a service within a given period, typically measured monthly or annually. It directly impacts recurring revenue — a 5% monthly churn means losing over half your customer base each year. For subscription SaaS, a healthy monthly churn rate is typically below 2%.
Tips:
- Bold the term on its first use
- Don't start with "In the world of..." or "When it comes to..."
- The definition should work even if the reader knows nothing about the topic
Pattern 2: Numbered Steps (How-To)
Used for: "How to X" and "How do I X" queries.
Requirements:
- Numbered list (not bulleted)
- Each step starts with an action verb
- Each step is self-contained (can be cited alone)
- 5-10 steps maximum
- Pair with HowTo schema markup
Template:
## How to [Task]
1. **[Verb phrase]** — [1-2 sentence explanation of this specific step]
2. **[Verb phrase]** — [1-2 sentence explanation]
3. **[Verb phrase]** — [1-2 sentence explanation]
4. **[Verb phrase]** — [1-2 sentence explanation]
5. **[Verb phrase]** — [1-2 sentence explanation]
Example:
## How to Reduce SaaS Churn
1. **Define your activation event** — Identify the specific action that signals a user has experienced core product value. For Slack, it's 2,000 messages sent. For Dropbox, it's saving the first file.
2. **Instrument the activation funnel** — Add event tracking from signup to activation. Find the step where most users drop off — that's your highest-leverage point.
3. **Build a customer health score** — Combine login frequency, feature adoption, and support ticket volume into a single score. Customers below 40 get proactive outreach.
4. **Segment churn by cohort** — Not all churn looks the same. Compare churn rates by acquisition channel, onboarding path, and company size to find patterns.
5. **Interview churned customers** — The customers who left quietly are more valuable than the ones who complained. Call 10 churned accounts per month and ask what they were trying to accomplish.
Schema markup (JSON-LD):
{
"@context": "https://schema.org",
"@type": "HowTo",
"name": "How to [Task]",
"step": [
{"@type": "HowToStep", "name": "Step 1 name", "text": "Step 1 explanation"},
{"@type": "HowToStep", "name": "Step 2 name", "text": "Step 2 explanation"}
]
}
Pattern 3: Comparison Table
Used for: "X vs Y" and "best X for Y" queries.
Requirements:
- Header row with category names
- First column: feature or criterion
- Remaining columns: the things being compared
- Keep it focused — 5-10 rows maximum
- Don't try to cover everything; cover what matters most
Template:
| Feature | [Option A] | [Option B] | [Option C] |
|---|---|---|---|
| [Criterion 1] | [Value] | [Value] | [Value] |
| [Criterion 2] | [Value] | [Value] | [Value] |
| [Criterion 3] | [Value] | [Value] | [Value] |
| Best for | [Audience A] | [Audience B] | [Audience C] |
| Pricing | [Range] | [Range] | [Range] |
Tips:
- Put the most important criteria first
- Use simple values — "Yes / No / Partial" beats long prose in cells
- Include a "Best for" row — AI systems use this for recommendation queries
- Add a sentence below the table summarizing the verdict: "X is best for teams that need A; Y is better when B matters more."
Pattern 4: FAQ Block
Used for: Question-style queries, People Also Ask queries, voice search.
Requirements:
- Question phrased exactly as someone would ask it (natural language)
- Answer is complete in 2-4 sentences (no "read more in section 3")
- 5-10 FAQs per block
- Pair with FAQPage schema markup
Template:
## Frequently Asked Questions
**What is [X]?**
[2-4 sentence complete answer]
**How does [X] work?**
[2-4 sentence complete answer]
**What's the difference between [X] and [Y]?**
[2-4 sentence complete answer]
**How much does [X] cost?**
[2-4 sentence complete answer]
**Is [X] right for [audience]?**
[2-4 sentence complete answer]
Schema markup (JSON-LD):
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "What is [X]?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Complete answer text here"
}
}
]
}
Tips:
- Write questions the way users actually type or speak them — use Google's "People Also Ask" as a source
- Answers should be complete without needing context from anywhere else on the page
- Don't start answers with "Great question" or "That's a common question" — just answer
Pattern 5: Statistic with Attribution
Used for: Data queries, "how many" queries, research-backed claims.
Requirements:
- Named source (not "a study" — the actual organization name)
- Year of the data
- Specific number (not "many" or "most")
- Context (what the number means)
Template:
According to [Organization Name]'s [Report Name] ([Year]), [specific statistic with units]. [One sentence on what this means or why it matters].
Example:
According to the Baymard Institute's 2024 UX benchmarking study, 69.8% of online shopping carts are abandoned before purchase. For a $1M/month ecommerce store, recovering just 5% of abandoned carts represents $35,000 in monthly revenue.
Tips:
- Link to the original source (AI systems and readers both benefit)
- If data is from your own research, say so: "In our 2025 survey of 500 SaaS founders..."
- Proprietary data is the highest-value citation target — AI systems actively seek original research
Pattern 6: Expert Quote Block
Used for: Authority building, "what do experts say" queries.
Requirements:
- Full name of the person quoted
- Their title and organization
- A quote that's substantive (not a generic endorsement)
- Brief context sentence before the quote
Template:
[Context sentence explaining why this person's view matters.]
"[Direct quote — specific, substantive, something only they would say]," says [Full Name], [Title] at [Organization].
Example:
Patrick Campbell, founder of ProfitWell (acquired by Paddle), studied pricing data from over 30,000 SaaS companies before reaching a counterintuitive conclusion about churn.
"Most churn that looks like pricing dissatisfaction is actually failed onboarding," says Campbell. "The customer never saw the value that justified the price. That's a different problem than being too expensive."
Tips:
- Don't use generic quotes ("innovation is key to success") — they add nothing
- Quotes should contain a specific claim, data point, or perspective
- If quoting your own team: "[Name], [Title] at [Company Name]" is still valid
- Live quotes (from interviews or primary research) outperform secondary quotes from other articles
Pattern 7: Quick-Scan Summary Box
Used for: Queries where users want the TL;DR before committing to the full article.
Requirements:
- Placed near the top of the article (after the intro)
- 3-7 key takeaways
- Each bullet stands alone — no context required
- Labeled clearly ("Key Takeaways" or "Quick Summary")
Template:
**Key Takeaways**
- [Specific, complete takeaway — could be read as a tweet]
- [Specific, complete takeaway]
- [Specific, complete takeaway]
- [Specific, complete takeaway]
- [Specific, complete takeaway]
Tips:
- This is often the block AI systems extract for "summary" type queries
- Make each bullet specific: "Monthly churn below 2% is considered healthy for most SaaS" beats "Churn should be low"
- Don't repeat the article intro verbatim — these should be the most actionable insights
Combining Patterns
The most citable pages combine multiple patterns throughout the piece:
Recommended page structure for maximum AI extractability:
- Definition block (first 300 words)
- Quick summary box (right after intro)
- Body sections with numbered steps or subsections
- Data points with full attribution throughout
- Comparison table (if competitive topic)
- FAQ block (before conclusion)
- Expert quote (to add authority)
A page with all 7 patterns has significantly more extractable surface area than a page with prose only. The AI has more options to pull from and a higher probability of finding something that perfectly matches the query.