* 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>
277 lines
9.4 KiB
Markdown
277 lines
9.4 KiB
Markdown
# Content Patterns for AI Citability
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Ready-to-use block templates for each content pattern that AI search engines reliably extract and cite. Copy, adapt, and embed in your pages.
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---
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## Why Patterns Matter
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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.
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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.
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---
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## Pattern 1: Definition Block
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**Used for:** "What is X" queries — the most common AI Overview trigger.
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**Requirements:**
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- First sentence: direct definition
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- Second sentence: why it matters or how it works
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- Third sentence (optional): example or context
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- Placed in first 300 words of the page
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**Template:**
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```markdown
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**[Term]** is [precise definition — what it is, what it does, who uses it].
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[One sentence on why it matters or what problem it solves].
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[Optional: one sentence example — "For example, a SaaS company might use X to..."].
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```
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**Example:**
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```markdown
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**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%.
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```
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**Tips:**
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- Bold the term on its first use
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- Don't start with "In the world of..." or "When it comes to..."
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- The definition should work even if the reader knows nothing about the topic
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---
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## Pattern 2: Numbered Steps (How-To)
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**Used for:** "How to X" and "How do I X" queries.
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**Requirements:**
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- Numbered list (not bulleted)
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- Each step starts with an action verb
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- Each step is self-contained (can be cited alone)
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- 5-10 steps maximum
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- Pair with HowTo schema markup
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**Template:**
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```markdown
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## How to [Task]
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1. **[Verb phrase]** — [1-2 sentence explanation of this specific step]
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2. **[Verb phrase]** — [1-2 sentence explanation]
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3. **[Verb phrase]** — [1-2 sentence explanation]
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4. **[Verb phrase]** — [1-2 sentence explanation]
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5. **[Verb phrase]** — [1-2 sentence explanation]
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```
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**Example:**
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```markdown
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## How to Reduce SaaS Churn
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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.
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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.
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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.
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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.
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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.
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```
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**Schema markup (JSON-LD):**
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```json
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{
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"@context": "https://schema.org",
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"@type": "HowTo",
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"name": "How to [Task]",
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"step": [
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{"@type": "HowToStep", "name": "Step 1 name", "text": "Step 1 explanation"},
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{"@type": "HowToStep", "name": "Step 2 name", "text": "Step 2 explanation"}
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]
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}
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```
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---
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## Pattern 3: Comparison Table
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**Used for:** "X vs Y" and "best X for Y" queries.
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**Requirements:**
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- Header row with category names
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- First column: feature or criterion
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- Remaining columns: the things being compared
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- Keep it focused — 5-10 rows maximum
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- Don't try to cover everything; cover what matters most
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**Template:**
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```markdown
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| Feature | [Option A] | [Option B] | [Option C] |
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|---|---|---|---|
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| [Criterion 1] | [Value] | [Value] | [Value] |
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| [Criterion 2] | [Value] | [Value] | [Value] |
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| [Criterion 3] | [Value] | [Value] | [Value] |
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| Best for | [Audience A] | [Audience B] | [Audience C] |
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| Pricing | [Range] | [Range] | [Range] |
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```
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**Tips:**
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- Put the most important criteria first
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- Use simple values — "Yes / No / Partial" beats long prose in cells
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- Include a "Best for" row — AI systems use this for recommendation queries
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- Add a sentence below the table summarizing the verdict: "X is best for teams that need A; Y is better when B matters more."
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---
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## Pattern 4: FAQ Block
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**Used for:** Question-style queries, People Also Ask queries, voice search.
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**Requirements:**
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- Question phrased exactly as someone would ask it (natural language)
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- Answer is complete in 2-4 sentences (no "read more in section 3")
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- 5-10 FAQs per block
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- Pair with FAQPage schema markup
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**Template:**
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```markdown
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## Frequently Asked Questions
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**What is [X]?**
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[2-4 sentence complete answer]
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**How does [X] work?**
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[2-4 sentence complete answer]
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**What's the difference between [X] and [Y]?**
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[2-4 sentence complete answer]
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**How much does [X] cost?**
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[2-4 sentence complete answer]
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**Is [X] right for [audience]?**
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[2-4 sentence complete answer]
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```
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**Schema markup (JSON-LD):**
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```json
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{
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"@context": "https://schema.org",
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"@type": "FAQPage",
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"mainEntity": [
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{
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"@type": "Question",
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"name": "What is [X]?",
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"acceptedAnswer": {
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"@type": "Answer",
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"text": "Complete answer text here"
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}
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}
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]
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}
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```
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**Tips:**
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- Write questions the way users actually type or speak them — use Google's "People Also Ask" as a source
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- Answers should be complete without needing context from anywhere else on the page
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- Don't start answers with "Great question" or "That's a common question" — just answer
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---
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## Pattern 5: Statistic with Attribution
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**Used for:** Data queries, "how many" queries, research-backed claims.
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**Requirements:**
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- Named source (not "a study" — the actual organization name)
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- Year of the data
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- Specific number (not "many" or "most")
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- Context (what the number means)
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**Template:**
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```markdown
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According to [Organization Name]'s [Report Name] ([Year]), [specific statistic with units]. [One sentence on what this means or why it matters].
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```
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**Example:**
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```markdown
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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.
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```
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**Tips:**
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- Link to the original source (AI systems and readers both benefit)
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- If data is from your own research, say so: "In our 2025 survey of 500 SaaS founders..."
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- Proprietary data is the highest-value citation target — AI systems actively seek original research
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---
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## Pattern 6: Expert Quote Block
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**Used for:** Authority building, "what do experts say" queries.
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**Requirements:**
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- Full name of the person quoted
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- Their title and organization
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- A quote that's substantive (not a generic endorsement)
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- Brief context sentence before the quote
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**Template:**
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```markdown
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[Context sentence explaining why this person's view matters.]
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"[Direct quote — specific, substantive, something only they would say]," says [Full Name], [Title] at [Organization].
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```
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**Example:**
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```markdown
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Patrick Campbell, founder of ProfitWell (acquired by Paddle), studied pricing data from over 30,000 SaaS companies before reaching a counterintuitive conclusion about churn.
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"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."
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```
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**Tips:**
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- Don't use generic quotes ("innovation is key to success") — they add nothing
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- Quotes should contain a specific claim, data point, or perspective
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- If quoting your own team: "[Name], [Title] at [Company Name]" is still valid
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- Live quotes (from interviews or primary research) outperform secondary quotes from other articles
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---
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## Pattern 7: Quick-Scan Summary Box
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**Used for:** Queries where users want the TL;DR before committing to the full article.
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**Requirements:**
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- Placed near the top of the article (after the intro)
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- 3-7 key takeaways
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- Each bullet stands alone — no context required
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- Labeled clearly ("Key Takeaways" or "Quick Summary")
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**Template:**
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```markdown
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**Key Takeaways**
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- [Specific, complete takeaway — could be read as a tweet]
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- [Specific, complete takeaway]
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- [Specific, complete takeaway]
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- [Specific, complete takeaway]
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- [Specific, complete takeaway]
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```
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**Tips:**
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- This is often the block AI systems extract for "summary" type queries
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- Make each bullet specific: "Monthly churn below 2% is considered healthy for most SaaS" beats "Churn should be low"
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- Don't repeat the article intro verbatim — these should be the most actionable insights
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---
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## Combining Patterns
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The most citable pages combine multiple patterns throughout the piece:
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**Recommended page structure for maximum AI extractability:**
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1. Definition block (first 300 words)
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2. Quick summary box (right after intro)
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3. Body sections with numbered steps or subsections
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4. Data points with full attribution throughout
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5. Comparison table (if competitive topic)
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6. FAQ block (before conclusion)
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7. Expert quote (to add authority)
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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.
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