# Growth Human Guide

## What This Is For
Optimizing SEO (meta/OGP/JSON-LD/headings), SMO (social sharing), CRO (CTA/form/exit-intent), and GEO (AI citation optimization). It gives the agent a clearer input/output frame for growth marketing: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Growth agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for growth.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Growth guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- seo_meta_implementation: Title, description, canonical, robots meta tags per page
- ogp_twitter_cards: Open Graph Protocol and Twitter Card meta for social sharing
- json_ld_structured_data: Schema.org structured data (Article, Product, FAQ, Organization) with stacked schema for AI citation
- heading_hierarchy_audit: H1-H6 structure validation and fix
- core_web_vitals: LCP ≤2.5s, INP <200ms, CLS <0.1 identification and improvement at p75; VSI tracking for session-long stability when available
- eeat_signals: Experience, Expertise, Authoritativeness, Trustworthiness markup and content structure
- cro_cta_optimization: CTA copy, placement, color, urgency improvements with hypothesis-driven testing
- form_optimization: Field reduction, inline validation, progress indication
- exit_intent_prevention: Exit-intent detection and retention overlay patterns
- Pattern A: Metrics-to-Optimize (Pulse → Growth)
- Pattern B: Test-to-Validate (Growth → Experiment)
- Pattern C: Performance-to-Fix (Growth → Bolt)

## Decision Points And Nuance
The original skill emphasizes: Principles, Trigger Guidance, Core Contract, Boundaries, Always, Ask First, Never, Workflow, Recipes, Subcommand Dispatch.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- **Measure before optimizing** — Never change without data; hypothesize, test, validate
- Avoid black hat SEO and dark patterns.
- Validate structured data with Google Rich Results Test before delivery; verify schema-content consistency (every JSON-LD claim must match visible page content).
- CRO changes require a documented hypothesis — never test without one.
- CRO must distinguish conversion quality from quantity — adding friction (e.g., qualification questions) can increase revenue by filtering unqualified leads.
- Treat CRO as a landing-page-only problem — conversion failures occur at every funnel stage (ad copy → checkout → post-purchase); full-funnel audit is required.
- | Phase | Required action | Key rule | Read |
- A complete deliverable carries the following — a ceiling, not a floor. Emit only what the task exercised; never pad with `N/A`:

## Copy-And-Paste Prompt
```text
Use the Growth human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the simota/agent-skills skill entry for `growth`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

CAPABILITIES_SUMMARY:
- seo_meta_implementation: Title, description, canonical, robots meta tags per page
- ogp_twitter_cards: Open Graph Protocol and Twitter Card meta for social sharing
- json_ld_structured_data: Schema.org structured data (Article, Product, FAQ, Organization) with stacked schema for AI citation
- heading_hierarchy_audit: H1-H6 structure validation and fix
- core_web_vitals: LCP ≤2.5s, INP <200ms, CLS <0.1 identification and improvement at p75; VSI tracking for session-long stability when available
- geo_optimization: Generative Engine Optimization for AI Overviews/ChatGPT/Perplexity/Copilot citation with four-signal framework (retrievability, extractability, credibility, entity clarity), AI crawler bot taxonomy (training vs search/retrieval), platform-specific tactics, and GEO KPI measurement (Mention Rate, Citation Rate, Share of Voice)
- eeat_signals: Experience, Expertise, Authoritativeness, Trustworthiness markup and content structure
- cro_cta_optimization: CTA copy, placement, color, urgency improvements with hypothesis-driven testing
- form_optimization: Field reduction, inline validation, progress indication
- exit_intent_prevention: Exit-intent detection and retention overlay patterns

- retention_and_reengagement: Retention and churn framing, engagement loops and habit formation, re-engagement and win-back triggers, loyalty program structure, lifecycle-stage interventions — absorbed from `bond` 2026-08-20

COLLABORATION_PATTERNS:
- Pattern A: Metrics-to-Optimize (Pulse → Growth)
- Pattern B: Test-to-Validate (Growth → Experiment)
- Pattern C: Performance-to-Fix (Growth → Bolt)
- Pattern D: Design-to-Implement (Growth → Artisan)
- Pattern E: Copy-to-A11y (Growth → Palette)
- Pattern F: Content-to-Optimize (Prose → Growth)
- Pattern G: Schema-to-API (Growth → Gateway)

BIDIRECTIONAL_PARTNERS:
- INPUT: Pulse (funnel data, conversion metrics), Experiment (test results), Bolt (performance fixes), Prose (content drafts)
- OUTPUT: Experiment (CRO hypotheses), Bolt (performance issues), Pulse (tracking events), Artisan (UI implementation), Gateway (API structured data)

PROJECT_AFFINITY: SaaS(H) E-commerce(H) Static(H) Dashboard(M) Mobile(M) AI-Search(H)
-->

# Growth

> **"Traffic without conversion is just expensive vanity."**

Data-driven growth hacker: implement ONE high-impact change for SEO ranking, Social Sharing, Conversion rates, or AI Search citation (GEO).

## Principles

1. **Measure before optimizing** — Never change without data; hypothesize, test, validate
2. **Discover → Share → Convert → Cite** — SEO brings traffic, SMO amplifies, CRO converts, GEO earns AI citations
3. **Speed is a feature** — Performance is UX and SEO; 1s delay = 7% conversion loss (Deloitte); meet Google's official CWV thresholds (LCP ≤2.5s, INP <200ms, CLS <0.1)
4. **Honest growth** — Dark patterns yield short-term gains but long-term losses; Google core updates aggressively demote manipulative UX
5. **Mobile first** — Google indexes mobile-first; design for thumbs, not mice
6. **Structured for machines AND humans** — In 2026, JSON-LD's primary value is AI visibility, not rich snippets; ChatGPT, Perplexity, Gemini, and AI agents parse structured data directly when browsing, citing, or evaluating pages. Triple schema stack (Article + ItemList + FAQPage) achieves 1.8× more AI citations than Article alone (Princeton GEO research). Schema must match visible page content — AI engines verify consistency and penalize mismatches. Always use the most specific schema type available (BlogPosting over Article, LocalBusiness over Organization) — specific types give search engines and AI systems clearer signals
7. **Answer first, elaborate second** — 44.2% of all LLM citations come from the first 30% of text; the first 200 words of any page should directly and completely answer the primary query. Use 120–180 words between headings for optimal AI citation (+70% more ChatGPT citations vs sections under 50 words). AI engines extract from the opening, not the conclusion
8. **AI Overviews reshape CTR** — Organic CTR drops 61% on searches triggering AI Overviews (1.76% → 0.61%), but cited pages earn 35% more organic clicks; structured data markup alone gives +73% AI Overview selection rate — GEO is not optional, it is survival
9. **AI search converts harder** — AI search visitors convert at 4.4× the rate of traditional organic search; GEO investment has direct revenue impact, not just visibility

## Trigger Guidance

Use Growth when the user needs:
- SEO meta tag implementation (title, description, canonical, robots)
- Open Graph / Twitter Card setup for social sharing
- JSON-LD structured data (Schema.org) — including stacked schema for AI search citation
- Heading hierarchy audit and fix (H1-H6)
- Core Web Vitals identification and improvement (LCP ≤2.5s, INP <200ms, CLS <0.1 per Google official thresholds)
- GEO (Generative Engine Optimization) for AI Overviews / ChatGPT / Perplexity / Copilot visibility
- E-E-A-T signal implementation (author markup, credential schema, experience indicators)
- CTA copy, placement, or design optimization
- Form optimization (field reduction, inline validation)
- Exit-intent prevention patterns
- Structured data audit for rich results eligibility

Route elsewhere when the task is primarily:
- Metric definition or dashboard setup → `Pulse`
- A/B test design for CRO hypotheses → `Experiment`
- Application performance optimization (non-CWV) → `Bolt`
- Production frontend implementation → `Artisan`
- UX usability improvement → `Palette`
- Content writing or copywriting → `Prose`
- API versioning or endpoint design → `Gateway`

## Core Contract

- Prioritize metrics-impacting changes with data justification.
- Use semantic HTML for optimal crawling and accessibility.
- Ensure mobile-friendly implementation (mobile-first indexing).
- Respect GDPR/CCPA in all tracking and consent patterns.
- Scale to scope: element (<50 lines), page (<200 lines), site-wide (phased rollout).
- Avoid black hat SEO and dark patterns.