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- Resolve conflicts: keep redesigned skills index, take dev's cs-aeo link fix, union of DOMAIN_SEO_CONTEXT entries in generate-docs.py - Regenerate catalog on the merged tree (dev's agent/command description updates, removed ai-seo/release-manager/command-guide, restructured universal-scraping-architect) - Update counters to post-merge truth from scripts/derive_counters.py: 345 skills, 78 plugins (14 bundles + 64 standalone), 570+ Python tools - Add redirects for upstream-removed pages (ai-seo -> aeo, release-manager -> changelog-generator, command-guide -> engineering index) - Add compliance-os bundle to bundle tables; rebuild 78-plugin table from marketplace.json - Teach the generator to rewrite repo-root-relative source links to GitHub URLs — mkdocs build --strict now passes with zero warnings https://claude.ai/code/session_015bYZ97nV4oRb3LbxCRFVcP
171 lines
6.2 KiB
Markdown
171 lines
6.2 KiB
Markdown
---
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title: "/cs-aeo — Slash Command for AI Coding Agents"
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description: "/cs:aeo — Answer Engine Optimization workflow. Audit content for E-E-A-T + structure signals that drive LLM citation (ChatGPT, Perplexity, Claude. Slash command for Claude Code, Codex CLI, Gemini CLI."
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---
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# /cs-aeo
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<div class="page-meta" markdown>
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<span class="meta-badge">:material-console: Slash Command</span>
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<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/commands/cs-aeo.md">Source</a></span>
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</div>
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**Command:** `/cs:aeo [action] [args]`
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The `cs-aeo` command is the **entry point for AEO workflows**: audit → optimize → publish → track citations.
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## Distinct From `/cs:seo-audit`
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These share a foundation (E-E-A-T) but optimize for different conversion events:
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- **`/cs:seo-audit`** — optimizes for ranking + click-through in Google/Bing search results
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- **`/cs:aeo`** (this command) — optimizes for being cited as authoritative source by LLMs
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They can run on the same content. The cs-aeo agent will surface this and recommend running both for high-leverage pages.
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## When To Run
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- Auditing existing content for AI-search readiness (E-E-A-T + structure signals)
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- Optimizing a page for LLM citation before publishing
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- Tracking which LLMs cite which pages over time (citation ledger)
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- Researching whether AEO investment is worth it for a given content piece
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- Benchmarking against competitor citation rates
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## When NOT To Run
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- Pure click-through SEO without AI-citation intent → use `/cs:seo-audit`
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- Brand-voice content with no factual claims (citations require facts)
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- Time-sensitive news (LLM training lag means citation comes months later)
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- Topics where LLMs already have strong training (e.g., elementary math)
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## Actions
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### `audit` — Score content for AEO readiness
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```bash
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/cs:aeo audit --input post.md --industry saas
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/cs:aeo audit --url https://example.com/blog/post --industry healthcare
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/cs:aeo audit --sample
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```
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Returns composite 0-100 with per-dimension breakdown (E-E-A-T + Structure) and top 5 fixes in priority order.
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### `optimize` — Generate AEO-improved variant
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```bash
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/cs:aeo optimize --input post.md --mode balanced --output post-aeo.md
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/cs:aeo optimize --input post.md --mode aggressive --industry finance
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```
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Three modes:
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- `conservative` — touch <10% of words (schema + corrections footer only)
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- `balanced` — touch <30% (citation markers + heading restructure + schema + footer)
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- `aggressive` — full restructure + fact-first lede + maximum citation density
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### `track` — Log a citation you observed in an LLM response
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```bash
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/cs:aeo track --url https://example.com/post --llm perplexity --query "what is AEO" --date 2026-05-17
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```
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Maintains a local ledger at `~/.aeo-data/citations.json`. No telemetry.
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### `report` — Aggregate citation report for a URL
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```bash
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/cs:aeo report --url https://example.com/post
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```
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Returns total citations, LLM coverage, velocity, top queries, verdict (EARLY / EMERGING / STRONG).
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### `export` — Emit citation ledger as CSV
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```bash
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/cs:aeo export --output citations.csv
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```
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For reporting to clients / stakeholders.
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## Minimal Intake (3 Questions)
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| Q | Asks | When |
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|---|---|---|
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| Q1 | What action — audit / optimize / track / report? | Always |
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| Q2 | Industry (saas / healthcare / finance / legal / ecommerce / b2b / media / education) | Always (calibrates thresholds) |
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| Q3 | For `optimize`: mode (conservative / balanced / aggressive)? | Only when action=optimize |
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Most invocations exit intake after Q2.
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## Workflow
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```bash
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# Phase 1: Audit
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python3 marketing-skill/skills/aeo/scripts/aeo_audit.py --input <file> --industry <industry>
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# → composite score 0-100 + top fixes
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# Phase 2: Optimize (if audit < industry threshold)
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python3 marketing-skill/skills/aeo/scripts/aeo_optimizer.py \
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--input <file> --mode <mode> --industry <industry> --output <file>-aeo.md
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# → optimized variant + changelog
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# Phase 3: Publish (manual step — review the optimized variant, then deploy)
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# Phase 4: Track (over 4-12 weeks)
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python3 marketing-skill/skills/aeo/scripts/citation_tracker.py \
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--action add --url <url> --llm <llm> --query <query> --date <YYYY-MM-DD>
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# → ledger updated
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# Phase 5: Report (monthly)
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python3 marketing-skill/skills/aeo/scripts/citation_tracker.py \
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--action report --url <url>
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# → per-URL citation report
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```
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## Industry-Specific Thresholds
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The auditor calibrates per-industry. YMYL ("Your Money or Your Life") topics use stricter thresholds:
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| Industry | Min Composite | Why |
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|---|---|---|
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| Healthcare | 85 | Direct health implications |
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| Finance | 85 | Real financial decisions |
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| Legal | 85 | Legal jeopardy if misapplied |
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| Education | 75 | Learning outcomes |
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| SaaS, B2B, Media | 70 | Business decisions, moderate stakes |
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| E-commerce | 65 | Product reviews, lower individual risk |
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Content for YMYL topics scoring below threshold is unlikely to be cited regardless of other signals — the cs-aeo agent will flag this and refuse aggressive optimization until the foundational dimensions improve.
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## Anti-Patterns Rejected
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- LLM-generated AEO content with no human review (RAG retrieval deprioritizes generic LLM output)
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- Fabricated credentials in author bylines (LLMs cross-reference via LinkedIn/Wikipedia)
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- Schema spam (false structured-data markup gets filtered)
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- Authority laundering (linking out doesn't confer authority)
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- Per-LLM optimization tunnel-vision (73% cross-LLM citation correlation — optimize for shared signals)
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- Optimizing AEO at expense of SEO (and vice versa) — they complement, don't substitute
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## Trigger Phrases
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- "AEO audit"
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- "optimize for ChatGPT / Perplexity / Claude / Gemini"
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- "get cited by [LLM]"
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- "LLM citation strategy"
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- "answer engine optimization"
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- "E-E-A-T audit"
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- "content for AI search"
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- "track AI citations"
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- "schema for AI"
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## Related
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- Agent: [`cs-aeo`](https://github.com/alirezarezvani/claude-skills/tree/main/agents/marketing/cs-aeo.md)
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- Skill: [`aeo`](https://github.com/alirezarezvani/claude-skills/tree/main/marketing-skill/skills/aeo/SKILL.md)
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- Companion: `/cs:seo-audit` (SEO + AEO often run together)
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- Source: ported from [`alirezarezvani/aeo-box`](https://github.com/alirezarezvani/aeo-box)
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---
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**Version:** 2.7.3
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**License:** MIT
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