Creator · AgriciDaniel
Last updated · Sep 2, 2026
AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation
Creator · AgriciDaniel
Last updated · Sep 2, 2026
AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation
Creator · AgriciDaniel
Last updated · Sep 2, 2026
AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation
Creator · AgriciDaniel
Last updated · Sep 2, 2026
AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation
Sandbox only
Install targets
Codex install prompt
Install the "blog-geo" agent skill from https://github.com/AgriciDaniel/claude-blog/tree/main/brain/.raw/sources/claude-blog-skill/skills/blog-geo. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation". After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"agricidaniel-blog-geo","task":"Install blog-geo","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add AgriciDaniel/claude-blog --skill blog-geo
Maintenance
fresh
8d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
2.0K
80/100 Quality · 82/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
2.0K GitHub stars
Repo activity
2.0K stars, 333 forks
Maintenance
8d since push
License
MIT
Install
npx skills add AgriciDaniel/claude-blog --skill blog-geo
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add AgriciDaniel/claude-blog --skill blog-geoDo not use when
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/agricidaniel-blog-geo/install
Agent should check
Copy prompt
Task: Use blog-geo in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/agricidaniel-blog-geo/install
Install command: npx skills add AgriciDaniel/claude-blog --skill blog-geo
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/agricidaniel-blog-geo/install
LLM text format
/api/skills/agricidaniel-blog-geo/install?format=text
Find alternatives
/api/skills/search?q=blog-geo&limit=3
Agent prompt
Use blog-geo for this task. Review https://www.openagentskill.com/api/skills/agricidaniel-blog-geo/install, then install with: npx skills add AgriciDaniel/claude-blog --skill blog-geoRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/agricidaniel-blog-geo
LLM text
/api/registry/manifest/agricidaniel-blog-geo?format=text
Install alias
/api/registry/install/agricidaniel-blog-geo
Recommend
/api/registry/recommend?task=Use%20blog-geo%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
RAG and knowledge
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS2.0K GitHub stars
Stars/forks activity
PASS2.0K stars, 333 forks; issue activity unavailable in current metadata
Recent maintenance
PASS8d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Collect structured data
I need my agent to scrape websites and extract structured data from pages.
Workflow fit
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Alternative shortlist
Similar skills that may fit this task.
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--- name: blog-geo description: > AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation". user-invokable: true argument-hint: "<file-path>" license: MIT ---
# Blog GEO: AI Citation Optimization Audit
Scores blog posts for AI citation readiness across ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, and Google AI Mode as one SEO workflow, not a separate discipline. Generates citation capsules and a 0-100 AI Citation Readiness score with platform-specific recommendations.
Google's 2026-05-15 guidance frames generative-AI optimization as SEO: no special markup, llms.txt requirement, or separate GEO/AEO playbook is required for Google visibility. Use GEO/AEO as shorthand labels only.
## Cross-reference
This skill covers FLOW surface 3 (AI assistant citations: ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com) and contributes to surface 2 (SERP plus AI Overviews). Surface mapping: `skills/blog/references/flow-alignment.md`.
For directly relevant AI-citation prompts (AI-supporting-pages-rewrite-prompt, ai-detector-test, ChatGPT discovery, visibility prompts), see `/blog flow optimize`.
## Evidence Discipline
Use numeric AI-citation benchmarks only when the report includes a source block with URL, publisher, methodology, sample size, engine or version, query class, retrieval date, and expiry date. If any field is missing, label the benchmark as directional or remove the number. Default heuristics:
- Self-contained 120-180 word answer passages are a practitioner heuristic. - Comparison tables with semantic headers may improve extractability, but do not cite an uplift without a dated source block. - AI Overviews coverage is methodology-dependent: cite a dated range, not a fixed point.
## Audit Process
### Step 1: Read Content
Extract from the blog post: - Full content text and word count - Heading structure (H1, H2, H3 hierarchy) - Individual paragraphs and their word counts - FAQ sections (if present) - Schema markup (JSON-LD, microdata, RDFa) - robots.txt mentions or meta robots directives - Any TL;DR or summary boxes - Comparison tables and their HTML structure - Numbered/ordered lists - Definition-style formatting
### Step 2: Passage-Level Citability (4 pts)
Check each section between headings for AI-extractable passages:
| Check | Criteria | |-------|----------| | Word count | Each section contains 120-180 word self-contained passages | | Context independence | Each passage makes sense extracted from surrounding context | | Claim structure | Passages contain: specific claim + supporting evidence + source attribution | | Completeness | Passage answers a question without requiring reader to read adjacent sections |
**Scoring:** Count passages meeting all criteria vs total sections. - 4 pts: 80%+ sections have citable passages - 3 pts: 60-79% - 2 pts: 40-59% - 1 pt: 20-39% - 0 pts: <20%
### Step 3: Q&A Formatting (3 pts)
Check heading format and answer structure:
| Check | Criteria | |-------|----------| | Question headings | 60-70% of H2s are phrased as questions | | Answer-first format | Opening paragraph under each H2 provides a direct answer | | FAQ section | Dedicated FAQ section with structured question-answer pairs |
**Scoring:** - 3 pts: All three criteria met - 2 pts: Two criteria met - 1 pt: One criterion met - 0 pts: None met
### Step 4: Entity Clarity (3 pts)
Check topic consistency and disambiguation:
| Check | Criteria | |-------|----------| | Canonical topic | One unambiguous primary topic per page | | Consistent naming | Same entity name used throughout (no confusing synonyms) | | Intro statement | Clear topic statement in the introduction paragraph | | Title-content match | Title accurately reflects the content focus |
**Scoring:** - 3 pts: All four criteria met - 2 pts: Three criteria met - 1 pt: One or two criteria met - 0 pts: None met
### Step 5: Content Structure for Extraction (3 pts)
Check for AI-extractable content patterns:
| Check | Criteria | |-------|----------| | TL;DR box | 40-60 word standalone summary present at top | | Comparison tables | Tables with semantic headers such as `<thead>` or clear column labels | | Ordered lists | Numbered lists for processes and step-by-step instructions | | Definition formatting | Key terms formatted with clear definition patterns | | Citation capsules | 40-60 word definitive statements in each major section |
**Scoring:** - 3 pts: 4-5 elements present - 2 pts: 3 elements present - 1 pt: 1-2 elements present - 0 pts: None present
### Step 6: AI Crawler Accessibility (2 pts)
Check technical requirements for AI crawler indexing:
| Check | Criteria | |-------|----------| | Static HTML | Content rendered in static HTML, not behind JavaScript | | Google visibility | Normal crawlability and indexability for Googlebot. No special GEO/AEO file or markup is required for Google AI features | | Non-Google AI crawlers | If the site wants visibility in non-Google answer engines, check robots.txt treatment for GPTBot, ChatGPT-User, ClaudeBot, PerplexityBot, and related documented crawlers | | Schema in HTML | Schema markup in static HTML, not JS-injected | | Page size | Reasonable page size within AI crawler limits |
**Scoring:** - 2 pts: Google crawlability/indexability is clean, and selected non-Google crawler policies match the site's stated goals - 1 pt: Google is indexable but one selected non-Google crawler or rendering check needs review - 0 pts: Google crawling/indexing is blocked or multiple selected crawlers are unintentionally blocked
### Step 7: Platform-Specific Analysis
Evaluate the post for each AI platform's citation preferences:
#### ChatGPT - Favors "Best X" listicles (43.8% of citations) - Prefers well-cited, authoritative content - Recency matters: recent updates get priority - Domain authority influences citation likelihood
#### Perplexity - Often favors fresh, source-dense, community-validated content. Verify with current logs or tooling before claiming a citation window.
#### Google AI Overviews - Follow Google's normal SEO guidance: make content helpful, crawlable, indexable, and eligible for snippets. No special GEO/AEO markup or llms.txt is required for Google visibility. - Prefers content that already ranks well organically, but verify any numeric claims with dated source blocks.
#### Google AI Mode - Treat separately from AI Overviews in reports. Emphasize normal Search eligibility, clear page purpose, accessible text, and consistency between visible content and structured data.
#### Claude, Gemini, Copilot, and You.com - Evaluate content clarity, source accessibility, freshness, and whether robots policy intentionally allows or blocks each crawler where documented. - Use engine-specific recommendations only when current docs, logs, or test results are available.
For each platform, provide: - Current citability rating (High / Medium / Low) - Specific improvements to increase citation likelihood - Content format recommendations
### Step 8: Generate Citation Capsules
For each H2 section in the post, write a citation capsule:
- **Length**: 40-60 words, self-contained - **Structure**: Specific claim + data point + source attribution - **Purpose**: A passage AI could directly quote as a citation - **Format**: Present as a suggested addition the author can embed
Example: ``` According to [Source], [specific claim with number]. This represents [context/comparison], making it [significance]. [Supporting detail that reinforces the claim]. ```
Generate one capsule per H2 section. Label each with the section heading it belongs under.
### Step 9: Calculate AI Citation Readiness Score (0-100)
Map the 15-point subcategory scores to a 0-100 display score:
| Category | Raw Points | Display Weight | Max Display Score | |----------|-----------|----------------|-------------------| | Passage-Level Citability | /4 | x6.75 | 27 | | Q&A Formatting | /3 | x6.67 | 20 | | Entity Clarity | /3 | x6.67 | 20 | | Content Structure | /3 | x6.67 | 20 | | AI Crawler Accessibility | /2 | x6.5 | 13 | | **Total** | **/15** | | **100** |
Rating thresholds: - 90-100: Excellent: highly citable by AI systems - 70-89: Good: citable with minor improvements - 50-69: Needs Work: significant gaps in citability - Below 50: Poor: major restructuring needed
### Step 10: Generate Report
Output the following report:
``` ## AI Citation Readiness Report: [Title]
**AI Citation Readiness Score: [X]/100**: [Rating]
### Score Breakdown | Category | Raw | Display | Max | |----------|-----|---------|-----| | Passage-Level Citability | X/4 | X | 27 | | Q&A Formatting | X/3 | X | 20 | | Entity Clarity | X/3 | X | 20 | | Content Structure | X/3 | X | 20 | | AI Crawler Accessibility | X/2 | X | 13 | | **Total** | **X/15** | **X** | **100** |
### Per-Section Citability Analysis | Section (H2) | Word Count | Self-Contained | Claim+Evidence | Citable | |---------------|-----------|----------------|----------------|---------| | [heading] | [N] | Yes/No | Yes/No | Yes/No |
### Platform-Specific Optimization #### ChatGPT - [specific recommendations]
#### Perplexity - [specific recommendations]
#### Google AI Overviews - [specific recommendations]
#### Google AI Mode - [specific recommendations]
#### Claude / Gemini / Copilot / You.com - [specific recommendations]
### Generated Citation Capsules
#### [H2 Section 1] > [40-60 word citation capsule]
#### [H2 Section 2] > [40-60 word citation capsule]
### Technical Recommendations - [ ] [Technical fix with specifics]
### Priority Action Items 1. [Most impactful improvement] 2. [Second most impactful] 3. [Third most impactful]
Run `/blog analyze <file>` for full content quality scoring. ```
### Optional: Search Performance Context (blog-google)
If blog-google credentials include Tier 1 (GSC) and the post has a published URL:
1. Query GSC by page and query dimensions, then filter rows to the URL: `python3 skills/blog-google/scripts/run.py gsc_query --property <property> --dimensions query,page --json` 2. Add to platform-specific analysis: - Current impressions, clicks, CTR, average position - Search queries driving traffic to this URL 3. Check indexation: `python3 skills/blog-google/scripts/run.py gsc_inspect <url> --json` 4. Report indexation status, canonical selection, mobile usability. 5. If skipped, report `SKIPPED: credentials unavailable` or `SKIPPED: unpublished URL`.
### Optional: AI Citation Probability Score
For a per-engine likelihood that the post gets cited by AI answer engines (distinct from the 15-point AI Citation Readiness category scored by `/blog analyze`), run:
```bash python3 scripts/ai_citation_score.py <file> --format markdown ```
It returns a 0-100 overall score plus per-engine subscores for Google AI Overview, Perplexity, and ChatGPT, a factor breakdown, and up to three highest-impact fixes.
Source provenance
Decision snapshot
2,016 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for blog-geo, ready for a manual X post.
A practical pick for a web workflow: blog-geo: AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever th... 2.0K stars https://www.openagentskill.com/skills/agricidaniel-blog-geo?ref=x
Listing + install path for blog-geo: https://www.openagentskill.com/skills/agricidaniel-blog-geo?ref=x Install: npx skills add AgriciDaniel/claude-blog --skill blog-geo
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Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
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Install targets
Codex install prompt
Install the "blog-geo" agent skill from https://github.com/AgriciDaniel/claude-blog/tree/main/brain/.raw/sources/claude-blog-skill/skills/blog-geo. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation". After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"agricidaniel-blog-geo","task":"Install blog-geo","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add AgriciDaniel/claude-blog --skill blog-geo
Maintenance
fresh
8d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
2.0K
80/100 Quality · 82/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
2.0K GitHub stars
Repo activity
2.0K stars, 333 forks
Maintenance
8d since push
License
MIT
Install
npx skills add AgriciDaniel/claude-blog --skill blog-geo
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add AgriciDaniel/claude-blog --skill blog-geoDo not use when
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/agricidaniel-blog-geo/install
Agent should check
Copy prompt
Task: Use blog-geo in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/agricidaniel-blog-geo/install
Install command: npx skills add AgriciDaniel/claude-blog --skill blog-geo
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/agricidaniel-blog-geo/install
LLM text format
/api/skills/agricidaniel-blog-geo/install?format=text
Find alternatives
/api/skills/search?q=blog-geo&limit=3
Agent prompt
Use blog-geo for this task. Review https://www.openagentskill.com/api/skills/agricidaniel-blog-geo/install, then install with: npx skills add AgriciDaniel/claude-blog --skill blog-geoRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/agricidaniel-blog-geo
LLM text
/api/registry/manifest/agricidaniel-blog-geo?format=text
Install alias
/api/registry/install/agricidaniel-blog-geo
Recommend
/api/registry/recommend?task=Use%20blog-geo%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
RAG and knowledge
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS2.0K GitHub stars
Stars/forks activity
PASS2.0K stars, 333 forks; issue activity unavailable in current metadata
Recent maintenance
PASS8d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Collect structured data
I need my agent to scrape websites and extract structured data from pages.
Workflow fit
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Alternative shortlist
Similar skills that may fit this task.
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--- name: blog-geo description: > AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation". user-invokable: true argument-hint: "<file-path>" license: MIT ---
# Blog GEO: AI Citation Optimization Audit
Scores blog posts for AI citation readiness across ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, and Google AI Mode as one SEO workflow, not a separate discipline. Generates citation capsules and a 0-100 AI Citation Readiness score with platform-specific recommendations.
Google's 2026-05-15 guidance frames generative-AI optimization as SEO: no special markup, llms.txt requirement, or separate GEO/AEO playbook is required for Google visibility. Use GEO/AEO as shorthand labels only.
## Cross-reference
This skill covers FLOW surface 3 (AI assistant citations: ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com) and contributes to surface 2 (SERP plus AI Overviews). Surface mapping: `skills/blog/references/flow-alignment.md`.
For directly relevant AI-citation prompts (AI-supporting-pages-rewrite-prompt, ai-detector-test, ChatGPT discovery, visibility prompts), see `/blog flow optimize`.
## Evidence Discipline
Use numeric AI-citation benchmarks only when the report includes a source block with URL, publisher, methodology, sample size, engine or version, query class, retrieval date, and expiry date. If any field is missing, label the benchmark as directional or remove the number. Default heuristics:
- Self-contained 120-180 word answer passages are a practitioner heuristic. - Comparison tables with semantic headers may improve extractability, but do not cite an uplift without a dated source block. - AI Overviews coverage is methodology-dependent: cite a dated range, not a fixed point.
## Audit Process
### Step 1: Read Content
Extract from the blog post: - Full content text and word count - Heading structure (H1, H2, H3 hierarchy) - Individual paragraphs and their word counts - FAQ sections (if present) - Schema markup (JSON-LD, microdata, RDFa) - robots.txt mentions or meta robots directives - Any TL;DR or summary boxes - Comparison tables and their HTML structure - Numbered/ordered lists - Definition-style formatting
### Step 2: Passage-Level Citability (4 pts)
Check each section between headings for AI-extractable passages:
| Check | Criteria | |-------|----------| | Word count | Each section contains 120-180 word self-contained passages | | Context independence | Each passage makes sense extracted from surrounding context | | Claim structure | Passages contain: specific claim + supporting evidence + source attribution | | Completeness | Passage answers a question without requiring reader to read adjacent sections |
**Scoring:** Count passages meeting all criteria vs total sections. - 4 pts: 80%+ sections have citable passages - 3 pts: 60-79% - 2 pts: 40-59% - 1 pt: 20-39% - 0 pts: <20%
### Step 3: Q&A Formatting (3 pts)
Check heading format and answer structure:
| Check | Criteria | |-------|----------| | Question headings | 60-70% of H2s are phrased as questions | | Answer-first format | Opening paragraph under each H2 provides a direct answer | | FAQ section | Dedicated FAQ section with structured question-answer pairs |
**Scoring:** - 3 pts: All three criteria met - 2 pts: Two criteria met - 1 pt: One criterion met - 0 pts: None met
### Step 4: Entity Clarity (3 pts)
Check topic consistency and disambiguation:
| Check | Criteria | |-------|----------| | Canonical topic | One unambiguous primary topic per page | | Consistent naming | Same entity name used throughout (no confusing synonyms) | | Intro statement | Clear topic statement in the introduction paragraph | | Title-content match | Title accurately reflects the content focus |
**Scoring:** - 3 pts: All four criteria met - 2 pts: Three criteria met - 1 pt: One or two criteria met - 0 pts: None met
### Step 5: Content Structure for Extraction (3 pts)
Check for AI-extractable content patterns:
| Check | Criteria | |-------|----------| | TL;DR box | 40-60 word standalone summary present at top | | Comparison tables | Tables with semantic headers such as `<thead>` or clear column labels | | Ordered lists | Numbered lists for processes and step-by-step instructions | | Definition formatting | Key terms formatted with clear definition patterns | | Citation capsules | 40-60 word definitive statements in each major section |
**Scoring:** - 3 pts: 4-5 elements present - 2 pts: 3 elements present - 1 pt: 1-2 elements present - 0 pts: None present
### Step 6: AI Crawler Accessibility (2 pts)
Check technical requirements for AI crawler indexing:
| Check | Criteria | |-------|----------| | Static HTML | Content rendered in static HTML, not behind JavaScript | | Google visibility | Normal crawlability and indexability for Googlebot. No special GEO/AEO file or markup is required for Google AI features | | Non-Google AI crawlers | If the site wants visibility in non-Google answer engines, check robots.txt treatment for GPTBot, ChatGPT-User, ClaudeBot, PerplexityBot, and related documented crawlers | | Schema in HTML | Schema markup in static HTML, not JS-injected | | Page size | Reasonable page size within AI crawler limits |
**Scoring:** - 2 pts: Google crawlability/indexability is clean, and selected non-Google crawler policies match the site's stated goals - 1 pt: Google is indexable but one selected non-Google crawler or rendering check needs review - 0 pts: Google crawling/indexing is blocked or multiple selected crawlers are unintentionally blocked
### Step 7: Platform-Specific Analysis
Evaluate the post for each AI platform's citation preferences:
#### ChatGPT - Favors "Best X" listicles (43.8% of citations) - Prefers well-cited, authoritative content - Recency matters: recent updates get priority - Domain authority influences citation likelihood
#### Perplexity - Often favors fresh, source-dense, community-validated content. Verify with current logs or tooling before claiming a citation window.
#### Google AI Overviews - Follow Google's normal SEO guidance: make content helpful, crawlable, indexable, and eligible for snippets. No special GEO/AEO markup or llms.txt is required for Google visibility. - Prefers content that already ranks well organically, but verify any numeric claims with dated source blocks.
#### Google AI Mode - Treat separately from AI Overviews in reports. Emphasize normal Search eligibility, clear page purpose, accessible text, and consistency between visible content and structured data.
#### Claude, Gemini, Copilot, and You.com - Evaluate content clarity, source accessibility, freshness, and whether robots policy intentionally allows or blocks each crawler where documented. - Use engine-specific recommendations only when current docs, logs, or test results are available.
For each platform, provide: - Current citability rating (High / Medium / Low) - Specific improvements to increase citation likelihood - Content format recommendations
### Step 8: Generate Citation Capsules
For each H2 section in the post, write a citation capsule:
- **Length**: 40-60 words, self-contained - **Structure**: Specific claim + data point + source attribution - **Purpose**: A passage AI could directly quote as a citation - **Format**: Present as a suggested addition the author can embed
Example: ``` According to [Source], [specific claim with number]. This represents [context/comparison], making it [significance]. [Supporting detail that reinforces the claim]. ```
Generate one capsule per H2 section. Label each with the section heading it belongs under.
### Step 9: Calculate AI Citation Readiness Score (0-100)
Map the 15-point subcategory scores to a 0-100 display score:
| Category | Raw Points | Display Weight | Max Display Score | |----------|-----------|----------------|-------------------| | Passage-Level Citability | /4 | x6.75 | 27 | | Q&A Formatting | /3 | x6.67 | 20 | | Entity Clarity | /3 | x6.67 | 20 | | Content Structure | /3 | x6.67 | 20 | | AI Crawler Accessibility | /2 | x6.5 | 13 | | **Total** | **/15** | | **100** |
Rating thresholds: - 90-100: Excellent: highly citable by AI systems - 70-89: Good: citable with minor improvements - 50-69: Needs Work: significant gaps in citability - Below 50: Poor: major restructuring needed
### Step 10: Generate Report
Output the following report:
``` ## AI Citation Readiness Report: [Title]
**AI Citation Readiness Score: [X]/100**: [Rating]
### Score Breakdown | Category | Raw | Display | Max | |----------|-----|---------|-----| | Passage-Level Citability | X/4 | X | 27 | | Q&A Formatting | X/3 | X | 20 | | Entity Clarity | X/3 | X | 20 | | Content Structure | X/3 | X | 20 | | AI Crawler Accessibility | X/2 | X | 13 | | **Total** | **X/15** | **X** | **100** |
### Per-Section Citability Analysis | Section (H2) | Word Count | Self-Contained | Claim+Evidence | Citable | |---------------|-----------|----------------|----------------|---------| | [heading] | [N] | Yes/No | Yes/No | Yes/No |
### Platform-Specific Optimization #### ChatGPT - [specific recommendations]
#### Perplexity - [specific recommendations]
#### Google AI Overviews - [specific recommendations]
#### Google AI Mode - [specific recommendations]
#### Claude / Gemini / Copilot / You.com - [specific recommendations]
### Generated Citation Capsules
#### [H2 Section 1] > [40-60 word citation capsule]
#### [H2 Section 2] > [40-60 word citation capsule]
### Technical Recommendations - [ ] [Technical fix with specifics]
### Priority Action Items 1. [Most impactful improvement] 2. [Second most impactful] 3. [Third most impactful]
Run `/blog analyze <file>` for full content quality scoring. ```
### Optional: Search Performance Context (blog-google)
If blog-google credentials include Tier 1 (GSC) and the post has a published URL:
1. Query GSC by page and query dimensions, then filter rows to the URL: `python3 skills/blog-google/scripts/run.py gsc_query --property <property> --dimensions query,page --json` 2. Add to platform-specific analysis: - Current impressions, clicks, CTR, average position - Search queries driving traffic to this URL 3. Check indexation: `python3 skills/blog-google/scripts/run.py gsc_inspect <url> --json` 4. Report indexation status, canonical selection, mobile usability. 5. If skipped, report `SKIPPED: credentials unavailable` or `SKIPPED: unpublished URL`.
### Optional: AI Citation Probability Score
For a per-engine likelihood that the post gets cited by AI answer engines (distinct from the 15-point AI Citation Readiness category scored by `/blog analyze`), run:
```bash python3 scripts/ai_citation_score.py <file> --format markdown ```
It returns a 0-100 overall score plus per-engine subscores for Google AI Overview, Perplexity, and ChatGPT, a factor breakdown, and up to three highest-impact fixes.
Source provenance
Decision snapshot
2,016 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for blog-geo, ready for a manual X post.
A practical pick for a web workflow: blog-geo: AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever th... 2.0K stars https://www.openagentskill.com/skills/agricidaniel-blog-geo?ref=x
Listing + install path for blog-geo: https://www.openagentskill.com/skills/agricidaniel-blog-geo?ref=x Install: npx skills add AgriciDaniel/claude-blog --skill blog-geo
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Install targets
Codex install prompt
Install the "blog-geo" agent skill from https://github.com/AgriciDaniel/claude-blog/tree/main/brain/.raw/sources/claude-blog-skill/skills/blog-geo. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation". After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"agricidaniel-blog-geo","task":"Install blog-geo","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add AgriciDaniel/claude-blog --skill blog-geo
Maintenance
fresh
8d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
2.0K
80/100 Quality · 82/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
2.0K GitHub stars
Repo activity
2.0K stars, 333 forks
Maintenance
8d since push
License
MIT
Install
npx skills add AgriciDaniel/claude-blog --skill blog-geo
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add AgriciDaniel/claude-blog --skill blog-geoDo not use when
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/agricidaniel-blog-geo/install
Agent should check
Copy prompt
Task: Use blog-geo in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/agricidaniel-blog-geo/install
Install command: npx skills add AgriciDaniel/claude-blog --skill blog-geo
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/agricidaniel-blog-geo/install
LLM text format
/api/skills/agricidaniel-blog-geo/install?format=text
Find alternatives
/api/skills/search?q=blog-geo&limit=3
Agent prompt
Use blog-geo for this task. Review https://www.openagentskill.com/api/skills/agricidaniel-blog-geo/install, then install with: npx skills add AgriciDaniel/claude-blog --skill blog-geoRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/agricidaniel-blog-geo
LLM text
/api/registry/manifest/agricidaniel-blog-geo?format=text
Install alias
/api/registry/install/agricidaniel-blog-geo
Recommend
/api/registry/recommend?task=Use%20blog-geo%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
RAG and knowledge
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS2.0K GitHub stars
Stars/forks activity
PASS2.0K stars, 333 forks; issue activity unavailable in current metadata
Recent maintenance
PASS8d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Collect structured data
I need my agent to scrape websites and extract structured data from pages.
Workflow fit
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Alternative shortlist
Similar skills that may fit this task.
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--- name: blog-geo description: > AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation". user-invokable: true argument-hint: "<file-path>" license: MIT ---
# Blog GEO: AI Citation Optimization Audit
Scores blog posts for AI citation readiness across ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, and Google AI Mode as one SEO workflow, not a separate discipline. Generates citation capsules and a 0-100 AI Citation Readiness score with platform-specific recommendations.
Google's 2026-05-15 guidance frames generative-AI optimization as SEO: no special markup, llms.txt requirement, or separate GEO/AEO playbook is required for Google visibility. Use GEO/AEO as shorthand labels only.
## Cross-reference
This skill covers FLOW surface 3 (AI assistant citations: ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com) and contributes to surface 2 (SERP plus AI Overviews). Surface mapping: `skills/blog/references/flow-alignment.md`.
For directly relevant AI-citation prompts (AI-supporting-pages-rewrite-prompt, ai-detector-test, ChatGPT discovery, visibility prompts), see `/blog flow optimize`.
## Evidence Discipline
Use numeric AI-citation benchmarks only when the report includes a source block with URL, publisher, methodology, sample size, engine or version, query class, retrieval date, and expiry date. If any field is missing, label the benchmark as directional or remove the number. Default heuristics:
- Self-contained 120-180 word answer passages are a practitioner heuristic. - Comparison tables with semantic headers may improve extractability, but do not cite an uplift without a dated source block. - AI Overviews coverage is methodology-dependent: cite a dated range, not a fixed point.
## Audit Process
### Step 1: Read Content
Extract from the blog post: - Full content text and word count - Heading structure (H1, H2, H3 hierarchy) - Individual paragraphs and their word counts - FAQ sections (if present) - Schema markup (JSON-LD, microdata, RDFa) - robots.txt mentions or meta robots directives - Any TL;DR or summary boxes - Comparison tables and their HTML structure - Numbered/ordered lists - Definition-style formatting
### Step 2: Passage-Level Citability (4 pts)
Check each section between headings for AI-extractable passages:
| Check | Criteria | |-------|----------| | Word count | Each section contains 120-180 word self-contained passages | | Context independence | Each passage makes sense extracted from surrounding context | | Claim structure | Passages contain: specific claim + supporting evidence + source attribution | | Completeness | Passage answers a question without requiring reader to read adjacent sections |
**Scoring:** Count passages meeting all criteria vs total sections. - 4 pts: 80%+ sections have citable passages - 3 pts: 60-79% - 2 pts: 40-59% - 1 pt: 20-39% - 0 pts: <20%
### Step 3: Q&A Formatting (3 pts)
Check heading format and answer structure:
| Check | Criteria | |-------|----------| | Question headings | 60-70% of H2s are phrased as questions | | Answer-first format | Opening paragraph under each H2 provides a direct answer | | FAQ section | Dedicated FAQ section with structured question-answer pairs |
**Scoring:** - 3 pts: All three criteria met - 2 pts: Two criteria met - 1 pt: One criterion met - 0 pts: None met
### Step 4: Entity Clarity (3 pts)
Check topic consistency and disambiguation:
| Check | Criteria | |-------|----------| | Canonical topic | One unambiguous primary topic per page | | Consistent naming | Same entity name used throughout (no confusing synonyms) | | Intro statement | Clear topic statement in the introduction paragraph | | Title-content match | Title accurately reflects the content focus |
**Scoring:** - 3 pts: All four criteria met - 2 pts: Three criteria met - 1 pt: One or two criteria met - 0 pts: None met
### Step 5: Content Structure for Extraction (3 pts)
Check for AI-extractable content patterns:
| Check | Criteria | |-------|----------| | TL;DR box | 40-60 word standalone summary present at top | | Comparison tables | Tables with semantic headers such as `<thead>` or clear column labels | | Ordered lists | Numbered lists for processes and step-by-step instructions | | Definition formatting | Key terms formatted with clear definition patterns | | Citation capsules | 40-60 word definitive statements in each major section |
**Scoring:** - 3 pts: 4-5 elements present - 2 pts: 3 elements present - 1 pt: 1-2 elements present - 0 pts: None present
### Step 6: AI Crawler Accessibility (2 pts)
Check technical requirements for AI crawler indexing:
| Check | Criteria | |-------|----------| | Static HTML | Content rendered in static HTML, not behind JavaScript | | Google visibility | Normal crawlability and indexability for Googlebot. No special GEO/AEO file or markup is required for Google AI features | | Non-Google AI crawlers | If the site wants visibility in non-Google answer engines, check robots.txt treatment for GPTBot, ChatGPT-User, ClaudeBot, PerplexityBot, and related documented crawlers | | Schema in HTML | Schema markup in static HTML, not JS-injected | | Page size | Reasonable page size within AI crawler limits |
**Scoring:** - 2 pts: Google crawlability/indexability is clean, and selected non-Google crawler policies match the site's stated goals - 1 pt: Google is indexable but one selected non-Google crawler or rendering check needs review - 0 pts: Google crawling/indexing is blocked or multiple selected crawlers are unintentionally blocked
### Step 7: Platform-Specific Analysis
Evaluate the post for each AI platform's citation preferences:
#### ChatGPT - Favors "Best X" listicles (43.8% of citations) - Prefers well-cited, authoritative content - Recency matters: recent updates get priority - Domain authority influences citation likelihood
#### Perplexity - Often favors fresh, source-dense, community-validated content. Verify with current logs or tooling before claiming a citation window.
#### Google AI Overviews - Follow Google's normal SEO guidance: make content helpful, crawlable, indexable, and eligible for snippets. No special GEO/AEO markup or llms.txt is required for Google visibility. - Prefers content that already ranks well organically, but verify any numeric claims with dated source blocks.
#### Google AI Mode - Treat separately from AI Overviews in reports. Emphasize normal Search eligibility, clear page purpose, accessible text, and consistency between visible content and structured data.
#### Claude, Gemini, Copilot, and You.com - Evaluate content clarity, source accessibility, freshness, and whether robots policy intentionally allows or blocks each crawler where documented. - Use engine-specific recommendations only when current docs, logs, or test results are available.
For each platform, provide: - Current citability rating (High / Medium / Low) - Specific improvements to increase citation likelihood - Content format recommendations
### Step 8: Generate Citation Capsules
For each H2 section in the post, write a citation capsule:
- **Length**: 40-60 words, self-contained - **Structure**: Specific claim + data point + source attribution - **Purpose**: A passage AI could directly quote as a citation - **Format**: Present as a suggested addition the author can embed
Example: ``` According to [Source], [specific claim with number]. This represents [context/comparison], making it [significance]. [Supporting detail that reinforces the claim]. ```
Generate one capsule per H2 section. Label each with the section heading it belongs under.
### Step 9: Calculate AI Citation Readiness Score (0-100)
Map the 15-point subcategory scores to a 0-100 display score:
| Category | Raw Points | Display Weight | Max Display Score | |----------|-----------|----------------|-------------------| | Passage-Level Citability | /4 | x6.75 | 27 | | Q&A Formatting | /3 | x6.67 | 20 | | Entity Clarity | /3 | x6.67 | 20 | | Content Structure | /3 | x6.67 | 20 | | AI Crawler Accessibility | /2 | x6.5 | 13 | | **Total** | **/15** | | **100** |
Rating thresholds: - 90-100: Excellent: highly citable by AI systems - 70-89: Good: citable with minor improvements - 50-69: Needs Work: significant gaps in citability - Below 50: Poor: major restructuring needed
### Step 10: Generate Report
Output the following report:
``` ## AI Citation Readiness Report: [Title]
**AI Citation Readiness Score: [X]/100**: [Rating]
### Score Breakdown | Category | Raw | Display | Max | |----------|-----|---------|-----| | Passage-Level Citability | X/4 | X | 27 | | Q&A Formatting | X/3 | X | 20 | | Entity Clarity | X/3 | X | 20 | | Content Structure | X/3 | X | 20 | | AI Crawler Accessibility | X/2 | X | 13 | | **Total** | **X/15** | **X** | **100** |
### Per-Section Citability Analysis | Section (H2) | Word Count | Self-Contained | Claim+Evidence | Citable | |---------------|-----------|----------------|----------------|---------| | [heading] | [N] | Yes/No | Yes/No | Yes/No |
### Platform-Specific Optimization #### ChatGPT - [specific recommendations]
#### Perplexity - [specific recommendations]
#### Google AI Overviews - [specific recommendations]
#### Google AI Mode - [specific recommendations]
#### Claude / Gemini / Copilot / You.com - [specific recommendations]
### Generated Citation Capsules
#### [H2 Section 1] > [40-60 word citation capsule]
#### [H2 Section 2] > [40-60 word citation capsule]
### Technical Recommendations - [ ] [Technical fix with specifics]
### Priority Action Items 1. [Most impactful improvement] 2. [Second most impactful] 3. [Third most impactful]
Run `/blog analyze <file>` for full content quality scoring. ```
### Optional: Search Performance Context (blog-google)
If blog-google credentials include Tier 1 (GSC) and the post has a published URL:
1. Query GSC by page and query dimensions, then filter rows to the URL: `python3 skills/blog-google/scripts/run.py gsc_query --property <property> --dimensions query,page --json` 2. Add to platform-specific analysis: - Current impressions, clicks, CTR, average position - Search queries driving traffic to this URL 3. Check indexation: `python3 skills/blog-google/scripts/run.py gsc_inspect <url> --json` 4. Report indexation status, canonical selection, mobile usability. 5. If skipped, report `SKIPPED: credentials unavailable` or `SKIPPED: unpublished URL`.
### Optional: AI Citation Probability Score
For a per-engine likelihood that the post gets cited by AI answer engines (distinct from the 15-point AI Citation Readiness category scored by `/blog analyze`), run:
```bash python3 scripts/ai_citation_score.py <file> --format markdown ```
It returns a 0-100 overall score plus per-engine subscores for Google AI Overview, Perplexity, and ChatGPT, a factor breakdown, and up to three highest-impact fixes.
Source provenance
Decision snapshot
2,016 GitHub stars
Audit
Install and adoption review
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Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
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Growth loop
Scenario-led draft for blog-geo, ready for a manual X post.
A practical pick for a web workflow: blog-geo: AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever th... 2.0K stars https://www.openagentskill.com/skills/agricidaniel-blog-geo?ref=x
Listing + install path for blog-geo: https://www.openagentskill.com/skills/agricidaniel-blog-geo?ref=x Install: npx skills add AgriciDaniel/claude-blog --skill blog-geo
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Install targets
Codex install prompt
Install the "blog-geo" agent skill from https://github.com/AgriciDaniel/claude-blog/tree/main/brain/.raw/sources/claude-blog-skill/skills/blog-geo. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation". After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"agricidaniel-blog-geo","task":"Install blog-geo","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add AgriciDaniel/claude-blog --skill blog-geo
Maintenance
fresh
8d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
2.0K
80/100 Quality · 82/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
2.0K GitHub stars
Repo activity
2.0K stars, 333 forks
Maintenance
8d since push
License
MIT
Install
npx skills add AgriciDaniel/claude-blog --skill blog-geo
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add AgriciDaniel/claude-blog --skill blog-geoDo not use when
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/agricidaniel-blog-geo/install
Agent should check
Copy prompt
Task: Use blog-geo in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20blog-geo%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/agricidaniel-blog-geo/install
Install command: npx skills add AgriciDaniel/claude-blog --skill blog-geo
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/agricidaniel-blog-geo/install
LLM text format
/api/skills/agricidaniel-blog-geo/install?format=text
Find alternatives
/api/skills/search?q=blog-geo&limit=3
Agent prompt
Use blog-geo for this task. Review https://www.openagentskill.com/api/skills/agricidaniel-blog-geo/install, then install with: npx skills add AgriciDaniel/claude-blog --skill blog-geoRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/agricidaniel-blog-geo
LLM text
/api/registry/manifest/agricidaniel-blog-geo?format=text
Install alias
/api/registry/install/agricidaniel-blog-geo
Recommend
/api/registry/recommend?task=Use%20blog-geo%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
RAG and knowledge
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS2.0K GitHub stars
Stars/forks activity
PASS2.0K stars, 333 forks; issue activity unavailable in current metadata
Recent maintenance
PASS8d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Collect structured data
I need my agent to scrape websites and extract structured data from pages.
Workflow fit
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Alternative shortlist
Similar skills that may fit this task.
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--- name: blog-geo description: > AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation". user-invokable: true argument-hint: "<file-path>" license: MIT ---
# Blog GEO: AI Citation Optimization Audit
Scores blog posts for AI citation readiness across ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, and Google AI Mode as one SEO workflow, not a separate discipline. Generates citation capsules and a 0-100 AI Citation Readiness score with platform-specific recommendations.
Google's 2026-05-15 guidance frames generative-AI optimization as SEO: no special markup, llms.txt requirement, or separate GEO/AEO playbook is required for Google visibility. Use GEO/AEO as shorthand labels only.
## Cross-reference
This skill covers FLOW surface 3 (AI assistant citations: ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com) and contributes to surface 2 (SERP plus AI Overviews). Surface mapping: `skills/blog/references/flow-alignment.md`.
For directly relevant AI-citation prompts (AI-supporting-pages-rewrite-prompt, ai-detector-test, ChatGPT discovery, visibility prompts), see `/blog flow optimize`.
## Evidence Discipline
Use numeric AI-citation benchmarks only when the report includes a source block with URL, publisher, methodology, sample size, engine or version, query class, retrieval date, and expiry date. If any field is missing, label the benchmark as directional or remove the number. Default heuristics:
- Self-contained 120-180 word answer passages are a practitioner heuristic. - Comparison tables with semantic headers may improve extractability, but do not cite an uplift without a dated source block. - AI Overviews coverage is methodology-dependent: cite a dated range, not a fixed point.
## Audit Process
### Step 1: Read Content
Extract from the blog post: - Full content text and word count - Heading structure (H1, H2, H3 hierarchy) - Individual paragraphs and their word counts - FAQ sections (if present) - Schema markup (JSON-LD, microdata, RDFa) - robots.txt mentions or meta robots directives - Any TL;DR or summary boxes - Comparison tables and their HTML structure - Numbered/ordered lists - Definition-style formatting
### Step 2: Passage-Level Citability (4 pts)
Check each section between headings for AI-extractable passages:
| Check | Criteria | |-------|----------| | Word count | Each section contains 120-180 word self-contained passages | | Context independence | Each passage makes sense extracted from surrounding context | | Claim structure | Passages contain: specific claim + supporting evidence + source attribution | | Completeness | Passage answers a question without requiring reader to read adjacent sections |
**Scoring:** Count passages meeting all criteria vs total sections. - 4 pts: 80%+ sections have citable passages - 3 pts: 60-79% - 2 pts: 40-59% - 1 pt: 20-39% - 0 pts: <20%
### Step 3: Q&A Formatting (3 pts)
Check heading format and answer structure:
| Check | Criteria | |-------|----------| | Question headings | 60-70% of H2s are phrased as questions | | Answer-first format | Opening paragraph under each H2 provides a direct answer | | FAQ section | Dedicated FAQ section with structured question-answer pairs |
**Scoring:** - 3 pts: All three criteria met - 2 pts: Two criteria met - 1 pt: One criterion met - 0 pts: None met
### Step 4: Entity Clarity (3 pts)
Check topic consistency and disambiguation:
| Check | Criteria | |-------|----------| | Canonical topic | One unambiguous primary topic per page | | Consistent naming | Same entity name used throughout (no confusing synonyms) | | Intro statement | Clear topic statement in the introduction paragraph | | Title-content match | Title accurately reflects the content focus |
**Scoring:** - 3 pts: All four criteria met - 2 pts: Three criteria met - 1 pt: One or two criteria met - 0 pts: None met
### Step 5: Content Structure for Extraction (3 pts)
Check for AI-extractable content patterns:
| Check | Criteria | |-------|----------| | TL;DR box | 40-60 word standalone summary present at top | | Comparison tables | Tables with semantic headers such as `<thead>` or clear column labels | | Ordered lists | Numbered lists for processes and step-by-step instructions | | Definition formatting | Key terms formatted with clear definition patterns | | Citation capsules | 40-60 word definitive statements in each major section |
**Scoring:** - 3 pts: 4-5 elements present - 2 pts: 3 elements present - 1 pt: 1-2 elements present - 0 pts: None present
### Step 6: AI Crawler Accessibility (2 pts)
Check technical requirements for AI crawler indexing:
| Check | Criteria | |-------|----------| | Static HTML | Content rendered in static HTML, not behind JavaScript | | Google visibility | Normal crawlability and indexability for Googlebot. No special GEO/AEO file or markup is required for Google AI features | | Non-Google AI crawlers | If the site wants visibility in non-Google answer engines, check robots.txt treatment for GPTBot, ChatGPT-User, ClaudeBot, PerplexityBot, and related documented crawlers | | Schema in HTML | Schema markup in static HTML, not JS-injected | | Page size | Reasonable page size within AI crawler limits |
**Scoring:** - 2 pts: Google crawlability/indexability is clean, and selected non-Google crawler policies match the site's stated goals - 1 pt: Google is indexable but one selected non-Google crawler or rendering check needs review - 0 pts: Google crawling/indexing is blocked or multiple selected crawlers are unintentionally blocked
### Step 7: Platform-Specific Analysis
Evaluate the post for each AI platform's citation preferences:
#### ChatGPT - Favors "Best X" listicles (43.8% of citations) - Prefers well-cited, authoritative content - Recency matters: recent updates get priority - Domain authority influences citation likelihood
#### Perplexity - Often favors fresh, source-dense, community-validated content. Verify with current logs or tooling before claiming a citation window.
#### Google AI Overviews - Follow Google's normal SEO guidance: make content helpful, crawlable, indexable, and eligible for snippets. No special GEO/AEO markup or llms.txt is required for Google visibility. - Prefers content that already ranks well organically, but verify any numeric claims with dated source blocks.
#### Google AI Mode - Treat separately from AI Overviews in reports. Emphasize normal Search eligibility, clear page purpose, accessible text, and consistency between visible content and structured data.
#### Claude, Gemini, Copilot, and You.com - Evaluate content clarity, source accessibility, freshness, and whether robots policy intentionally allows or blocks each crawler where documented. - Use engine-specific recommendations only when current docs, logs, or test results are available.
For each platform, provide: - Current citability rating (High / Medium / Low) - Specific improvements to increase citation likelihood - Content format recommendations
### Step 8: Generate Citation Capsules
For each H2 section in the post, write a citation capsule:
- **Length**: 40-60 words, self-contained - **Structure**: Specific claim + data point + source attribution - **Purpose**: A passage AI could directly quote as a citation - **Format**: Present as a suggested addition the author can embed
Example: ``` According to [Source], [specific claim with number]. This represents [context/comparison], making it [significance]. [Supporting detail that reinforces the claim]. ```
Generate one capsule per H2 section. Label each with the section heading it belongs under.
### Step 9: Calculate AI Citation Readiness Score (0-100)
Map the 15-point subcategory scores to a 0-100 display score:
| Category | Raw Points | Display Weight | Max Display Score | |----------|-----------|----------------|-------------------| | Passage-Level Citability | /4 | x6.75 | 27 | | Q&A Formatting | /3 | x6.67 | 20 | | Entity Clarity | /3 | x6.67 | 20 | | Content Structure | /3 | x6.67 | 20 | | AI Crawler Accessibility | /2 | x6.5 | 13 | | **Total** | **/15** | | **100** |
Rating thresholds: - 90-100: Excellent: highly citable by AI systems - 70-89: Good: citable with minor improvements - 50-69: Needs Work: significant gaps in citability - Below 50: Poor: major restructuring needed
### Step 10: Generate Report
Output the following report:
``` ## AI Citation Readiness Report: [Title]
**AI Citation Readiness Score: [X]/100**: [Rating]
### Score Breakdown | Category | Raw | Display | Max | |----------|-----|---------|-----| | Passage-Level Citability | X/4 | X | 27 | | Q&A Formatting | X/3 | X | 20 | | Entity Clarity | X/3 | X | 20 | | Content Structure | X/3 | X | 20 | | AI Crawler Accessibility | X/2 | X | 13 | | **Total** | **X/15** | **X** | **100** |
### Per-Section Citability Analysis | Section (H2) | Word Count | Self-Contained | Claim+Evidence | Citable | |---------------|-----------|----------------|----------------|---------| | [heading] | [N] | Yes/No | Yes/No | Yes/No |
### Platform-Specific Optimization #### ChatGPT - [specific recommendations]
#### Perplexity - [specific recommendations]
#### Google AI Overviews - [specific recommendations]
#### Google AI Mode - [specific recommendations]
#### Claude / Gemini / Copilot / You.com - [specific recommendations]
### Generated Citation Capsules
#### [H2 Section 1] > [40-60 word citation capsule]
#### [H2 Section 2] > [40-60 word citation capsule]
### Technical Recommendations - [ ] [Technical fix with specifics]
### Priority Action Items 1. [Most impactful improvement] 2. [Second most impactful] 3. [Third most impactful]
Run `/blog analyze <file>` for full content quality scoring. ```
### Optional: Search Performance Context (blog-google)
If blog-google credentials include Tier 1 (GSC) and the post has a published URL:
1. Query GSC by page and query dimensions, then filter rows to the URL: `python3 skills/blog-google/scripts/run.py gsc_query --property <property> --dimensions query,page --json` 2. Add to platform-specific analysis: - Current impressions, clicks, CTR, average position - Search queries driving traffic to this URL 3. Check indexation: `python3 skills/blog-google/scripts/run.py gsc_inspect <url> --json` 4. Report indexation status, canonical selection, mobile usability. 5. If skipped, report `SKIPPED: credentials unavailable` or `SKIPPED: unpublished URL`.
### Optional: AI Citation Probability Score
For a per-engine likelihood that the post gets cited by AI answer engines (distinct from the 15-point AI Citation Readiness category scored by `/blog analyze`), run:
```bash python3 scripts/ai_citation_score.py <file> --format markdown ```
It returns a 0-100 overall score plus per-engine subscores for Google AI Overview, Perplexity, and ChatGPT, a factor breakdown, and up to three highest-impact fixes.
Source provenance
Decision snapshot
2,016 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for blog-geo, ready for a manual X post.
A practical pick for a web workflow: blog-geo: AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever th... 2.0K stars https://www.openagentskill.com/skills/agricidaniel-blog-geo?ref=x
Listing + install path for blog-geo: https://www.openagentskill.com/skills/agricidaniel-blog-geo?ref=x Install: npx skills add AgriciDaniel/claude-blog --skill blog-geo
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Wazuh
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16.3K StarsMaigret
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32.9K StarsNuclei
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27.4K StarsPermission surface
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Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
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Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
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Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
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Docs
Strong README/SKILL.md context
Risk summary
Install readiness