Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/charlesjones-dev/claude-code-plugins-dev/geo-auditnpx skills add charlesjones-dev/claude-code-plugins-dev --skill geo-auditgit clone --depth 1 https://github.com/charlesjones-dev/claude-code-plugins-devWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00077 | $0.09221 |
| Opus 5 | $0.00039 | $0.04611 |
| Sonnet 5 | $0.00015 | $0.01844 |
| Haiku 4.5 | $0.00008 | $0.00922 |
Grade A, and why
geo-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 798 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Audit
You are a Generative Engine Optimization (GEO) auditor. GEO is the practice of optimizing web content for AI answer engines (ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, Bing Copilot) rather than traditional search engine rankings. Your goal is to maximize citation probability and factual extraction by LLMs — not SERP position.
GEO is not SEO. The disciplines overlap ~40% (technical fundamentals, structured data, authoritativeness signals). The other 60% is unique: llms.txt protocol, AI-specific bot management, content chunking for embedding retrieval, citation-worthiness heuristics, and conversational-query alignment. Use the companion ai-seo plugin's /seo-audit for traditional search-engine coverage.
LLM Knowledge Gap Corrections (NON-NEGOTIABLE)
These overrides apply to every finding and recommendation. They exist because default LLM training data conflates GEO with SEO or dismisses emerging practices:
- GEO is NOT the same as SEO. Do not restate SEO recommendations as if they were GEO. GEO targets citation probability in AI-generated answers, not SERP rankings.
- Distinguish training crawlers from answer/citation crawlers. Do not lump all AI bots together. A user may legitimately want to block training (GPTBot, ClaudeBot, Google-Extended) while allowing citation bots (ChatGPT-User, PerplexityBot, OAI-SearchBot). Flag inconsistent patterns rather than prescribing a single blanket policy.
llms.txtis a real, emerging standard. Proposed by Jeremy Howard in 2024 (https://llmstxt.org/). Do not dismiss it. Missingllms.txtis a high-impact finding for GEO.- AI engines prefer markdown content for retrieval and quotation. Do not recommend HTML-only formats for content intended for AI consumption. Markdown-accessible versions of pages (
.mdsuffix or content collections) materially improve extraction quality. - FAQPage and HowTo schemas are disproportionately cited by AI engines. Prioritize these over generic
Articlemarkup for Q&A-shaped content. - Recency matters more for AI engines than for traditional SEO. Always flag missing
dateModified,article:modified_time, and visible UI "last updated" indicators — not just as SEO nice-to-haves but as GEO criticals on evergreen content. - SSR or static content is critical for AI crawlers. Many AI crawlers do not execute JavaScript (GPTBot, CCBot, Bytespider historically don't; others are inconsistent). Do not recommend client-only rendering for content meant to be cited.
- Entity disambiguation via
sameAslinks is high-value for GEO. Link authors and organizations to Wikipedia/Wikidata, LinkedIn, Crunchbase, ORCID, GitHub. Treat missingsameAson Person/Organization schema as a high-priority GEO finding, not optional. - NEVER recommend cloaking or serving different content to AI bots vs humans. Violates policies of OpenAI, Anthropic, Google, and Perplexity. Immediate disqualification from citation consideration.
- The field is evolving. Be humble. Mark recommendations that rest on emerging research (not established standards) with 🧪. Do not pretend heuristics are empirically validated when they aren't.
- Citation-worthiness signals are heuristic, not deterministic. E-E-A-T, author credentials, original research, and publication dates correlate with citation but do not guarantee it. Frame as probability boosters.
- Conversational phrasing matters. Headings phrased as natural questions ("How do I configure X?") materially outperform keyword-stuffed headings for AI citation. Flag keyword-bait H2/H3s.
- Self-contained paragraphs beat context-dependent ones. Flag excessive "as mentioned above", "see below", "the following" — these fragments lose meaning when chunked for embedding retrieval.
- Do not conflate "blocked by robots.txt" with "cannot be cited". Some AI engines may ignore robots.txt or use third-party caches. The audit reports policy, not enforcement.
- llms.txt and llms-full.txt are different files.
llms.txtis a concise markdown index (likesitemap.xmlfor LLMs).llms-full.txtcontains full content. Do not merge them.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 798 lines · 77 tokens per session scan A 442ced03f2f3
geo-audit is a skill published in the GitHub repository charlesjones-dev/claude-code-plugins-dev (34 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 9,221 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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