geo-fix

A website-fixing helper that applies selected improvements from the latest GEO audit. GEO means preparing content for AI-generated answers, rather than only for traditional search rankings.

In plain words
What is it for?
Updating robots.txt, adding supported structured data such as Author, Organization, and FAQPage JSON-LD, adding modification dates, and proposing question-and-answer content improvements.
Why use it?
It turns audit findings into concrete site changes while asking before making decisions that depend on the site's identity or crawler policy.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/charlesjones-dev/claude-code-plugins-dev/geo-fix
Any agent
npx skills add charlesjones-dev/claude-code-plugins-dev --skill geo-fix
Clone the repo
git clone --depth 1 https://github.com/charlesjones-dev/claude-code-plugins-dev

Made for: Claude Code, Codex.

Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,030 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5 $0.00080 $0.05030
Opus 5 $0.00040 $0.02515
Sonnet 5 $0.00016 $0.01006
Haiku 4.5 $0.00008 $0.00503

Measured 2d ago against content hash baf6247c70e2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

geo-fix 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.

plugins/ai-geo/skills/geo-fix/SKILL.md · 436 lines

How it starts

The opening of the file, as written. The whole thing — 436 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GEO Fix

You are a Generative Engine Optimization remediation engineer. You take findings from the most recent /geo-audit run and apply safe, framework-appropriate fixes that maximize AI citation probability. Ambiguous changes (writing meta descriptions, choosing which AI crawlers to allow, supplying sameAs URLs) must be proposed to the user for confirmation — never guess at user intent or fabricate identity URLs.

GEO is not SEO. Do not apply SEO-style fixes generically. Use /seo-fix from the ai-seo plugin for traditional SEO remediations. This skill focuses on AI answer engines.

LLM Knowledge Gap Corrections (NON-NEGOTIABLE)

These overrides apply to every fix you propose:

  1. NEVER block all AI crawlers wholesale without confirming intent. Always prompt separately for training-bot and citation-bot preferences.
  2. NEVER fabricate sameAs URLs or author profile links. If the user hasn't provided them, prompt — don't guess.
  3. NEVER serve different content to AI bots than to humans (cloaking). Violates policies of all major AI engines.
  4. NEVER recommend client-only rendering for content pages. Propose SSR/static instead.
  5. ALWAYS generate JSON-LD for structured data (never microdata / RDFa).
  6. ALWAYS use framework-idiomatic APIs for head/meta/route-level data (Next.js Metadata API, Nuxt useSeoMeta, TanStack Start route head, Astro frontmatter/content collections).
  7. ALWAYS preserve existing AI-bot policies the user set intentionally. Before modifying robots.txt, read existing directives and confirm overwrites.
  8. llms.txt is not generated here. Direct the user to /geo-llms-txt for that.

Instructions

CRITICAL: Accept one optional flag only: --dry-run. Ignore any other arguments.

Step 1: Locate Latest Audit

  1. Detect docs dir: check docs/, documentation/, .docs/ (same order as /geo-audit).
  2. Read <docs-dir>/geo-audit/latest.md.
  3. If missing:

    "No audit found at <docs-dir>/geo-audit/latest.md. Run /geo-audit first to generate the baseline audit." Then stop.

  4. Parse the audit to extract findings grouped by severity. Capture each finding's file, line, category, current code, and recommended fix.

Read the full file on GitHub · 436 lines

Changes

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.

  1. 2d ago First seen · 436 lines · 80 tokens per session scan A baf6247c70e2

Subscribe to this mod's changes

geo-fix is a skill published in the GitHub repository charlesjones-dev/claude-code-plugins-dev (34 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 5,030 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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