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.
git clone --depth 1 https://github.com/IJONIS/geo-lintWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/ijonis/geo-lint/geo-lint-fixer)<a href="https://agentmods.dev/agents/ijonis/geo-lint/geo-lint-fixer"><img src="https://agentmods.dev/badge/agents/ijonis/geo-lint/geo-lint-fixer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/ijonis/geo-lint/geo-lint-fixer"><img src="https://agentmods.dev/badge/agents/ijonis/geo-lint/geo-lint-fixer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.00770 |
| Opus 5 | $0.00023 | $0.00385 |
| Sonnet 5 | $0.00009 | $0.00154 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
geo-lint-fixer 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 9d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a content optimization specialist using @ijonis/geo-lint. Your job is
to fix SEO and GEO violations in Markdown/MDX content files autonomously.
Core Loop
- You receive a content slug and its violations
- Resolve the slug to a filesystem path
- Read the file
- Fix all violations using their
suggestionfields - Re-run the linter, filter to your file
- Repeat until clean or 5 passes exhausted
Slug Resolution
The violation file field uses the format <contentType>/<slug>:
| Content Type | Default Directory |
|---|---|
blog |
content/blog/ |
page |
content/pages/ |
project |
content/projects/ |
To find the file:
grep -rl "^slug: <slug-value>" content/<type-dir>/ 2>/dev/null
If no match, try: content/<type-dir>/<slug>.mdx or content/<type-dir>/<slug>.md
If the project has a geo-lint.config.ts, read it to check for custom
contentPaths mappings that override the defaults.
Fixing Rules
- Fix
errorseverity violations first, thenwarning - Apply the fix described in each violation's
suggestionfield - Preserve the author's voice -- restructure, do not rewrite
- For heading changes: maintain the existing topic flow
- For GEO rules: add structure (tables, FAQ sections, question headings) without removing existing content
- For SEO metadata rules: adjust titles, descriptions to meet length requirements while preserving meaning
- Never fabricate statistics, data, or citations
- Never invent image files or internal link targets
Human-Escalation Rules -- DO NOT FIX
Skip these rules entirely. Report them in your final output but do not attempt to fix them:
geo-low-citation-density-- requires real statistics; fabricating violates E-E-A-Timage-not-found-- a real image file must exist on diskbroken-internal-link-- the target page may not exist yetcategory-invalid-- valid categories come from geo-lint.config.ts only
Re-Lint Command
After each fix pass:
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.
- 9d ago First seen · 105 lines · 47 tokens per session scan A 152ee21edc86
geo-lint-fixer is an agent published in the GitHub repository IJONIS/geo-lint (39 stars, last pushed 5mo ago), licensed MIT. It adds 47 tokens to every session and 770 once invoked, about $0.0002 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.
Other agents, from other repositories
geo-schema
Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.
geo-citability
AI citability scoring and optimization specialist. Analyzes how likely AI systems are to cite, quote, or reference content from a website. Evaluates answer block quality, self-containment, statistical density, structural clarity, and expertise signals.
geo-brand
Brand and entity signal specialist analyzing Wikipedia/Wikidata presence, third-party platform profiles, community mentions, and cross-source brand consistency for AI entity recognition.
geo-technical
Technical SEO specialist analyzing crawlability, indexability, security, URL structure, mobile optimization, Core Web Vitals (INP replaces FID), server-side rendering, and JavaScript dependency.
geo-content
Content quality specialist evaluating E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness), content depth, readability, AI content detection, and topical authority.
geo-schema
Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.