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 instructions/alonw0/llm-docs-optimizer/claude-mdgit clone --depth 1 https://github.com/alonw0/llm-docs-optimizerWrote 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/instructions/alonw0/llm-docs-optimizer/claude-md)<a href="https://agentmods.dev/instructions/alonw0/llm-docs-optimizer/claude-md"><img src="https://agentmods.dev/badge/instructions/alonw0/llm-docs-optimizer/claude-md.svg" alt="Measured on agentmods" 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 | $0.01380 | $0.01380 |
| Opus 5 | $0.00690 | $0.00690 |
| Sonnet 5 | $0.00276 | $0.00276 |
| Haiku 4.5 | $0.00138 | $0.00138 |
Grade A, and why
llm-docs-optimizer CLAUDE.md 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 3d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
llm-docs-optimizer is a Claude Code plugin that optimizes documentation for AI coding assistants (Claude, GitHub Copilot, etc.). It provides two core capabilities:
- C7Score Optimization: Transforms documentation to score highly on Context7's benchmark - the leading quality metric for AI-assisted coding documentation
- llms.txt Generation: Creates standardized navigation files (llmstxt.org format) that help LLMs quickly understand and navigate project documentation
Architecture
This is NOT a Traditional Codebase
This is a Claude Code skill/plugin where the "code" is markdown. There are no build tools, test frameworks, or compilation steps for the main plugin.
skills/llm-docs-optimizer/
├── SKILL.md (21,827 lines) # Main skill logic - the "code"
├── references/ # Knowledge base
│ ├── c7score_metrics.md # Scoring rubrics for 5 metrics
│ ├── optimization_patterns.md # 20+ transformation examples
│ └── llmstxt_format.md # llms.txt specification
├── examples/ # Before/after samples
└── scripts/
└── analyze_docs.py # Optional Python analysis tool
Component Interaction
- SKILL.md: Contains structured workflows for both optimization and generation. This is where all logic lives.
- Reference Materials: Knowledge base that SKILL.md references during execution
- Python Script: Optional standalone tool for automated documentation scanning
- c7score/: Separate TypeScript reference implementation (Upstash's package) - not directly used by the skill
Key Architectural Pattern: Structured Workflows
C7Score Optimization (6 Steps):
- Analyze current documentation
- Generate 15-20 developer questions
- Map questions to existing snippets
- Optimize based on priority (80% weight on question-answering)
- Validate optimizations
- Evaluate c7score impact with before/after scoring
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.
- 3d ago First seen · 153 lines · 1,380 tokens per session scan A a0b7cdbba354
llm-docs-optimizer CLAUDE.md is an instructions file published in the GitHub repository alonw0/llm-docs-optimizer (62 stars, last pushed 9mo ago), licensed MIT. It adds 1,380 tokens to every session, about $0.0069 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 instructions, from other repositories
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Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
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AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
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langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.