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Agent Context Audit

OrganizationPopular
AI-Builder-Club
agent-context-audit

Audit a repo's agent context — CLAUDE.md files, codebase docs, skills, and tool/MCP designs — against Anthropic's Claude 5 context-engineering guidance ("unhobbling": Anthropic cut ~80% of Claude Code's system prompt with no eval loss). Finds overconstraint, conflicting instructions, redundancy, stale facts, and missing "unknown knowns"; produces a scored findings report with concrete rewrites, then applies approved fixes. Use when someone says "audit my CLAUDE.md", "context audit", "unhobble this repo", "review our agent docs/skills/tools", or after upgrading to Claude 5-generation models.

Overview

PublisherAI-Builder-Club
Repositoryskills
Skill nameagent-context-audit
Stars
1.3K
Forks
159
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AI-Builder-Club on GitHub. Read the source before you install it.

Installation

Install the Agent Context Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AI-Builder-Club/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/agent-context-audit .claude/skills/agent-context-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Context Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Context Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Context Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

agent-context-audit — unhobble this repo's agent context

Goal: find where this repo's context (CLAUDE.md, docs, skills, tool designs) hobbles a Claude 5-generation model — overconstrains it, contradicts itself, repeats itself, or hides context the model actually needs — and leave behind a findings report plus approved fixes.

Background: Anthropic removed over 80% of Claude Code's system prompt for Claude 5 models with no measurable loss on coding evals. Older context was written for models that needed rules; newer models need judgment, good interfaces, and the facts they can't infer. This skill audits against that shift, plus the "finding your unknowns" framework (the gap between the map — your prompts/docs — and the territory — the actual codebase).

You are auditing first, fixing second. Do not edit anything until Step 4.

The six shifts (the audit rubric)

Every finding maps to one of these. Cite the shift number in the report.

  1. Rules → Judgment. Hard rules ("NEVER…", "ALWAYS…", "do not add comments", "one-line docstrings max") that encode a preference, not a real constraint, should become judgment framing ("write code that reads like the surrounding code") — or be deleted if the model would infer it anyway. Keep hard rules only where violation is genuinely costly (security, prod data, irreversible actions, legal/billing).
  2. Examples → Interface design. Long tool-usage examples and few-shot transcripts constrain exploration. Prefer expressive interfaces: good parameter names, enums that hint at valid states, tight descriptions. In tool/MCP definitions, an enum of pending | in_progress | completed teaches more than three worked examples.
  3. Upfront context → Progressive disclosure. Anything long that's only sometimes needed (review checklists, deploy runbooks, style deep-dives) should move out of CLAUDE.md into a skill or linked file loaded on demand. CLAUDE.md is loaded every session — it should carry only what every session needs.
  4. Repetition → Concise, single-home instructions. The same instruction appearing in CLAUDE.md and a skill and a tool description is a bug: copies drift and eventually conflict. Each instruction gets exactly one home — tool-usage guidance lives in the tool description, repo gotchas in CLAUDE.md, team opinions in skills.
  5. Manual memory → Automatic memory. Sections telling the agent to hand-maintain notes/changelogs in CLAUDE.md, or accumulated session-specific trivia, are obsolete where auto-memory exists. Flag CLAUDE.md content that is really memory (per-user, per-incident, time-bound) rather than repo truth.
  6. Simple specs → Rich references. Where docs describe behavior in loose prose, prefer pointing at the real thing: @-referenced source files, a test suite, an HTML mockup, a rubric a verifier can score against. Code-based specs beat prose paraphrases of code.

Cross-cutting failure modes to hunt alongside the shifts:

  • Conflicts — instructions that clash across layers (e.g. "document thoroughly" in one file, "DO NOT add comments" in another). Highest-value findings; a conflict forces the model to deliberate or guess on every task.
  • Staleness (map ≠ territory) — docs naming files, commands, flags, or services that no longer exist, or missing ones that now do. Verify every concrete claim you audit against the actual repo.
  • Missing unknown-knowns — things obvious to the team but written nowhere: the non-obvious build step, the directory you must never touch, the reason a weird pattern exists. These are what CLAUDE.md is for ("repository gotchas rather than obvious patterns").

Step 0 — Inventory the context surface

Collect everything that gets assembled into an agent's context here. Look for the capability, not a specific filename:

  • CLAUDE.md files — root, nested per-directory, ~/.claude/CLAUDE.md only if the user asks for a global audit. Also AGENTS.md, .cursorrules, .github/copilot-instructions.md if present (same disease, same cure).
  • Skills.claude/skills/**/SKILL.md, skills/**/SKILL.md, plugin skills committed to the repo.
  • Tool designs — MCP server definitions the repo owns (tool names, descriptions, parameter schemas), custom slash commands, hooks, and any agent definitions (.claude/agents/*.md).
  • Codebase docs agents are pointed at — README, CONTRIBUTING, docs/ referenced from CLAUDE.md or skills.

Record rough sizes (lines/tokens) per artifact — total always-loaded weight is itself a finding when large.

Step 1 — Audit each artifact against the rubric

For each artifact, walk the six shifts and cross-cutting modes. For every finding record: file:line, quote, shift #, severity, proposed rewrite (the actual replacement text — or "delete", with one line of why it's safe).

Severity:

  • high — conflicts between layers; rules that block correct behavior; stale facts an agent would act on.
  • medium — overconstraint, redundancy, always-loaded bulk that belongs in a skill.
  • low — style, phrasing, minor bloat.

Verify before you flag: a claim of staleness must be checked against the repo (does that script exist? does that command run?); a claim of redundancy must cite both locations.

Step 2 — Probe for unknowns (the gaps docs don't show)

Auditing text only finds what's written. Now find what's missing:

  • Blind-spot pass: skim the actual territory — build config, CI, scripts, the weirdest-looking directories — and list load-bearing facts that appear in no doc. Each is a candidate "unknown known" to add.
  • Knowledge quiz: write 5–10 questions a fresh agent must answer to work here safely ("how do I run one test?", "what must never be committed?", "which service is the source of truth for X?"). Answer each using only the audited docs. Unanswerable questions = gaps; wrong answers = stale docs.
  • Git check: git log --oneline -20 -- <doc> — a CLAUDE.md untouched for months in an active repo is presumptively stale; recent churny areas of the codebase with no doc coverage are presumptive gaps.

Step 3 — Report

Deliver a findings report (markdown in the repo, e.g. docs/agent-context-audit-YYYY-MM-DD.md, or just in the reply if the user prefers):

  1. Scorecard — per artifact: size, finding counts by severity, one-line verdict (keep / trim / restructure / delete).
  2. Findings table — file:line, quote, shift #, severity, proposed rewrite.
  3. Gaps — missing unknown-knowns from Step 2, each with proposed text and the home it belongs in (CLAUDE.md vs skill vs tool description).
  4. Projected result — estimated always-loaded context before → after.

Lead with the top 3–5 highest-value changes; don't bury a layer conflict under twenty style nits.

Step 4 — Apply (with approval)

Ask which findings to apply (all high, everything, or cherry-pick). Then:

  • Make the edits exactly as proposed in the report.
  • When moving content out of CLAUDE.md into a skill, create the skill and leave a one-line pointer behind.
  • Keep each change reviewable — don't reflow or rewrite text you didn't flag.
  • If /doctor is available in this Claude Code install, suggest the user also run it as a second opinion on CLAUDE.md/skill sizing.

Anti-patterns for the auditor

  • Deleting a hard rule that guards something genuinely irreversible — the point is removing fake constraints, not real ones.
  • Flagging brevity as a problem — short, dense CLAUDE.md files are the goal.
  • Rewriting voice/style wholesale — preserve the team's phrasing where content is sound.
  • Reporting a finding without a concrete rewrite — every finding must be actionable as written.

Sources: Anthropic, "The new rules of context engineering for Claude 5 generation models" (claude.com/blog); "A field guide to Claude Fable: finding your unknowns" (claude.com/blog).

Frequently asked questions

What does the Agent Context Audit AI skill do?

Audit a repo's agent context — CLAUDE.md files, codebase docs, skills, and tool/MCP designs — against Anthropic's Claude 5 context-engineering guidance ("unhobbling": Anthropic cut ~80% of Claude Code's system prompt with no eval loss). Finds overconstraint, conflicting instructions, redundancy, stale facts, and missing "unknown knowns"; produces a scored findings report with concrete rewrites, then applies approved fixes. Use when someone says "audit my CLAUDE.md", "context audit", "unhobble this repo", "review our agent docs/skills/tools", or after upgrading to Claude 5-generation models.

Why use Agent Context Audit on TypingMind?

Because you install it once and use it with any model. Agent Context Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Context Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AI-Builder-Club/skills/tree/main/skills/agent-context-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Context Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Context Audit?

As many as you like. As long as a model supports skills, you can use Agent Context Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Context Audit AI skill free?

It is published on GitHub by AI-Builder-Club. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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