agent-context-audit — unhobble this repo's agent context
SkillFiles & storageFind out whether the instructions and docs your AI coding assistant reads are helping it or getting in its way. Once added, your AI can audit a project's agent context — CLAUDE.md files, codebase docs, skills, and tool designs — against Anthropic's Claude 5 context-engineering guidance, flagging rules that over-restrict the AI, instructions that conflict with each other, and redundant guidance. Acting on the findings leaves your AI with a leaner, clearer set of instructions to follow.
Available today. Use it from your connected AI after setup.
No other account needed.
Add the skill, then ask your AI to audit the project whose agent instructions you want reviewed. You'll get a list of the issues it finds so you know what to trim or rewrite.
Then ask your AI: use the agent-context-audit — unhobble this repo's agent context skill
What your AI can do with it
- Audit CLAUDE.md files and codebase docs in a project
- Flag conflicting instructions that could confuse the AI
- Spot redundant or repeated guidance
- Find overconstraint — rules that limit the AI more than needed
- Check skills and tool designs against Anthropic's context-engineering guidance
What this skill tells your AI
The instructions your AI receives, as published by ai-builder-club/skills in skills/agent-context-audit/SKILL.md and read by ahel’s review.
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.
- 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).
- 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 | completedteaches more than three worked examples. - 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.
- 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.
- 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.
- 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.mdonly if the user asks for a global audit. AlsoAGENTS.md,.cursorrules,.github/copilot-instructions.mdif 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):
- Scorecard — per artifact: size, finding counts by severity, one-line verdict (keep / trim / restructure / delete).
- Findings table — file:line, quote, shift #, severity, proposed rewrite.
- Gaps — missing unknown-knowns from Step 2, each with proposed text and the home it belongs in (CLAUDE.md vs skill vs tool description).
- 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
/doctoris 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).
Signals
- GitHub stars
- 1k
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- Last commit
- Jul 2026
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agent-context-audit- Source
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