Agent skill

audit-agentsmd

Audit AGENTS.md (or CLAUDE.md) for length, attention-zone misplacement, and extraction opportunities. Produces a prioritized report with reordering and extraction recommendations. Use when AGENTS.md exceeds 400 lines, after major additions, or when agent instruction compliance is poor.

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npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/audit-agentsmd

SKILL.md

Audit AGENTS.md

Analyzes an AGENTS.md (or CLAUDE.md) file for structural problems that cause AI agents to miss critical instructions — primarily length bloat and attention-zone misplacement.

Why This Matters

LLMs exhibit a well-documented "lost in the middle" effect: instructions at the start (primacy) and end (recency) of long context get significantly higher attention than those in the middle. A 500+ line AGENTS.md has a ~60% "dead zone" where behavioral rules are likely to be ignored. This audit identifies misplacements and recommends fixes.

Workflow

Copy this checklist and track progress:

Audit Progress:
- [ ] Step 1: Locate and measure the file
- [ ] Step 2: Parse section structure
- [ ] Step 3: Classify each section
- [ ] Step 4: Map to attention zones
- [ ] Step 5: Identify misplacements
- [ ] Step 6: Recommend extractions
- [ ] Step 7: Output report

Step 1: Locate and Measure

Find the target file. Check in order:

  1. Argument path (if provided via $ARGUMENTS)
  2. ./AGENTS.md
  3. ./CLAUDE.md

Record the line count. Flag if over 500 lines (Anthropic's recommended maximum for SKILL.md files, equally applicable to AGENTS.md when inlined via @).

If the file is under 300 lines, report "No structural issues — file is within optimal range" and stop.

Step 2: Parse Section Structure

Identify every ## heading. For each section, record:

  • Section name
  • Start line number
  • End line number (start of next section or EOF)
  • Line count
  • Whether it contains code blocks (count code block lines separately)

Use Grep to find headings, then Read to measure each section.

Step 3: Classify Each Section

Classify every section into one of two categories using the rules in EXTRACTION_RULES.md:

Category Description Extraction Safety
Behavioral Rules, guardrails, policies agents must always follow UNSAFE to extract — must stay inline
Reference Lookup material needed only for specific task types SAFE to extract to docs/

Key heuristic: If the section contains words like NEVER, MUST, ALWAYS, "do not", or establishes a rule that applies to every task — it is behavioral. If it contains code examples, schema definitions, or step-by-step processes needed only situationally — it is reference.

Step 4: Map to Attention Zones

Using the total line count, calculate the attention zones per ATTENTION_MODEL.md:

Zone Range Attention Level
Primacy First ~20% of lines Strong
Fade ~20-35% Moderate, declining
Dead zone ~35-65% Weakest
Recovery ~65-80% Moderate, rising
Recency Last ~20% of lines Strong

Map each section's midpoint to a zone.

Step 5: Identify Misplacements

Flag these anti-patterns:

Critical misplacements (fix first):

  • Behavioral section in the dead zone → agent likely ignores the rule
  • Reference section in the primacy zone → wastes highest-attention position on lookup material

Medium misplacements:

  • Behavioral section in fade/recovery zone → rule may be partially followed
  • Large code blocks (>20 lines) in primacy zone → displaces behavioral content

Low priority:

  • Reference section in recency zone → not harmful but wastes prime position
  • Duplicate/overlapping sections → consolidation opportunity

Step 6: Recommend Extractions

For each reference section, evaluate extraction safety:

  1. Safe to extract: Pure reference — code examples, schema details, process templates, syntax guides. Claude reads these on-demand when encountering a relevant task.
  2. Partially extractable: Contains both a behavioral rule AND detailed process. Keep the rule stub inline (3-5 lines), extract the process details.
  3. Must stay inline: Pure behavioral — guardrails, session workflows, policies. If removed, agents won't read the linked file proactively.

See EXTRACTION_RULES.md for the full classification checklist.

Calculate projected line count after recommended extractions.

Step 7: Output Report

markdown
# AGENTS.md Audit Report

Generated: {date}
File: {path}
Lines: {count} ({over/under} 500-line target)
Sections: {count}
Code block lines: {count} ({percent}% of file)

## Attention Zone Map

| Zone | Lines | Sections | Issues |
|------|-------|----------|--------|
| Primacy (1–{N}) | {list} | {count of behavioral vs reference} |
| ...

## Critical Misplacements

{For each: section name, current zone, recommended zone, why}

## Extraction Candidates

| Section | Lines | Category | Action |
|---------|-------|----------|--------|
| {name} | {N} | Safe to extract | → docs/{filename}.md |
| {name} | {N} | Partial | Keep {N}-line stub, extract {N} lines |
| {name} | {N} | Must stay inline | — |

## Recommended Section Order

{Numbered list showing optimal ordering:}
1. {Behavioral sections → primacy zone}
2. {Core patterns → early-middle}
3. {Reference/situational → middle}
4. {Process/workflow → late}
5. {Reminders section → EOF}

## Projected Result

| Metric | Before | After |
|--------|--------|-------|
| Total lines | {N} | {N} |
| Behavioral in dead zone | {N} sections | 0 |
| Reference in primacy zone | {N} sections | 0 |

## Reminders Section (Recommended)

If the file exceeds 300 lines, recommend adding a "## Reminders (Critical)"
section at EOF that repeats the 5-6 most important behavioral rules in
one-line form. This exploits recency bias to reinforce primacy-zone rules.

Error Handling

Situation Action
No AGENTS.md or CLAUDE.md found Report "No agent instruction file found" and stop
File under 300 lines Report "No structural issues" and stop
File uses @ imports Note that imported content is inlined — audit the combined effective content
CLAUDE.md contains only @AGENTS.md Audit AGENTS.md (it's the real content)

REMINDER: The goal is not to make AGENTS.md as short as possible — it's to ensure behavioral rules are in high-attention zones and reference material is either in low-attention zones or extracted to on-demand files. A 450-line file with good ordering beats a 300-line file missing critical context.

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