Context Audit Skill

Audit a codebase's context engineering health and identify optimization opportunities.

Purpose

A focused agent is a performant agent. This skill helps you understand what's consuming your context window and where to apply the R&D framework.

When to Use

Starting work on a new codebase
Agent performance feels sluggish
Context warnings appearing
Before optimizing context strategy
Periodic context health checks

Audit Process

1. Memory File Analysis

Scan for CLAUDE.md and related memory files:

Check:
- Root CLAUDE.md size (target: <2KB)
- Number of imports
- Per-directory CLAUDE.md files
- Total memory file tokens

Score memory health:

Size	Score	Assessment
<1KB	Excellent	Minimal and focused
1-2KB	Good	Within target range
2-5KB	Needs Review	Growing, audit content
>5KB	Action Required	Bloated, needs R&D

2. MCP Server Analysis

Check MCP configurations:

Check:
- .mcp.json existence
- Number of MCP servers configured
- Per-server token estimate (2-5% each)
- Active vs unused servers

Score MCP health:

Servers	Score	Assessment
0	Excellent	No MCP bloat
1-2	Good	Targeted usage
3-5	Review	May be over-provisioned
>5	Action Required	Likely consuming 15%+

3. Commands Analysis

Review .claude/commands/:

Check:
- Number of commands
- Command complexity (simple vs complex)
- Priming commands present?
- Task-type coverage

Score command health:

Commands	Score	Assessment
Has priming	Excellent	Dynamic context loading
No priming	Needs Attention	Relying on static memory

4. Hooks Analysis

Check for context-consuming hooks:

Check:
- Number of hooks
- Hook event types
- Potential context injection

5. Overall Context Score

Calculate overall context engineering score:

Component	Weight	Max Points
Memory Files	30%	30
MCP Configuration	25%	25
Command Infrastructure	25%	25
Context Patterns	20%	20

Output Format

{
  "score": 75,
  "grade": "B",
  "components": {
    "memory": {
      "score": 20,
      "max": 30,
      "files_found": ["CLAUDE.md"],
      "total_tokens": 1500,
      "issues": ["No priming commands detected"]
    },
    "mcp": {
      "score": 25,
      "max": 25,
      "servers_found": 0,
      "estimated_consumption": "0%"
    },
    "commands": {
      "score": 15,
      "max": 25,
      "count": 5,
      "has_priming": false,
      "issues": ["Missing /prime command"]
    },
    "patterns": {
      "score": 15,
      "max": 20,
      "issues": ["No output styles defined"]
    }
  },
  "recommendations": [
    "Create /prime command for dynamic context loading",
    "Reduce CLAUDE.md size by delegating to priming",
    "Consider output styles for token efficiency"
  ]
}

Grading Scale

Score	Grade	Status
90-100	A	Elite context engineering
80-89	B	Good practices, minor optimizations
70-79	C	Functional, needs attention
60-69	D	Significant issues
<60	F	Context bloat, major rework needed

Recommendations Framework

Based on findings, recommend:

For Memory Bloat (Reduce)

Identify content that can move to priming commands
Flag outdated or contradictory guidance
Suggest minimal CLAUDE.md structure

For Missing Infrastructure (Delegate)

Recommend priming command creation
Suggest output styles for verbosity control
Propose agent expert patterns

Cross-References

@rd-framework.md - Reduce and Delegate strategies
@context-layers.md - Understanding context composition
@context-rot-vs-pollution.md - Diagnosing context problems
@context-priming-patterns.md - Dynamic context loading

Version History

v1.0.0 (2025-12-26): Initial release

Last Updated

Date: 2025-12-26 Model: claude-opus-4-5-20251101

context-audit