Context Audit Skill Skill

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

Agent Skills: Context Audit Skill

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Skill Files