majiayu000 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