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