m2ai-portfolio Token Burn Auditor
Audits your current Claude Code environment to identify where tokens are being wasted. Measures actual overhead, flags bloat, and produces actionable reduction targets.
Trigger
Use when the user says "audit my tokens", "where are my tokens going", "token burn", "session overhead", "why is my context so full", "token waste", or asks about optimizing their Claude Code environment for token efficiency.
Phase 1: Measure Current Overhead
Run the following diagnostics:
Skill frontmatter scan -- count total skills in
~/.claude/skills/and estimate token load from all SKILL.md files:find ~/.claude/skills -name "SKILL.md" | wc -l find ~/.claude/skills -name "SKILL.md" -exec wc -w {} + | tail -1CLAUDE.md chain -- measure the full instruction chain that loads on every session:
~/.claude/CLAUDE.md(global)- Project-level
CLAUDE.md - Any
.claude/CLAUDE.mdin project - Memory files referenced by these
MCP server count -- check how many MCP servers are configured
Hook overhead -- count configured hooks
Session history weight -- if JSONL session logs exist, sample recent ones for size
Phase 2: Calculate Token Budget
Estimate token consumption using these approximations:
- 1 word ~ 1.3 tokens (English text)
- 1 line of code ~ 10 tokens
- YAML/JSON frontmatter ~ 1.5x word count in tokens
Produce a breakdown table:
| Source | Est. Words | Est. Tokens | % of 200K |
|---|---|---|---|
| Global CLAUDE.md | X | X | X% |
| Project CLAUDE.md | X | X | X% |
| Skill descriptions (N skills) | X | X | X% |
| MCP server schemas (N servers) | X | X | X% |
| Hooks config | X | X | X% |
| Memory files | X | X | X% |
| Total static overhead | X | X | X% |
Note: 200K is the effective context for Sonnet/Opus. Adjust if the user specifies a different limit.
Phase 3: Identify Waste
Flag issues in priority order:
- Critical (>10% of context): Any single source consuming >20K tokens
- Warning (5-10%): Sources between 10K-20K tokens
- Info (<5%): Normal overhead, no action needed
Specific patterns to flag:
- Skills that haven't been invoked in 30+ days (check
skill_invocations.dbif available) - Duplicate or overlapping skill coverage
- CLAUDE.md sections that repeat information already in skills
- MCP servers that are configured but unused
- Memory files that have grown beyond 500 words
Phase 4: Recommend Reductions
For each flagged issue, provide:
- What to do -- specific action (archive skill, trim CLAUDE.md section, etc.)
- Token savings -- estimated tokens recovered
- Risk -- what capability is lost if this is removed
Produce a summary:
Current static overhead: ~XXK tokens (XX% of 200K context)
Achievable reduction: ~XXK tokens
Post-optimization: ~XXK tokens (XX% of 200K context)
Phase 5: Before/After Delta (Optional)
If the user makes changes based on recommendations, re-run Phase 1-2 and show the delta:
Before: XXK tokens (XX%)
After: XXK tokens (XX%)
Saved: XXK tokens
Output Format
Keep output concise and scannable. Use tables over paragraphs. Lead with the number, not the explanation.
Source Attribution
Technique derived from Nate's Newsletter (2026-04-02): "You're Loading 66,000 Tokens of Plugins Before You Even Type" -- token waste taxonomy and environment-level auditing approach.