Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/benmarte/autoimprove/auditnpx skills add benmarte/autoimprove --skill auditgit clone --depth 1 https://github.com/benmarte/autoimproveWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00040 | $0.01536 |
| Opus 5 | $0.00020 | $0.00768 |
| Sonnet 5 | $0.00008 | $0.00307 |
| Haiku 4.5 | $0.00004 | $0.00154 |
Grade A, and why
audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Skill
Pre-flight
- Check
.claude/autoimprove/config.mdexists. If not, stop: "Run /autoimprove:setup first." - Check git is available and working tree is clean.
Step 1: Run Measurement Suite
Run the measure skill to get the composite score and per-metric breakdown. Capture both the scores AND the raw command output for each metric.
For each metric defined in the config:
- Run the command (e.g.,
tsc --noEmit 2>&1,pnpm test 2>&1,pnpm lint 2>&1) - Record the score (using the measure skill's scoring logic)
- Also capture the raw output for deficiency counting
Step 2: Count Individual Deficiencies
Parse the raw output from each command to count specific issues:
- Type errors: Count lines matching error patterns (e.g.,
error TSfor TypeScript,error:for Rust). Group by file. - Build: Pass/fail only — no granular count. If build fails, it becomes top priority.
- Tests: Count passing vs total from test runner output. Identify failing test names if any.
- Lint: Count warning/error lines from linter output. Group by rule if possible.
Step 3: Calculate Efficiency
For each metric with a gap (score < max):
- Point gap = max_weight - current_score
- Estimated iterations =
ceil(issue_count / issues_per_iteration)using these heuristics:- Type errors: ~3 per iteration (clustered in files)
- Lint warnings: ~2.5 per iteration
- Tests (new): ~1 per iteration
- Build fix: ~1-2 iterations
- These are initial estimates — actual results will vary
- Efficiency = point_gap / estimated_iterations
- Estimated tokens = estimated_iterations × 22000 (rough average per iteration)
Read weights dynamically from .claude/autoimprove/config.md. If a metric is not applicable, skip it and redistribute weight as the measure skill already handles.
Sort areas by efficiency (highest pts/iteration first).
Step 4: Display Report
Print the audit report:
━━━ Codebase Audit ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Current Score: XX/100
[Metric 1]: XX/WW [progress bar] (gap pts to max) or ✓ maxed
[Metric 2]: XX/WW [progress bar] (gap pts to max) or ✓ maxed
[Metric 3]: XX/WW [progress bar] (gap pts to max) or ✓ maxed
[Metric 4]: XX/WW [progress bar] (gap pts to max) or ✓ maxed
━━━ Fastest Path to 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# Area Gap Issues Est. iterations Efficiency
1 [best area] Xpts N items M iterations X.X pts/iter ← best
2 [next area] Xpts N items M iterations X.X pts/iter
...
Total: ~N iterations to reach 100/100
⚡ Estimated token usage: ~XXXK tokens (rough estimate, actual usage varies)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 154 lines · 40 tokens per session scan A 0ee37b41d39a
audit is a skill published in the GitHub repository benmarte/autoimprove (5 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 1,536 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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