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Detects codebase bloat via dead code, duplication, complexity, and doc bloat scans.
Passed 16 of 19 test cases with the skill, and 9 of 19 without it.
and 5 more in the full report
8 fixed10 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
When reporting the per-session overhead cost of multiple always-on skills, which frontmatter metadata field should be summed across those skills?
| Checked | Without skill | With skill |
|---|---|---|
| Sums the `estimated_tokens` metadata field across the always-on skills. | Missed | Passed |
The instructions your model reads when the skill loads.
Systematically detect and eliminate codebase bloat through progressive analysis tiers.
| Category | Examples |
|---|---|
| Code | Dead code, God classes, Lava flow, duplication |
| AI-Generated | Tab-completion bloat, vibe coding, hallucinated deps |
| Documentation | Redundancy, verbosity, stale content, slop |
| Dependencies | Unused imports, dependency bloat, phantom packages |
| Git History | Stale files, low-churn code, massive single commits |
/bloat-scan
Detects: Large files, stale code, old TODOs, commented blocks, basic duplication
/bloat-scan --level 2 --focus code # or docs, deps
Adds: Static analysis (Vulture/Knip), git churn hotspots, doc similarity
/bloat-scan --level 3 --report audit.md
Adds: Cross-file redundancy, dependency graphs, readability metrics
| Do | Don't |
|---|---|
| Context usage > 30% | Active feature development |
| Quarterly maintenance | Time-sensitive bugs |
test cases passed, vs 9 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.