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Code learning and audit framework. Analyze any codebase — new, legacy, or AI-generated — and produce educational explanations or architectural audits.
Passed 7 of 16 test cases with the skill, and 4 of 16 without it.
and 1 more in the full report
4 fixed5 passed either way12 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
Refactor `src/legacy/parser.js` to use modern async/await syntax and output a clean code patch.
| Checked | Without skill | With skill |
|---|---|---|
| The response outputs modified source code or a patch rather than educational deep-dive markdown files | Missed | Passed |
The instructions your model reads when the skill loads.
AntiVibe generates learning-focused explanations or architectural audits of any code — AI-generated, legacy, or otherwise. It helps developers understand:
Works on any codebase — you don't need recent git history or AI-authored files.
Use AntiVibe when:
/antivibe or "deep dive"Output saved to deep-dive/ folder as markdown:
deep-dive/
├── auth-system-2026-01-15.md
├── api-layer-2026-01-15.md
└── database-models-2026-01-15.md
The exact sections depend on the output mode (see Output Mode):
| Section | compact (default) | full |
|---|---|---|
| Overview — what the code does and why it exists | ✅ | ✅ |
| Key Components / Concepts — design patterns, algorithms, CS concepts used | ✅ | ✅ |
| Code Walkthrough — file-by-file, line-by-line notes | — | ✅ |
| Learning Resources — curated docs, tutorials, videos | — | ✅ |
| Related Code — links to other files in the codebase | — | ✅ |
test cases passed, vs 4 / 16 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.