multi-pass-self-critique
Skill36 starsChanged 5 months agoMITView on GitHub
Meta-skill for /audit-strict and high-stakes audits. Run two independent passes with different starting contexts, then keep only consensus findings. Aggressively cuts false positives.
In this repository44 other files it gives its agents.
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The file
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1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
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Distinctive · estimate
This author's own choices. Worth a look.
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- Distinctive: line 20
Read the code line-by-line.
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Files
Multi-pass self-critique (meta-skill)
This skill governs the protocol for high-precision audits where false positives are unacceptable.
When to use
/audit-strictinvocations- Pre-launch audits where the user has explicitly opted into slower, higher-precision review
- Re-audits where prior tools have been noisy
The protocol
Pass A — Skill-driven, bottom-up
Read the code line-by-line. Apply the vuln-skill library. Emit candidate findings with reasoning traces.
Pass B — Exploit-driven, top-down
Fresh context. Pretend you have no prior findings. Approach the contract as an attacker: "What would I steal here? What's the cheapest exploit?" Emit findings.
Compare
For each Pass-A finding, check Pass-B:
- Did Pass B independently identify this issue (under any name)?
- Does Pass B's exploit narrative match this issue's mechanism?
For each Pass-B finding, check Pass-A:
- Did the skill library flag this?
Categorize
| Pass A | Pass B | Result |
| ✓ | ✓ | Consensus — Confidence HIGH, keep |
| ✓ | ✗ | Single-source A — Confidence MEDIUM, keep with note |
| ✗ | ✓ | Single-source B — Confidence MEDIUM, keep with note |
| ✗ | ✗ | Not reported |
Synthesize
Output the consensus findings as primary, single-source findings as secondary. Be explicit about which is which.
Why this works
- Each pass has different blind spots. Skill-based misses novel patterns; exploit-based misses subtle CWE patterns.
- Their intersection is the high-precision set.
- Their union is the high-recall set. Sometimes you want union; for
/audit-strict, you want intersection.
Anti-patterns
- Sharing findings between passes. Pass B must not see Pass A's findings; that defeats independence.
- Counting "Pass A finds X, Pass A also finds X in a different file" as consensus. Same pass = same blind spots.
- Using the same model temperature for both passes. Vary the approach, not just the seed.
Output
Multi-pass audit:
Pass A (skill-driven) findings: 12
Pass B (exploit-driven) findings: 9
Consensus (both): 7 ← HIGH confidence
Pass-A-only (no exploit found): 5 ← MEDIUM, flagged for review
Pass-B-only (skills missed): 2 ← MEDIUM, possibly novel patterns
Final report:
→ 7 HIGH-confidence findings (action recommended)
→ 7 MEDIUM-confidence findings (requires user judgment)Related
- [[confidence-scoring]] — output of this skill drives the confidence label
- [[known-good-comparison]] — third axis: also check against reference impls
- /audit-strict (command)
Does it work?
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Your trail
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