evaluate-findings

Solid

Critically assess external feedback (code reviews, AI reviewers, PR comments) and decide which suggestions to apply using adversarial verification. Use when the user asks to "evaluate findings", "assess review comments", "triage review feedback", "evaluate review output", or "filter false positives".

Code & Development 336 stars 26 forks Updated 6 days ago MIT

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Skill Content

# Evaluate Findings Assess external feedback (code reviews, AI suggestions, PR comments) with adversarial verification. Triage findings into binary verdicts. Do not apply fixes. ## Step 1: Assess Each Finding For each finding: 1. **Read the referenced code** at the mentioned location — include the full function or logical block, not just the flagged line 2. **Check for early exits:** - If the finding references code that no longer exists or has since changed, skip it and note that the code has diverged. - If two findings conflict with each other, skip both and document the conflict. 3. **Determine scope** — clarify whether the issue was introduced by the PR/changeset or is pre-existing. Present this distinction explicitly so the user can decide whether it belongs in this PR's scope. - Pre-existing issues in earlier commits on the same feature branch are in-scope by default — the entire branch is one coherent unit of work. - Out-of-scope findings that are genuinely useful and have low blast radius should be accepted. Only skip out-of-scope findings when the change is disproportionate to the current work. 4. **Verify the claim** against the actual code — does the issue genuinely exist? 5. **Assign a verdict and confidence:** | Verdict | Criteria | |---------|----------| | **Apply** | The finding is real: clear bug, missing check, genuine improvement, style violation matching project conventions | | **Skip** | False positive, subjective preference, reviewer is w...

Details

Author
tobihagemann
Repository
tobihagemann/turbo
Created
3 months ago
Last Updated
6 days ago
Language
Python
License
MIT

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