Individual skill
ql-deep-review
Multi-perspective post-implementation review aggregator for quantum-loop. Invokes 2-7 reviewer agents in parallel based on risk score, applies actionability filter, dedups, aggregates with evidence requirements. Use AFTER the per-story two-stage review gates pass and before merging a whole-feature PR to master. Complements ql-review (per-story) with whole-feature review.
skills/ql-deep-review/SKILL.md
Source commit bed463b5db7ec57e30f3c3a47ca1025f95cfe453
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- Observed behavior
- Network or broad shell
- Last checked
- Nov 5, 2026
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- 24
- Repository last updated
- Jun 25, 2026
- License
- MIT
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# ql-deep-review — whole-feature post-implementation review
## Purpose
quantum-loop's built-in two-stage review gate (`ql-review`: spec-compliance → code-quality) operates on ONE story at a time inside ONE worktree. It does not detect:
1. **Cross-story divergence** (e.g., story A uses `'google'` as a secret key while story B uses `'google-api-key'` for the same constant).
2. **Post-merge regressions** (test that was green in isolation breaks after integration).
3. **Drift from original user intent** (paraphrase chain from intent → design → PRD → plan → code).
4. **Low-signal comments** that look like findings but lack evidence (CRA actionability is 0.9-19.2% per Chowdhury 2604.03196; human baseline is ~60%).
`ql-deep-review` closes these gaps with a whole-feature review that runs AFTER all stories in a wave / feature pass the per-story gate.
## When to use