adversarial-reviewlisted
Install: claude install-skill Eliyce/paqad-ai
## What It Does
Runs a risk-first review that looks for correctness defects, rollback hazards, missing coverage, and weak assumptions before work is treated as complete.
## Use This When
Use this after design or implementation when the change is medium or high risk, customer-facing, security-sensitive, or hard to roll back safely.
## Inputs
- Read the request, accepted plan, code or doc diffs, and any claimed verification results.
- Read the most relevant canonical docs for the changed behavior.
- Read `references/review-dimensions.md` before structuring findings.
## Procedure
1. Run `scripts/digest-evidence.sh` to flatten `.paqad/session/verification-evidence.json` into a `gate | category | file:line | ac_id | message` table; cite this table directly when anchoring findings.
2. Review the diff and digest against the dimensions in `references/review-dimensions.md` — correctness, safety, performance, docs drift, verification sufficiency.
3. Prioritize user-visible failure, data corruption, migration risk, and contract breakage above style.
4. Draft the output following `assets/output.template.md`. Severity tags must come from `assets/severity-ranks.txt`. Order findings non-increasing by severity.
5. Validate with `scripts/lint-findings.sh` before returning — exit 0 means the structural contract is met.
## Output Contract
- Match `assets/output.template.md`: `## Findings` heading, one bullet per finding tagged `**Critical|High|Medium|Low**`, each citing concrete `file: