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Get Started Free →Systematic PR review with blast-radius analysis, security scanning, and breaking-change and test-coverage deltas. Use when reviewing PRs that touch shared libraries, APIs, database schemas, auth, or security-sensitive code.
.claude/skills/borghei-pr-review-expert/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 1% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 211% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -19% | 0% |
Structured, systematic code review for GitHub PRs and GitLab MRs. Goes beyond style nits to perform blast-radius analysis, security vulnerability scanning, breaking-change detection, test-coverage delta calculation, and performance impact assessment. Produces reviewer-ready reports with prioritized findings categorized as must-fix, should-fix, and suggestions.
Keywords: PR review, code review, pull request, merge request, blast radius, security scan, breaking changes, test coverage, review checklist, code quality
| Tool | Purpose | Command | |------|---------|---------| | blast_radius_calculator.py | Calculate PR blast radius from import chains / dependency trees of changed files | git diff --name-only main...HEAD \| python scripts/blast_radius_calculator.py --root src | | diff_analyzer.py | Analyze a diff for risk indicators (large files, sensitive paths, config/breaking/security patterns) | gh pr diff $PR \| python scripts/diff_analyzer.py --json | | review_checklist_generator.py | Generate a tailored review checklist from changed file types/patterns | git diff --name-only main...HEAD \| python scripts/review_checklist_generator.py |
Load the reference that matches the task — keep this file lean and pull detail on demand:
gh/grep command catalog for context gathering, blast radius, security scan, breaking-change detection, coverage delta, and performance impact (plus the blast-radius severity table and coverage rules). Read when actually performing a review.This skill covers:
This skill does NOT cover:
engineering/saas-scaffolder or engineering/migration-architect for code generationengineering/dependency-auditor for dependency-level vulnerability scanningengineering/ci-cd-pipeline-builder for pipeline designengineering/performance-profiler for profiling and optimization guidance| Skill | Integration | Data Flow | |-------|-------------|-----------| | engineering/dependency-auditor | Run dependency audit before reviewing PRs that add or upgrade packages | Audit report feeds into the Security section of the review report | | engineering/ci-cd-pipeline-builder | Embed review checklist gates into CI pipelines as automated PR checks | Checklist items become pass/fail signals in the pipeline | | engineering/performance-profiler | Escalate N+1 and unbounded query findings for detailed profiling | Flagged code paths from review become profiling targets | | engineering/migration-architect | Validate database migration safety for PRs that include schema changes | Migration risk assessment supplements the Breaking Changes section | | engineering/release-manager | Feed breaking change detection results into release notes and changelogs | Detected breaking changes auto-populate release documentation | | engineering/api-design-reviewer | Cross-reference API endpoint changes with API design standards | API review findings merge into the Blast Radius and Breaking Changes sections |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-07 | fail→pass | 9,488 | 1,868 | -80% | 1 | 1 | 0% | 1,483 | 1,493 | +1% | 0 | 0 | — |
case-01 | fail→fail | 16,315 | 30,966 | +90% | 1 | 1 | 0% | 1,562 | 6,369 | +308% | 0 | 0 | — |
case-02 | fail→pass | 8,236 | 17,074 | +107% | 1 | 1 | 0% | 976 | 3,033 | +211% | 0 | 0 | — |
case-03 | fail→fail | 10,159 | 8,452 | -17% | 1 | 1 | 0% | 1,503 | 1,570 | +4% | 0 | 0 | — |
case-04 | fail→fail | 6,353 | 7,171 | +13% | 1 | 1 | 0% | 945 | 2,246 | +138% | 0 | 0 | — |
case-05 | fail→fail | 9,104 | 8,346 | -8% | 1 | 1 | 0% | 1,662 | 2,644 | +59% | 0 | 0 | — |
case-06 | pass→pass | 9,773 | 5,173 | -47% | 1 | 1 | 0% | 1,616 | 2,017 | +25% | 0 | 0 | — |
case-08 | fail→pass | 11,299 | 5,337 | -53% | 1 | 1 | 0% | 1,925 | 2,048 | +6% | 0 | 0 | — |
case-09 | fail→pass | 11,254 | 2,362 | -79% | 1 | 1 | 0% | 1,831 | 1,559 | -15% | 0 | 0 | — |
case-10 | pass→pass | 7,361 | 2,855 | -61% | 1 | 1 | 0% | 1,045 | 1,556 | +49% | 0 | 0 | — |
case-11 | pass→pass | 10,931 | 2,597 | -76% | 1 | 1 | 0% | 1,629 | 1,555 | -5% | 0 | 0 | — |
case-12 | fail→pass | 13,352 | 2,865 | -79% | 1 | 1 | 0% | 2,004 | 1,633 | -19% | 0 | 0 | — |
case-13 | pass→pass | 16,140 | 17,164 | +6% | 1 | 1 | 0% | 2,598 | 2,904 | +12% | 0 | 0 | — |
case-14 | fail→pass | 19,483 | 12,628 | -35% | 1 | 1 | 0% | 2,841 | 3,172 | +12% | 0 | 0 | — |
case-15 | fail→pass | 16,456 | 12,964 | -21% | 1 | 1 | 0% | 2,401 | 3,032 | +26% | 0 | 0 | — |
case-16 | pass→pass | 16,576 | 17,978 | +8% | 1 | 1 | 0% | 2,587 | 3,895 | +51% | 0 | 0 | — |
case-17 | fail→pass | 10,767 | 1,733 | -84% | 1 | 1 | 0% | 1,690 | 1,479 | -12% | 0 | 0 | — |
case-18 | fail→pass | 9,610 | 1,691 | -82% | 1 | 1 | 0% | 1,422 | 1,440 | +1% | 0 | 0 | — |
case-19 | fail→pass | 11,394 | 2,570 | -77% | 1 | 1 | 0% | 1,502 | 1,566 | +4% | 0 | 0 | — |
case-20 | pass→pass | 12,249 | 2,840 | -77% | 1 | 1 | 0% | 1,793 | 1,560 | -13% | 0 | 0 | — |
case-21 | pass→pass | 6,326 | 2,222 | -65% | 1 | 1 | 0% | 919 | 1,524 | +66% | 0 | 0 | — |
case-22 | pass→pass | 13,858 | 2,924 | -79% | 1 | 1 | 0% | 2,083 | 1,585 | -24% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +45 percentage points is the difference between those two pass rates over the 21 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.