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Get Started Free →AI-powered senior code review engine that delivers production-grade PR analysis. Performs multi-dimensional review covering correctness, security vulnerabilities, performance bottlenecks, code style, architectural design patterns, and accessibility compliance. Supports 30+ languages with framework-specific rules for React, Vue, Angular, Next.js, Express, Django, Spring Boot, and more. Generates structured review reports with severity levels (critical/warning/info), line-level annotations, and ac
.claude/skills/gitstq-super-reviewer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 86% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 94% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 141% | 0% |
| case-09 | ✓→✗ | ▼ Worse | 441% | 0% |
> The most comprehensive AI code review skill. One skill replaces 10+ specialized review tools.
Most code review skills only check one dimension (style or security). Super Reviewer performs a 7-dimensional holistic review that mimics how a senior staff engineer reviews code in production:
No setup needed. Simply say any of:
The skill auto-detects language and framework.
For each changed file, run ALL of the following checks:
expect(true).toBe(true))Generate a structured review report using the following format:
## Code Review Report
### Summary
- Files reviewed: X
- Critical issues: X | Warnings: X | Suggestions: X
- Overall assessment: [APPROVE / REQUEST_CHANGES / COMMENT]
### Critical Issues (Must Fix)
#### [SEC-001] SQL Injection Vulnerability (Line 42)
**File**: `src/api/users.ts`
**Severity**: CRITICAL
**Description**: User input `req.body.name` is directly interpolated into SQL query without sanitization.
**Impact**: An attacker could read, modify, or delete any data in the database.
**Fix**:// Before (vulnerable) const query = SELECT * FROM users WHERE name = '${req.body.name}'; // After (safe) const query = 'SELECT FROM users WHERE name = ?'; db.query(query, req.body.name]);
#### [PERF-001] N+1 Query Problem (Line 78-85)
**File**: `src/services/order.ts`
**Severity**: WARNING
**Description**: Inside a loop, individual queries fetch user data for each order.
**Impact**: With 1000 orders, this generates 1000+ database queries instead of 1.
**Fix**: Use JOIN or batch loading with `IN` clause.
### Warnings (Should Fix)
...
### Suggestions (Nice to Have)
...
### Positive Highlights
- Good error handling in `src/utils/validation.ts`
- Proper use of TypeScript generics in repository pattern
- Comprehensive test coverage for payment module (95%)Use these strict severity levels:
| Level | Color | Meaning | Action Required | |-------|-------|---------|----------------| | CRITICAL | Red | Security vulnerability, data loss risk, crash bug | MUST fix before merge | | WARNING | Yellow | Performance issue, potential bug, bad practice | SHOULD fix before merge | | INFO | Blue | Style improvement, readability suggestion | Consider fixing |
For each issue, always provide:
key props in listsuseEffectuseMemo/useCallback usage for expensive operationsref vs reactive vs computed)v-if + v-for on same element (anti-pattern)use*)err ignores)The skill maintains an internal database of 200+ known anti-patterns across languages, including:
By default, generate a full report. User can request:
"brief review" - Only critical and warning issues"security only" - Only security dimension"performance only" - Only performance dimension"explain like I'm junior" - Simpler explanations with more contextThis skill works with any AI coding agent that supports the SKILL.md standard:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 30,003 | 22,106 | -26% | 1 | 1 | 0% | 6,242 | 6,721 | +8% | 0 | 0 | — |
case-02 | fail→fail | 7,240 | 7,209 | -0% | 1 | 1 | 0% | 905 | 3,026 | +234% | 0 | 0 | — |
case-03 | fail→fail | 4,175 | 7,027 | +68% | 1 | 1 | 0% | 673 | 3,242 | +382% | 0 | 0 | — |
case-04 | pass→pass | 13,942 | 11,355 | -19% | 1 | 1 | 0% | 3,027 | 4,429 | +46% | 0 | 0 | — |
case-05 | pass→pass | 4,703 | 6,263 | +33% | 1 | 1 | 0% | 755 | 2,970 | +293% | 0 | 0 | — |
case-06 | pass→pass | 8,613 | 9,190 | +7% | 1 | 1 | 0% | 1,673 | 3,867 | +131% | 0 | 0 | — |
case-07 | fail→fail | 6,949 | 8,588 | +24% | 1 | 1 | 0% | 706 | 3,153 | +347% | 0 | 0 | — |
case-08 | fail→fail | 7,410 | 8,275 | +12% | 1 | 1 | 0% | 962 | 3,280 | +241% | 0 | 0 | — |
case-09 | pass→fail | 3,821 | 7,163 | +87% | 1 | 1 | 0% | 586 | 3,169 | +441% | 0 | 0 | — |
case-10 | pass→pass | 17,841 | 17,224 | -3% | 1 | 1 | 0% | 3,235 | 5,591 | +73% | 0 | 0 | — |
case-11 | pass→pass | 13,320 | 10,253 | -23% | 1 | 1 | 0% | 2,310 | 3,922 | +70% | 0 | 0 | — |
case-12 | fail→pass | 11,243 | 6,702 | -40% | 1 | 1 | 0% | 1,726 | 3,207 | +86% | 0 | 0 | — |
case-13 | pass→pass | 11,486 | 7,898 | -31% | 1 | 1 | 0% | 1,832 | 3,437 | +88% | 0 | 0 | — |
case-14 | pass→pass | 4,475 | 3,789 | -15% | 1 | 1 | 0% | 725 | 2,718 | +275% | 0 | 0 | — |
case-15 | pass→pass | 5,393 | 5,650 | +5% | 1 | 1 | 0% | 1,008 | 3,136 | +211% | 0 | 0 | — |
case-16 | pass→pass | 4,204 | 4,040 | -4% | 1 | 1 | 0% | 745 | 2,761 | +271% | 0 | 0 | — |
case-17 | pass→pass | 6,100 | 6,491 | +6% | 1 | 1 | 0% | 1,089 | 3,184 | +192% | 0 | 0 | — |
case-18 | pass→pass | 10,719 | 8,010 | -25% | 1 | 1 | 0% | 2,021 | 3,667 | +81% | 0 | 0 | — |
case-19 | pass→pass | 12,915 | 6,564 | -49% | 1 | 1 | 0% | 1,025 | 2,764 | +170% | 0 | 0 | — |
case-20 | fail→pass | 7,933 | 3,607 | -55% | 1 | 1 | 0% | 1,395 | 2,710 | +94% | 0 | 0 | — |
case-21 | fail→pass | 6,061 | 2,490 | -59% | 1 | 1 | 0% | 1,033 | 2,490 | +141% | 0 | 0 | — |
case-22 | pass→pass | 7,279 | 5,913 | -19% | 1 | 1 | 0% | 1,326 | 3,032 | +129% | 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. The headline lift of +14 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
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.