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Get Started Free →Structured code review skill. Use when asked to "review this code", "review this PR", "check this diff", or when acting as a Hydra reviewer. Runs a multi-pass review with specialist focus areas and confidence-gated findings.
.claude/skills/blueberrycongee-code-review/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 80% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 30% | 0% |
Structured multi-pass review. Read the full diff before commenting on anything.
git diff <base>..HEADgit diff --cachedtask description to understand what the change is supposed to do
Read the diff line by line. Flag only real issues:
Always check:
Never flag:
Based on the change type, apply the relevant specialist lens:
If the diff touches tests:
expect(true).toBe(true))?If the diff touches data access:
If the diff touches auth/security:
security-audit skill to the changed codeIf the diff touches UI:
For each finding, assign a confidence level:
Report format per finding:
[severity] file:line — description
Confidence: N/10
Suggestion: specific fix| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 4,924 | 4,193 | -15% | 1 | 1 | 0% | 820 | 1,402 | +71% | 0 | 0 | — |
case-02 | pass→pass | 3,373 | 1,691 | -50% | 1 | 1 | 0% | 535 | 961 | +80% | 0 | 0 | — |
case-03 | pass→pass | 4,774 | 3,622 | -24% | 1 | 1 | 0% | 789 | 1,028 | +30% | 0 | 0 | — |
case-04 | fail→pass | 7,243 | 2,567 | -65% | 1 | 1 | 0% | 1,144 | 1,162 | +2% | 0 | 0 | — |
case-05 | pass→pass | 9,487 | 3,154 | -67% | 1 | 1 | 0% | 1,466 | 1,186 | -19% | 0 | 0 | — |
case-06 | pass→pass | 8,201 | 3,383 | -59% | 1 | 1 | 0% | 1,208 | 1,240 | +3% | 0 | 0 | — |
case-15 | pass→pass | 6,127 | 2,219 | -64% | 1 | 1 | 0% | 1,007 | 1,034 | +3% | 0 | 0 | — |
case-07 | pass→pass | 11,171 | 5,499 | -51% | 1 | 1 | 0% | 1,695 | 1,490 | -12% | 0 | 0 | — |
case-08 | fail→pass | 7,685 | 3,531 | -54% | 1 | 1 | 0% | 1,258 | 1,299 | +3% | 0 | 0 | — |
case-09 | pass→pass | 7,981 | 4,385 | -45% | 1 | 1 | 0% | 1,312 | 1,089 | -17% | 0 | 0 | — |
case-10 | pass→pass | 4,790 | 3,259 | -32% | 1 | 1 | 0% | 780 | 1,219 | +56% | 0 | 0 | — |
case-11 | pass→pass | 9,633 | 4,519 | -53% | 1 | 1 | 0% | 1,576 | 1,330 | -16% | 0 | 0 | — |
case-12 | pass→pass | 9,007 | 4,487 | -50% | 1 | 1 | 0% | 1,490 | 1,440 | -3% | 0 | 0 | — |
case-13 | pass→pass | 9,755 | 3,101 | -68% | 1 | 1 | 0% | 1,381 | 1,166 | -16% | 0 | 0 | — |
case-14 | pass→pass | 5,630 | 3,428 | -39% | 1 | 1 | 0% | 929 | 1,165 | +25% | 0 | 0 | — |
case-16 | pass→pass | 11,504 | 2,493 | -78% | 1 | 1 | 0% | 1,728 | 1,064 | -38% | 0 | 0 | — |
case-17 | fail→pass | 5,822 | 2,276 | -61% | 1 | 1 | 0% | 923 | 1,072 | +16% | 0 | 0 | — |
case-18 | pass→pass | 9,526 | 6,791 | -29% | 1 | 1 | 0% | 1,607 | 1,813 | +13% | 0 | 0 | — |
case-19 | pass→pass | 15,115 | 9,390 | -38% | 1 | 1 | 0% | 2,580 | 2,264 | -12% | 0 | 0 | — |
case-20 | pass→pass | 15,789 | 9,118 | -42% | 1 | 1 | 0% | 2,612 | 2,216 | -15% | 0 | 0 | — |
case-21 | pass→pass | 13,595 | 11,586 | -15% | 1 | 1 | 0% | 2,740 | 3,083 | +13% | 0 | 0 | — |
case-22 | pass→pass | 10,928 | 8,570 | -22% | 1 | 1 | 0% | 1,847 | 2,294 | +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. The headline lift of +14 percentage points is the difference between those two pass rates over the 22 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.