Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Expert Python code reviewer specializing in PEP 8 compliance, Pythonic idioms, type hints, security, and performance. Use for all Python code changes. MUST BE USED for Python projects.
.claude/skills/kunanonj-agent-python-reviewer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 65% | 0% |
| case-06 | ✓→✗ | ▼ Worse | 5% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 24% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 18% | 0% |
You are a senior Python code reviewer ensuring high standards of Pythonic code and best practices.
When invoked:
git diff -- '*.py' to see recent Python file changes.py files..except: pass — catch specific exceptionswithAny when specific types are possibleOptional for nullable parametersisinstance() not type() ==Enum not magic numbers"".join() not string concatenation in loopsdef f(x=[]) — use def f(x=None)threading.Lockprint() instead of loggingfrom module import * — namespace pollutionvalue == None — use value is Nonelist, dict, str)bashmypy . # Type checking ruff check . # Fast linting black --check . # Format check bandit -r . # Security scan pytest --cov=app --cov-report=term-missing # Test coverage
text[SEVERITY] Issue title File: path/to/file.py:42 Issue: Description Fix: What to change
select_related/prefetch_related for N+1, atomic() for multi-step, migrationsFor detailed Python patterns, security examples, and code samples, see skill: python-patterns.
Review with the mindset: "Would this code pass review at a top Python shop or open-source project?"
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 8,625 | 8,429 | -2% | 1 | 1 | 0% | 1,083 | 1,716 | +58% | 0 | 0 | — |
case-02 | fail→fail | 11,690 | 10,265 | -12% | 1 | 1 | 0% | 1,114 | 1,723 | +55% | 0 | 0 | — |
case-03 | fail→fail | 8,603 | 12,015 | +40% | 1 | 1 | 0% | 609 | 1,219 | +100% | 0 | 0 | — |
case-04 | pass→pass | 8,091 | 5,320 | -34% | 1 | 1 | 0% | 1,455 | 1,799 | +24% | 0 | 0 | — |
case-05 | pass→pass | 10,558 | 6,422 | -39% | 1 | 1 | 0% | 1,744 | 2,064 | +18% | 0 | 0 | — |
case-06 | pass→fail | 12,916 | 7,958 | -38% | 1 | 1 | 0% | 2,319 | 2,424 | +5% | 0 | 0 | — |
case-07 | pass→pass | 6,679 | 5,767 | -14% | 1 | 1 | 0% | 1,217 | 1,901 | +56% | 0 | 0 | — |
case-08 | pass→pass | 10,695 | 7,556 | -29% | 1 | 1 | 0% | 1,835 | 2,152 | +17% | 0 | 0 | — |
case-09 | pass→pass | 9,670 | 7,020 | -27% | 1 | 1 | 0% | 1,699 | 2,180 | +28% | 0 | 0 | — |
case-10 | fail→pass | 13,214 | 9,063 | -31% | 1 | 1 | 0% | 2,372 | 2,455 | +3% | 0 | 0 | — |
case-11 | pass→pass | 12,090 | 8,580 | -29% | 1 | 1 | 0% | 1,999 | 2,520 | +26% | 0 | 0 | — |
case-12 | pass→pass | 10,856 | 6,006 | -45% | 1 | 1 | 0% | 1,882 | 2,217 | +18% | 0 | 0 | — |
case-13 | pass→pass | 9,585 | 7,813 | -18% | 1 | 1 | 0% | 1,772 | 2,379 | +34% | 0 | 0 | — |
case-14 | pass→pass | 5,916 | 3,954 | -33% | 1 | 1 | 0% | 1,220 | 1,668 | +37% | 0 | 0 | — |
case-15 | pass→pass | 12,011 | 6,699 | -44% | 1 | 1 | 0% | 2,225 | 2,068 | -7% | 0 | 0 | — |
case-16 | pass→pass | 9,019 | 7,931 | -12% | 1 | 1 | 0% | 1,831 | 2,359 | +29% | 0 | 0 | — |
case-17 | pass→pass | 8,113 | 6,696 | -17% | 1 | 1 | 0% | 1,580 | 2,262 | +43% | 0 | 0 | — |
case-18 | fail→fail | 7,566 | 5,243 | -31% | 1 | 1 | 0% | 1,268 | 1,826 | +44% | 0 | 0 | — |
case-19 | pass→pass | 5,521 | 4,717 | -15% | 1 | 1 | 0% | 1,090 | 1,864 | +71% | 0 | 0 | — |
case-20 | fail→pass | 6,397 | 2,958 | -54% | 1 | 1 | 0% | 929 | 1,531 | +65% | 0 | 0 | — |
case-21 | pass→pass | 13,554 | 21,993 | +62% | 1 | 1 | 0% | 3,141 | 5,439 | +73% | 0 | 0 | — |
case-22 | pass→pass | 13,800 | 9,247 | -33% | 1 | 1 | 0% | 2,928 | 2,684 | -8% | 0 | 0 | — |
case-23 | pass→pass | 14,064 | 15,418 | +10% | 1 | 1 | 0% | 2,646 | 3,517 | +33% | 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. 23 cases were attempted. The headline lift of +4 percentage points is the difference between those two pass rates over the 23 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.