← Back to Skills Marketplace
anderskev

Review Python

by Kevin Anderson · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ✓ Security Clean
95
Downloads
0
Stars
1
Active Installs
2
Versions
Install in OpenClaw
/install review-python
Description
Comprehensive Python/FastAPI backend code review with optional parallel agents
README (SKILL.md)

Backend Code Review

Hard gates (sequence)

Advance only when each pass condition is objectively satisfied (prevents linter-owned false positives and ungrounded findings):

Gate Pass condition
G1 — Diff scope Step 1 command has been run; the changed .py paths are enumerated in writing (list may be empty — if empty, state that explicitly and do not invent Python findings).
G2 — Linters before manual style/type For ruff and mypy: either no project config exists for that tool, or it was run on the changed files and you captured pass/fail (exit code or clear tool output). Do not add manual style or type findings for rules those tools already enforce when configured.
G3 — Protocol and base skills beagle-python:review-verification-protocol, beagle-python:python-code-review, and beagle-python:fastapi-code-review are loaded before Step 6 substantive review.
G4 — Evidence per issue Step 7 checks are satisfied for each reported issue before it appears in the final list (re-read source, search references for “unused”, confirm framework handling for “missing”, verify syntax against current docs).
G5 — Output contract Findings use sequential numbering, every issue has FILE:LINE, and the Verdict follows Step 8 (Critical/Major only block; Minor/Informational do not).

Arguments

  • --parallel: Spawn specialized subagents per technology area
  • Path: Target directory (default: current working directory)

Step 1: Identify Changed Files

Pass (G1): Capture the command output (or equivalent) as your authoritative changed-.py set before Steps 2–3.

git diff --name-only $(git merge-base HEAD main)..HEAD | grep -E '\.py$'

Step 2: Verify Linter Status

CRITICAL: Run project linters BEFORE flagging any style or type issues. Pass (G2): You may only proceed to Step 3 after each configured linter has been run on the changed files or you have recorded why it was skipped (missing config).

# Check if ruff config exists and run it
if [ -f "pyproject.toml" ] || [ -f "ruff.toml" ]; then
    ruff check \x3Cchanged_files>
fi

# Check if mypy config exists and run it
if [ -f "pyproject.toml" ] || [ -f "mypy.ini" ]; then
    mypy \x3Cchanged_files>
fi

Rules:

  • If a linter passes for a specific rule (e.g., line length), DO NOT flag that issue manually
  • Linter configuration is authoritative for style rules
  • Only flag issues that linters cannot detect (semantic issues, architectural problems)

Why: Analysis of 24 review outcomes showed 4 false positives (17%) where reviewers flagged line-length violations that ruff check confirmed don't exist. The linter's configuration reflects intentional project decisions.

Step 3: Detect Technologies

# Detect Pydantic-AI
grep -r "pydantic_ai\|@agent\.tool\|RunContext" --include="*.py" -l | head -3

# Detect SQLAlchemy
grep -r "from sqlalchemy\|Session\|relationship" --include="*.py" -l | head -3

# Detect Postgres-specific
grep -r "psycopg\|asyncpg\|JSONB\|GIN" --include="*.py" -l | head -3

# Check for test files
git diff --name-only $(git merge-base HEAD main)..HEAD | grep -E 'test.*\.py$'

Step 4: Load Verification Protocol

Load beagle-python:review-verification-protocol skill and keep its checklist in mind throughout the review.

Step 5: Load Skills

Use the Skill tool to load each applicable skill (e.g., Skill(skill: "beagle-python:python-code-review")).

Always load:

  • beagle-python:python-code-review
  • beagle-python:fastapi-code-review

Conditionally load based on detection:

Condition Skill
Test files changed beagle-python:pytest-code-review
Pydantic-AI detected beagle-ai:pydantic-ai-common-pitfalls
SQLAlchemy detected beagle-python:sqlalchemy-code-review
Postgres detected beagle-python:postgres-code-review

Step 6: Review

Sequential (default):

  1. Load applicable skills
  2. Review Python quality issues first
  3. Review FastAPI patterns
  4. Review detected technology areas
  5. Consolidate findings

Parallel (--parallel flag):

  1. Detect all technologies upfront
  2. Spawn one subagent per technology area with Task tool
  3. Each agent loads its skill and reviews its domain
  4. Wait for all agents
  5. Consolidate findings

Before Flagging Optimization or Pattern Issues

  1. Check CLAUDE.md for documented intentional patterns
  2. Check code comments around the flagged area for "intentional", "optimization", or "NOTE:"
  3. Trace the code path before claiming missing coverage or inconsistent handling
  4. Consider framework idioms - what looks wrong generically may be correct for the framework

Why: Analysis showed rejections where reviewers flagged "inconsistent error handling" that was intentional optimization, and "missing test coverage" for code paths that don't exist.

Step 7: Verify Findings

Pass (G4): No issue ships until all bullets below are true for that issue.

Before reporting any issue:

  1. Re-read the actual code (not just diff context)
  2. For "unused" claims - did you search all references?
  3. For "missing" claims - did you check framework/parent handling?
  4. For syntax issues - did you verify against current version docs?
  5. Remove any findings that are style preferences, not actual issues

Step 8: Review Convergence

Pass (G5): Final markdown matches the Output Format template; verdict line reflects only Critical/Major blockers per scope rules below.

Single-Pass Completeness

You MUST report ALL issues across ALL categories (style, logic, types, tests, security, performance) in a single review pass. Do not hold back issues for later rounds.

Before submitting findings, ask yourself:

  • "If all my recommended fixes are applied, will I find NEW issues in the fixed code?"
  • "Am I requesting new code (tests, types, modules) that will itself need review?"

If yes to either: include those anticipated downstream issues NOW, in this review, so the author can address everything at once.

Scope Rules

  • Review ONLY the code in the diff and directly related existing code
  • Do NOT request new features, test infrastructure, or architectural changes that didn't exist before the diff
  • If test coverage is missing, flag it as ONE Minor issue ("Missing test coverage for X, Y, Z") — do NOT specify implementation details like mock libraries, behaviour extraction, or dependency injection patterns that would introduce substantial new code
  • Typespecs, documentation, and naming issues are Minor unless they affect public API contracts
  • Do NOT request adding new dependencies (e.g. Mox, testing libraries, linter plugins)

Fix Complexity Budget

Fixes to existing code should be flagged at their real severity regardless of size.

However, requests for net-new code that didn't exist before the diff must be classified as Informational:

  • Adding a new dependency (e.g. Mox, a linter plugin)
  • Creating entirely new modules, files, or test suites
  • Extracting new behaviours, protocols, or abstractions

These are improvement suggestions for the author to consider in future work, not review blockers.

Iteration Policy

If this is a re-review after fixes were applied:

  • ONLY verify that previously flagged issues were addressed correctly
  • Do NOT introduce new findings unrelated to the previous review's issues
  • Accept Minor/Nice-to-Have issues that weren't fixed — do not re-flag them
  • The goal of re-review is VERIFICATION, not discovery

Output Format

## Review Summary

[1-2 sentence overview of findings]

## Issues

### Critical (Blocking)

1. [FILE:LINE] ISSUE_TITLE
   - Issue: Description of what's wrong
   - Why: Why this matters (bug, type safety, security)
   - Fix: Specific recommended fix

### Major (Should Fix)

2. [FILE:LINE] ISSUE_TITLE
   - Issue: ...
   - Why: ...
   - Fix: ...

### Minor (Nice to Have)

N. [FILE:LINE] ISSUE_TITLE
   - Issue: ...
   - Why: ...
   - Fix: ...

### Informational (For Awareness)

N. [FILE:LINE] SUGGESTION_TITLE
   - Suggestion: ...
   - Rationale: ...

## Good Patterns

- [FILE:LINE] Pattern description (preserve this)

## Verdict

Ready: Yes | No | With fixes 1-N (Critical/Major only; Minor items are acceptable)
Rationale: [1-2 sentences]

Post-Fix Verification

After fixes are applied, run:

ruff check .
mypy .
pytest

All checks must pass before approval.

Rules

  • Load skills BEFORE reviewing (not after)
  • Number every issue sequentially (1, 2, 3...)
  • Include FILE:LINE for each issue
  • Separate Issue/Why/Fix clearly
  • Categorize by actual severity
  • Run verification after fixes
  • Report ALL issues in a single pass — do not hold back findings for later iterations
  • Re-reviews verify previous fixes ONLY — no new discovery
  • Requests for net-new code (new modules, dependencies, test suites) are Informational, not blocking
  • The Verdict ignores Minor and Informational items — only Critical and Major block approval
Usage Guidance
This skill appears to do what it claims: run repo-local checks and coordinate focused review subagents. Before installing or using it, verify: (1) your agent runtime actually provides git, grep, and the linters referenced (ruff, mypy) since the skill assumes they exist but doesn't declare them; (2) the other skills it loads (beagle‑python:*, beagle-ai:*, etc.) are available and trusted in your environment, because the skill delegates work to them; (3) the agent has only the repository access you intend (it will read code and repo files); and (4) if you enable parallel subagents, expect wider scanning of the repo (ensure no sensitive secrets are present in code or tests). The skill requests no credentials and makes no network/installation calls itself, which lowers risk.
Capability Assessment
Purpose & Capability
The skill's name/description match the runtime instructions (enumerating changed .py files, running linters, and loading focused review skills). One minor incoherence: the SKILL.md expects tools like git, ruff, mypy, grep to be present and runnable, but the registry metadata declares no required binaries. This is a documentation/manifest omission rather than a functional mismatch.
Instruction Scope
Instructions are narrowly scoped to repository inspection and code review (git diff, grep, run configured linters, re-read sources, check comments/docs). It directs loading of other skills and launching subagents for parallel review, which expands review surface but is coherent with the stated 'parallel agents' feature. It does not request unrelated files, environment variables, or external endpoints.
Install Mechanism
This is instruction-only (no install spec, no code files); nothing will be written or downloaded by the skill itself. That is the lowest-risk pattern for install behavior.
Credentials
The skill declares no required environment variables or credentials and its actions (reading repo files, running linters) are proportionate to a code-review task. There is no request for unrelated secrets or config paths.
Persistence & Privilege
always is false and the skill does not request persistent system or cross-skill configuration changes. It does load other skills and may spawn subagents, which increases operational scope but is consistent with its purpose. disable-model-invocation is set, which reduces autonomous model-driven behavior.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install review-python
  3. After installation, invoke the skill by name or use /review-python
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Introduced explicit "hard gates" for review process, establishing objective pass/fail criteria at each review phase. - Added a summary table of gates (G1–G5), requiring explicit linter output and evidence-based issue submission. - Reinforced requirement to enumerate changed files (Step 1) before reviewing, and mandated capturing linter results (Step 2) before flagging manual findings. - Clarified that each issue must satisfy verification steps: re-read source, reference searches, framework checks, and up-to-date syntax validation. - Updated output contract: findings must be numbered, include FILE:LINE, and verdicts only block on Critical/Major issues, in alignment with new gate G5. - No changes to the technology detection, skill loading, or review scope logic.
v1.0.0
Initial release with comprehensive Python/FastAPI backend code review workflow. - Defines sequential and parallel review processes with step-by-step protocols. - Enforces linter results as authoritative before flagging style/type issues. - Dynamically loads additional review skills based on detected project technologies (e.g., SQLAlchemy, Pydantic-AI, Postgres, Pytest). - Introduces clear output formats and strict issue categorization (Critical, Major, Minor, Informational). - Streamlines re-review policy: verification only, no new issues. - Includes checklists for reviewing fixes and convergence for single-pass completeness.
Metadata
Slug review-python
Version 1.0.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 2
Frequently Asked Questions

What is Review Python?

Comprehensive Python/FastAPI backend code review with optional parallel agents. It is an AI Agent Skill for Claude Code / OpenClaw, with 95 downloads so far.

How do I install Review Python?

Run "/install review-python" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Review Python free?

Yes, Review Python is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Review Python support?

Review Python is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Review Python?

It is built and maintained by Kevin Anderson (@anderskev); the current version is v1.0.1.

💬 Comments