本文へ移動
cccskills
無料GitHub で公開

review-python

Comprehensive Python/FastAPI backend code review with optional parallel agents

インストール方法を見る

含まれるファイル(1)

  • SKILL.md9.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Backend Code Review

Hard gates (sequence)

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

GatePass condition
G1 — Diff scopeStep 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/typeFor 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 skillsThe review-verification-protocol, python-code-review, and fastapi-code-review skills are loaded before Step 6 substantive review.
G4 — Evidence per issueStep 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 contractFindings use sequential numbering, every issue has FILE:LINE, and the Verdict follows Step 8 (Critical/Major only block; Minor/Informational do not).

Arguments

  • --parallel: If the agent supports subagents, run a specialized subagent per technology area (otherwise reviews run sequentially with identical output)
  • 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 <changed_files>
fi

# Check if mypy config exists and run it
if [ -f "pyproject.toml" ] || [ -f "mypy.ini" ]; then
    mypy <changed_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 the review-verification-protocol skill and keep its checklist in mind throughout the review.

Step 5: Load Skills

Load each applicable skill (read its SKILL.md) before reviewing its domain.

Always load:

Conditionally load based on detection:

ConditionSkill
Test files changedpytest-code-review
Pydantic-AI detectedpydantic-ai-common-pitfalls
SQLAlchemy detectedsqlalchemy-code-review
Postgres detectedpostgres-code-review

Step 6: Review

If the agent supports subagents, dispatch one per technology area in parallel; otherwise run the same areas sequentially, producing identical output.

Sequential (default, or when subagents are unavailable):

  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, when the agent supports subagents):

  1. Detect all technologies upfront
  2. Dispatch one subagent per technology area
  3. Each subagent loads its skill and reviews its domain
  4. Wait for all subagents
  5. Consolidate findings

Before Flagging Optimization or Pattern Issues

  1. Check project conventions (e.g. AGENTS.md or 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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when you need to mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit "[ADR]" tags. Triggers on "what decisions did we make", "extract decisions from this chat", "find the choices in our discussion", or "summarize architectural decisions". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents — use adr-writing or write-adr for that.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on "write the ADR", "format as MADR", "check ADR quality", "mark gaps in ADR". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Vercel AI Elements for workflow UI components. Use when building chat interfaces, displaying tool execution, showing reasoning/thinking, or creating job queues. Triggers on ai-elements, Queue, Confirmation, Tool, Reasoning, Shimmer, Loader, Message, Conversation, PromptInput.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Reviews App Intents code for intent structure, entities, shortcuts, and parameters. Use when reviewing code with import AppIntents, @AppIntent, AppEntity, AppShortcutsProvider, or @Parameter.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when the user wants a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

existential-birds のスキルをすべて見る

このスキルの問題を報告する