fleet-memory/.claude/skills/code-review/SKILL.md
Nicolò Boschi f8f62030e3
Add /code-review skill for automated code quality checks (#805)
* feat: add /code-review skill for automated code quality checks

Adds a Claude Code skill that reviews changes against project standards:
missing tests, dead code, type safety, lint, and CLAUDE.md conventions.
CLAUDE.md now instructs contributors to run /code-review after implementation.

* refactor: move code standards from CLAUDE.md into /code-review skill

Single source of truth for coding conventions (Python style, type safety,
TypeScript style) is now .claude/skills/code-review.md. CLAUDE.md points
to the skill for reading before coding and running after implementation.

* feat: add code comments convention to /code-review skill

Require comments explaining non-trivial technical decisions, with history
of previous approaches. Review step checks for missing reasoning comments,
stale comments, and undocumented approach changes.

* fix: move skill to directory structure for Claude Code discovery

Claude Code requires .claude/skills/<name>/SKILL.md, not loose .md files.

* feat: add branch hygiene checks to /code-review skill

Review step 1 now verifies branch is based on recent origin/main and
all commits are relevant to the feature. Unrelated commits flagged as
must-fix.
2026-03-31 10:21:25 +02:00

7.4 KiB

name description user_invocable
code-review Review changed code against project standards. Checks for missing tests, dead code, type safety, lint issues, and coding conventions. Run after completing any implementation work. true

Code Review

Review all changed code against the project's quality standards and coding conventions.

Code Standards

Read and internalize these standards before writing code. The review steps below verify compliance.

Python Style

  • Python 3.11+, type hints required
  • Async throughout (asyncpg, async FastAPI)
  • Pydantic models for request/response
  • Ruff for linting (line-length 120)
  • No Python files at project root - maintain clean directory structure
  • Never use multi-item tuple return values - prefer dataclass or Pydantic model for structured returns

Type Safety with Pydantic Models

NEVER use raw dict types for structured data. Always use Pydantic models:

  • Use Pydantic BaseModel for all data structures passed between functions
  • Add @field_validator for type coercion (e.g., ensuring datetimes are timezone-aware)
  • Avoid dict.get() patterns - use typed model attributes instead
  • Parse external data (JSON, API responses) into Pydantic models at the boundary
  • This catches type errors at parse time, not deep in business logic
# BAD - error-prone dict access
def process(data: dict) -> str:
    return data.get("name", "")  # No validation, silent failures

# GOOD - typed and validated
class UserData(BaseModel):
    name: str
    created_at: datetime

    @field_validator("created_at", mode="before")
    @classmethod
    def ensure_tz_aware(cls, v):
        if isinstance(v, str):
            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
        if v.tzinfo is None:
            return v.replace(tzinfo=timezone.utc)
        return v

def process(data: UserData) -> str:
    return data.name  # Type-safe, validated at construction

TypeScript Style

  • Next.js App Router for control plane
  • Tailwind CSS with shadcn/ui components

Code Comments

  • Always comment non-trivial technical decisions with the reasoning behind the choice. If someone would ask "why is it done this way?", there should be a comment.
  • Keep comments up to date with history — when changing an approach, update the comment to explain what was tried before and why it was changed. Comments serve as a tracker of previous implementations that likely had problems.
  • Don't comment obvious code — only where the "why" isn't self-evident from the code itself.
# BAD - no context for future readers
results = await asyncio.gather(*tasks, return_exceptions=True)

# GOOD - explains the non-obvious choice
# Use return_exceptions=True to avoid cancelling sibling tasks on failure.
# Previously we used TaskGroup but it cancelled all tasks when one failed,
# causing partial writes that left orphaned entity links (see #412).
results = await asyncio.gather(*tasks, return_exceptions=True)

Branch Hygiene

  • Always start new feature branches from origin/main — rebase to ensure a clean base.
  • Only include commits relevant to the PR/branch/feature — no unrelated changes. If the branch contains commits that don't belong, they must be removed before merging.

General Principles

  • Don't add features, refactor code, or make "improvements" beyond what was asked
  • Don't add unnecessary error handling for impossible scenarios
  • Don't create helpers or abstractions for one-time operations
  • No backwards-compatibility hacks (unused vars, re-exports, "removed" comments)
  • Three similar lines of code is better than a premature abstraction

Review Steps

1. Check branch hygiene

  • Run git log --oneline main..HEAD to list all commits on the branch.
  • Verify every commit is relevant to the feature/PR. Flag any unrelated commits.
  • Check the branch is based on a recent origin/main (no stale base).

2. Identify changed files

Run git diff --name-only HEAD (unstaged) and git diff --cached --name-only (staged) to get all changed files. If there are no local changes, diff against the base branch using git diff main...HEAD --name-only and git diff main...HEAD to review all commits on the current branch.

3. Run linters

./scripts/hooks/lint.sh

Report any failures. Do NOT fix them yourself — just report.

4. Check for dead code

For each changed Python file, check for:

  • Unused imports (Ruff should catch these, but verify)
  • Functions/methods/classes that were added but are never called from anywhere
  • Variables assigned but never read
  • Commented-out code blocks that should be removed

For each changed TypeScript file, check for:

  • Unused imports
  • Unused variables or functions
  • Commented-out code

5. Check type safety (Python)

For each changed Python file, check for violations:

  • No raw dict for structured data — should use Pydantic models
  • No multi-item tuple returns — should use dataclass or Pydantic model
  • Missing type hints on function parameters and return types
  • Missing @field_validator for datetime fields that should be timezone-aware

6. Check for missing tests

For each new or significantly changed function/endpoint/class:

  • Check if there is a corresponding test addition or update
  • New API endpoints MUST have integration tests
  • New utility functions MUST have unit tests
  • Bug fixes SHOULD have a regression test

Flag any new logic that lacks test coverage.

7. Check API consistency

If any files in hindsight-api-slim/hindsight_api/api/ were changed:

  • Were the OpenAPI specs regenerated? (./scripts/generate-openapi.sh)
  • Were the client SDKs regenerated? (./scripts/generate-clients.sh)
  • Were the control plane proxy routes updated? (hindsight-control-plane/src/app/api/)

8. Check code comments

For each non-trivial change:

  • New non-obvious logic — is there a comment explaining the reasoning?
  • Changed approach — does the comment include what was done before and why it changed?
  • Stale comments — do existing comments near the changed code still accurately describe the behavior?

9. Review against other coding standards

Check the diff for violations of the standards listed above:

  • Python files at project root (not allowed)
  • Missing async patterns (should be async throughout)
  • Pydantic models for request/response
  • Line length > 120 chars
  • New features/code beyond what was asked (over-engineering)
  • Unnecessary error handling for impossible scenarios
  • Premature abstractions or speculative helpers
  • Backwards-compatibility hacks (unused vars, re-exports, "removed" comments)

10. Report findings

Present a clear summary organized by severity:

Must fix — issues that will break CI or violate hard project rules:

  • Unrelated commits on the branch
  • Lint failures
  • Missing type hints on public functions
  • Raw dict usage for structured data
  • Missing tests for new endpoints

Should fix — issues that hurt code quality:

  • Dead code / unused imports missed by linter
  • Missing tests for non-trivial utility functions
  • Over-engineering beyond the task scope

Note — observations that may or may not need action:

  • API changes that might need client regeneration
  • Patterns that deviate from nearby code style

For each finding, include the file path, line number, and a brief explanation.

Do NOT auto-fix any issues. Report all findings and let the user decide what to address. If there are no findings, confirm the code looks good.