CODE_QUALITY_CHECK

CODE QUALITY CHECK

Owner: QA

Goal

Audit a target (file set or directory) against .github/checklists/code-quality-checklist.yaml and deliver a Markdown report with evidence and actionable fixes.

Workflow

1. Load Inputs

• Read target_path (file, directory, or list).
• Respect checklist meta.scope (default: changed files + directly impacted dependencies).

2. Evaluate Rules

• Process rules top-down.

• Apply activation_hint and stop_condition:

  • Stop on first HIGH unless --exhaustive is requested.
  • Stop if findings_count > 25.

• Enforce modern, pragmatic (non-enterprise) code quality standards by ensuring the checklist includes (or is extended to include) rules in these areas:

  • Correctness & Safety (baseline): input validation, error handling, edge cases, deterministic behavior, safe defaults.
  • Readability & Maintainability: naming, small focused functions, clear control flow, avoid cleverness, consistent conventions.
  • Negative Space (readability-by-absence):
    • Visual: paragraph rule (blank lines), avoid wall-of-code, consistent formatting.
    • Structural: minimize public API surface, keep helpers private/internal.
    • Absence: prefer deleting/reducing code; avoid “just in case” abstractions.
  • DRY / Single Source of Truth: eliminate duplicated logic/data, centralize constants and domain rules.
  • KISS: simplest working solution, avoid unnecessary patterns/framework layers.
  • YAGNI: reject speculative features and premature generalization.
  • SOLID (lightweight): primarily SRP and clear boundaries; avoid over-engineering in small projects.
  • Complexity control: avoid deep nesting, use guard clauses/early returns, limit cyclomatic complexity.
  • Testability: dependency boundaries, pure functions where possible, deterministic units, easy-to-mock seams.
  • Testing (pragmatic): unit tests for business logic, integration tests for critical paths; avoid low-value test bloat.
  • Type Safety (if applicable): avoid any, narrow types, prefer explicit interfaces, validate at boundaries.
  • Performance (only when justified): avoid obvious inefficiencies, but do not micro-optimize without evidence.
  • Security (baseline): secrets handling, injection risks, auth/permission checks, safe logging.
  • Observability (baseline): meaningful errors, structured logs, no noisy logging, clear failure modes.
  • Dependencies: avoid unnecessary deps, keep versions updated, remove unused.
  • Docs (just enough): READMEs for modules, docstrings only where behavior is non-obvious.

• For each rule:

  • Mark PASS/FAIL with evidence (file path + line/snippet).
  • For FAIL, provide a concrete fix that matches the rule’s fix guidance.
  • Preserve severity and autofix flags from the checklist.

• When proposing fixes, prioritize changes that maximize clarity and negative space:

  • Prefer guard clauses over nested conditionals.
  • Prefer extracting small, named helpers over long inline logic.
  • Prefer removing dead/duplicate code over refactoring into new abstractions.
  • Prefer consolidating domain rules/constants into a single place.

3. Apply Lean Guards

• Do not expand scope beyond meta.scope.
• Prefer small, safe fixes.
• Refactor only when required by a rule.
• Skip large migrations.
• Do not enforce enterprise-grade process (e.g., mandatory ADRs, heavy frameworks) unless explicitly requested.
• Prefer "small surface area" design: fewer public functions/classes, fewer configuration knobs.
• Avoid recommending new abstractions unless they remove duplication or reduce complexity.
• If a standard conflicts (e.g., DRY vs KISS), explain the trade-off and choose the simpler option unless the duplication is high-risk.

4. Produce Report

• Follow the checklist output_schema.
• Include:
  • Summary: counts by severity + decision.
  • Findings: list items with id, severity, file, symbol (if known), evidence, fix, autofix.
  • Suggestions: targeted next steps based on findings.

5. Save Output

• Write Markdown to docs/qa/reports/code-quality-{{target_slug}}.md.
• Create directories if missing.

Anti-Patterns

• Do not mark PASS without evidence.
• Do not invent IDs, symbols, or file paths.
• Do not change scope unless the user explicitly requests it.
• Do not recommend “best practice” patterns without tying them to a concrete risk.
• Do not propose premature architecture (microservices, event buses, complex DDD) for small projects.
• Do not optimize performance without evidence (profiling, metrics, clear bottleneck).
• Do not add tests that duplicate coverage or only assert implementation details.
• Do not increase API surface area unless it improves usability and reduces coupling.