Revue de code Home Assistant

Sélectionnez ce skill pour revoir en profondeur les modifications de code Home Assistant, fournir des retours constructifs et vérifier les points clés avec des sous-agents parallèles.

Spar Skills Guide Bot
DeveloppementAvancé
0029/08/2026
Claude Code
#code-review#home-assistant#git#quality-verification

Recommandé pour


name: ha-review description: Reviews Home Assistant code changes and provides constructive feedback. Should be used when a review is requested to provide a consistent review behavior and output format. This skill can be used for code reviews in general, not just for GitHub pull requests.

Review Code Changes

Scope:

  • Unless instructed otherwise, review the full changes (the ones from the branch plus uncommitted ones) against the target branch. Resolve the base to an available ref (prefer upstream/<base>, then origin/<base>, then local <base>) and review git diff "$(git merge-base "$BASE_REF" HEAD)"; use dev as the default base.

Analyze the code changes for:

  • Code quality and style consistency
  • Potential bugs or issues
  • Performance implications
  • Security concerns
  • Test coverage
  • Documentation updates if needed

Quality scale:

  • If the changes include a quality_scale.yaml file, run a subagent to verify all the added or modified rules, following the ha-quality-scale-verify skill.
  • Include the verification results in the final review comments.

Verification:

  • After the review, run parallel subagents for each finding to double-check it.
  • Spawn up to a maximum of 10 parallel subagents at a time.
  • Gather the results from the subagents and summarize them in the final review comments.

IMPORTANT:

  • Just review. DO NOT make any changes.
  • Be constructive and specific in your comments.
  • Suggest improvements where appropriate.
  • No need to run tests or linters, just review the code changes.
  • No need to highlight things that are already good.

Output format:

  • List specific comments for each file/line that needs attention.
  • In the end, summarize with an overall assessment (approve, request changes, or comment) and bullet point list of changes suggested, if any.
    • Example output:
      Overall assessment: request changes.
      - [CRITICAL] sensor.py:143 - Memory leak
      - [PROBLEM] data_processing.py:87 - Inefficient algorithm
      - [SUGGESTION] test_init.py:45 - Improve x variable name
      
    • Make sure to include the file and line number when possible in the bullet points.
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