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ha-review

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分支38,068

更新时间2026年7月2日 19:46

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.

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来源

home-assistant

home-assistant/core

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在 Manus 中运行

Review Code Changes

Analyze the code changes for:

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

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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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

Analyze the code changes for:

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

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.