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研究检索需要联网github未标认证来源可访问许可证需确认审计异常

fix-review修复评论

Agent Skill

fix-review 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

635

周安装

27

GitHub Stars

4

下载量

222
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:fix-review(修复评论)
来源仓库:https://github.com/s-hiraoku/synapse-a2a
仓库路径:skills/fix-review
安装命令:
npx skills add https://github.com/s-hiraoku/synapse-a2a --skill fix-review
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/s-hiraoku/synapse-a2a --skill fix-review

简介

fix-review 用于查找、检索和筛选相关信息,适合根据关键词或任务场景快速定位候选结果。

  • 适用于研究检索类任务,如问题排查和解决方案筛选。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,具体用法需参考原始 README 和项目文档。
  • 安装前建议确认权限范围和维护状态,注意是否涉及联网、命令执行或文件读写操作。
  • fix-review 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Fix CodeRabbit Review Comments

This skill automatically addresses CodeRabbit review comments by fetching inline comments, classifying their severity, verifying each finding against the current code, applying targeted fixes, and pushing the result.

Usage

/fix-review           # Auto-fix actionable CodeRabbit comments
/fix-review --dry-run # Show what would be fixed without applying
/fix-review --all     # Also attempt to fix suggestions and nitpicks (not just bugs/style)

Workflow

Step 1: Fetch PR and Review Data

Get the current PR details:

gh pr view --json number,url,headRefName

If no PR exists, report "No PR found for the current branch." and stop.

Get the repo identifier:

gh repo view --json nameWithOwner -q '.nameWithOwner'

Step 2: Fetch CodeRabbit Reviews and Comments

Fetch all reviews from CodeRabbit:

gh api "repos/<owner>/<repo>/pulls/<pr_number>/reviews" \
  --jq '[.[] | select(.user.login == "coderabbitai[bot]")]'

Fetch inline comments (including outside-diff and nitpick comments):

gh api "repos/<owner>/<repo>/pulls/<pr_number>/comments" \
  --jq '[.[] | select(.user.login == "coderabbitai[bot]") | {id, path, line, original_line, diff_hunk, body, created_at, subject_type}]'

Also fetch PR-level review body comments (CodeRabbit often posts a summary review with actionable items in the review body itself):

gh api "repos/<owner>/<repo>/pulls/<pr_number>/reviews" \
  --jq '[.[] | select(.user.login == "coderabbitai[bot]" and .body != "") | {id, body, state}]'

If no CodeRabbit reviews or comments exist, report "No CodeRabbit review comments found on this PR." and stop.

Step 3: Parse Comment Sections

CodeRabbit structures its review body into distinct sections. Parse each section:

  • Inline comments (In \@file: blocks): These reference specific files and line ranges
  • Outside diff comments: Comments about code not in the current diff but affected by changes
  • Nitpick comments: Low-priority style/improvement suggestions
  • Duplicate comments: Comments that repeat across review rounds (may already be fixed)

For each comment, extract:

  • File path (from @file reference or inline comment path)
  • Line range (approximate — verify against current code)
  • Referenced symbols (function names, class names, CSS selectors mentioned)
  • The specific ask (what change is requested)

Step 4: Verify Each Finding Against Current Code

CRITICAL STEP — Do not skip this. For each comment:

  1. Read the referenced file at the specified path
  2. Locate the exact code referenced by the comment using:

- Line numbers (may have shifted — use symbol names as anchors) - Function/class/variable names mentioned in the comment - Code snippets quoted in the comment body

  1. Confirm the issue still exists in the current code:

- If the code has already been fixed (e.g., by a previous commit), mark as "already resolved" and skip - If the referenced code no longer exists (refactored away), mark as "no longer applicable" and skip - If the issue exists as described, proceed to classification

  1. Check for consistency with implementation — when a comment says "option X should be Y", verify by reading the actual argparse/CLI definition, not just the referenced doc line. Cross-reference:

- CLI option names: check synapse/cli.py argparse definitions - Function signatures: check the actual function definition - API endpoints: check the server route definitions - CSS classes: check both .css and .js files that reference them

Step 5: Classify Comments

For each verified comment, classify into categories:

Priority rule: If a comment contains a ``` `suggestion ``` code block, it is always auto-fixable regardless of category — apply the suggestion directly. Classification then only affects reporting priority.

Bug/Security (auto-fix):

  • CodeRabbit header markers: ⚠️ Potential issue, 🐛 Bug, 🔒 Security
  • Keywords in body (case-insensitive): bug, error, security, vulnerability, incorrect, wrong, crash, leak, null, undefined, race condition, injection, xss, overflow, missing check, unhandled, exception, type error, not defined
  • Pattern: comment describes *what is broken*, not what could be improved
  • Edge case: if body contains both a bug keyword AND consider/might want to, classify as Bug (err on side of safety)

Inconsistency (auto-fix):

  • Comments about documentation/code mismatch (e.g., docs say --id but CLI uses --card-id)
  • Comments about DOM structure inconsistency (e.g., meta inside <pre> in one renderer but outside in another)
  • Comments about naming differences between files
  • Always verify both sides: read both the doc AND the implementation to determine which is correct

Style (auto-fix):

  • CodeRabbit header markers: 🧹 Nitpick, 📝 Style
  • Keywords in body: style, format, naming, convention, readability, import, unused, lint, whitespace, indentation, nit:, nit, typo, spelling, consistent, redundant
  • Delegation: if the issue is about Python formatting/linting (import order, unused imports, line length), delegate to ruff check --fix and ruff format rather than manual editing
  • Edge case: nit: prefix always means Style regardless of other keywords

Suggestion (report only, unless --all flag):

  • CodeRabbit header markers: 🛠️ Refactor suggestion, 💡 Suggestion, 📖 Note
  • Keywords in body: consider, might want to, could be, alternative, refactor, performance, optimization, simplify, extract, pattern, architecture
  • Default: any comment that does not match Bug/Security or Style patterns falls here
  • Edge case: suggest alone is NOT a Style keyword — it goes to Suggestion unless a suggestion code block is present (which makes it auto-fixable)

Step 6: Apply Fixes

For each actionable comment (Bug/Security, Inconsistency, and Style categories):

  1. Read the file at the specified path
  2. Locate the exact code using symbol names (not just line numbers, which shift)
  3. Analyze the comment body for:

- Code suggestion blocks (``` `suggestion ``` markers): Apply the suggested code directly - Descriptive feedback: Understand the issue and implement an appropriate fix

  1. Apply the fix to the file
  2. After each fix, verify the change is correct — re-read the modified code to confirm it matches the intent

For Style issues that ruff can handle:

ruff check --fix synapse/ tests/
ruff format synapse/ tests/

If --dry-run flag is provided: show each comment with its classification and proposed fix, but do NOT modify files, commit, or push.

Step 7: Local Verification

After applying all fixes:

ruff check synapse/ tests/
ruff format synapse/ tests/ --check
pytest

If any check fails after fixing:

  • Attempt one targeted correction
  • If it still fails, revert the problematic fix and report it as unresolvable

Step 8: Commit and Push

Stage and commit the changes:

git add -u
git commit -m "fix: address CodeRabbit review comments

Resolved:
- <summary of each fixed comment>

Reported (not auto-fixed):
- <summary of suggestion-only comments>"
git push

Step 9: Report Summary

Report what was done:

  • Number of comments addressed (by category: bug, inconsistency, style, suggestion)
  • Number of comments verified as already resolved or not applicable
  • Files modified
  • Comments left as suggestions (not auto-fixed)
  • Whether all local checks pass
  • Link to the PR for manual review of remaining suggestions

Error Handling

  • No PR found: Report and stop
  • No CodeRabbit comments: Report and stop
  • gh API errors: Report the error and suggest manual review
  • Fix causes test failure: Revert that specific fix, report the issue
  • Never force-push: Always use git push
  • Max 1 fix cycle: One pass of fixes. If issues remain, report for manual handling

Safety

  • Only fixes Bug/Security, Inconsistency, and Style categories by default
  • Suggestions are reported but not auto-fixed (unless --all)
  • Every finding is verified against current code before fixing
  • Cross-references implementation (argparse, routes, etc.) when fixing doc/code mismatches
  • Local verification before pushing
  • One retry per failed fix, then revert
  • CodeRabbit's profile: "chill" means suggestions are advisory, not blocking

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.02%
按下载量换算82

Claude

27.99%
按下载量换算62

Cursor

18.89%
按下载量换算42

Gemini CLI

8.3%
按下载量换算18

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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