GitHub Issue 解决器:自主 Bug 修复代理 - Openclaw 技能库

作者:互联网

2026-04-06

AI教程

什么是 GitHub Issue 解决器?

GitHub Issue 解决器是一个复杂的自主代理,能够简化软件维护的全生命周期。通过直接集成到代码库工作流中,该工具通过识别高影响问题并提出经过验证的修复方案,帮助开发人员管理技术债务。它通过强大的内部安全协议优先考虑安全性和透明度,在 Openclaw 技能生态系统中脱颖而出。

该技能处理从代码库克隆和分支管理到自动化测试和草案 PR 创建的所有事务。它确保在没有明确监督的情况下,不会发生修改受保护分支或访问机密信息等危险操作,使其成为现代开发团队可靠的合作伙伴。通过利用标准化的建议和审计脚本,它为问题解决提供了前后一致且专业的体验。

下载入口:https://github.com/openclaw/skills/tree/main/skills/ashwinhegde19/github-issue-resolver

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

npx clawhub@latest install github-issue-resolver

2. 手动安装

将技能文件夹复制到以下位置之一

全局模式 ~/.openclaw/skills/ 工作区 /skills/

优先级:工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

请帮我使用 Clawhub 安装 github-issue-resolver。如果尚未安装 Clawhub,请先安装(npm i -g clawhub)。

GitHub Issue 解决器 应用场景

  • 根据严重程度和影响,自动发现并为仓库中的开放 Bug 评分。
  • 为定义明确的 GitHub 问题开发代码修复方案,无需手动切换上下文。
  • 为新修复方案生成全面的测试套件,以确保无回归部署。
  • 通过创建详细的草案 PR 供人工审查,简化拉取请求流程。
  • 按顺序管理多个问题,同时确保对采取的每项操作都有审计追踪。
GitHub Issue 解决器 工作原理
  1. 仓库发现:代理根据安全防护栏验证目标仓库,并使用推荐引擎对最适合处理的问题进行评分和展示。
  2. 问题分析与规划:选择问题后,代理将锁定任务,探索代码库,并提交详细的修复计划供用户批准。
  3. 安全实施:获批后,代理将创建专用分支并在安全的沙箱环境中实施修复。
  4. 验证与测试:代理运行现有测试套件并编写新测试以验证修复,如果测试失败,还具有可选的自动回滚功能。
  5. PR 提交:最后,代理生成变更摘要并在 GitHub 上创建草案拉取请求,供人工最终验证。

GitHub Issue 解决器 配置指南

确保你已安装 GitHub CLI (gh) 并已通过身份验证。要开始使用 Openclaw 技能的这一组件,请克隆仓库并安装依赖项:

# 初始化环境
pip install -r requirements.txt
# 配置防护栏
cp guardrails.json.example guardrails.json
# 开始发现流程
python3 scripts/recommend.py /

GitHub Issue 解决器 数据架构与分类体系

该技能维护一个结构化的操作和审计目录,以确保完全透明。

路径 描述
/audit/ 包含为合规性而执行的每项操作和命令的 JSON 日志。
/references/ 代理使用的防护栏文档和 API 评分标准。
guardrails.json 定义允许的范围、路径和操作关口的配置文件。
/tmp/openclaw-work/ 用于仓库克隆和代码操作的临时沙箱目录。
name: github-issue-resolver
description: Autonomous GitHub Issue Resolver Agent with guardrails. Use when the user wants to discover, analyze, and fix open issues in GitHub repositories. Triggers on requests like "fix GitHub issues", "resolve issues in repo", "work on GitHub bugs", or when the user provides a GitHub repository URL and asks for issue resolution. Supports the full workflow from issue discovery to PR submission with safety guardrails preventing scope creep, unauthorized access, and dangerous operations.

GitHub Issue Resolver

Autonomous agent for discovering, analyzing, and fixing open GitHub issues — with a 5-layer guardrail system.

?? GUARDRAILS — Read First

Every action goes through guardrails. Before any operation:

  1. Load guardrails.json config
  2. Validate scope (repo, branch, path)
  3. Check action gate (auto/notify/approve)
  4. Validate command against allowlist
  5. Log to audit trail

For guardrail details, see references/guardrails-guide.md.

Key Rules (Non-Negotiable)

  • Never touch protected branches (main, master, production)
  • Never modify .env, secrets, CI configs, credentials
  • Never force push
  • Never modify dependency files without explicit approval
  • Never modify own skill/plugin files
  • One issue at a time — finish or abandon before starting new
  • All dangerous actions require user approval (write code, commit, push, PR)
  • Everything is logged to audit/ directory

Workflow

Phase 1 — Issue Discovery

Trigger: User provides a GitHub repository (owner/repo).

Steps:

  1. Validate repo against guardrails:

    python3 scripts/guardrails.py repo  
    

    If blocked, tell the user and stop.

  2. Fetch, score, and present issues using the recommendation engine:

    python3 scripts/recommend.py  
    

    This automatically fetches open issues, filters out PRs, scores them by severity/impact/effort/freshness, and presents a formatted recommendation.

    Always use recommend.py — never manually format issue output. The script ensures consistent presentation every time.

    For raw JSON (e.g., for further processing):

    python3 scripts/recommend.py   --json
    

?? STOP. Wait for user to select an issue.


Phase 2 — Fixing

Trigger: User selects an issue.

Steps:

  1. Lock the issue (one-at-a-time enforcement):

    python3 scripts/guardrails.py issue_lock   
    
  2. Read full issue thread including comments.

  3. Clone the repo (Gate: notify):

    python3 scripts/sandbox.py run git clone https://github.com//.git /tmp/openclaw-work/
    
  4. Create a safe branch (Gate: auto):

    python3 scripts/sandbox.py run git checkout -b fix-issue-
    
  5. Explore codebase — read relevant files. For each file:

    python3 scripts/guardrails.py path 
    
  6. Plan the fix — explain approach to user:

    ## Proposed Fix
    - Problem: [root cause]
    - Solution: [what changes]
    - Files: [list of files and what changes in each]
    - Estimated diff size: [lines]
    

?? STOP. Wait for user to approve the plan before implementing.

  1. Implement the fix (Gate: approve):
    • Apply changes
    • Check diff size: python3 scripts/guardrails.py diff
    • Log: python3 scripts/audit.py log_action write_code success

Phase 3 — Testing

After implementing:

  1. Find and run tests (Gate: notify):

    python3 scripts/sandbox.py run npm test   # or pytest, cargo test, etc.
    
  2. If tests fail AND autoRollbackOnTestFail is true:

    • Revert all changes
    • Notify user
    • Suggest alternative approach
  3. If no tests exist, write basic tests covering the fix.

  4. Report results to user.


Phase 4 — Draft PR for Review (Approval REQUIRED)

?? NEVER create PR automatically. Always ask first.

Do NOT dump full diffs in chat. For any non-trivial project, push the branch and let the user review on GitHub where they get syntax highlighting, file-by-file navigation, and inline comments.

  1. Commit changes (Gate: approve):

    python3 scripts/sandbox.py run git add .
    python3 scripts/sandbox.py run git commit -m "Fix #: "
    </CODE></PRE></LI>
    <LI>
    <P><STRONG>Show a change summary</STRONG> (NOT the raw diff) — keep it concise:</P><PRE><CODE>## Changes
    - **src/models.py** — Added field validation (title length, enum checks)
    - **app.py** — Added validation to POST endpoint, 400 error responses
    - **tests/test_app.py** — 22 new tests covering validation rules
    - 4 files changed, ~100 lines of source + ~150 lines of tests
    - All tests passing ?
    </CODE></PRE></LI>
    <LI>
    <P><STRONG>Ask explicitly:</STRONG> "Ready to push and create a draft PR?"</P></LI>
    <LI>
    <P><STRONG>Only after user says "yes"</STRONG> (Gate: <CODE>approve</CODE>):</P><PRE><CODE class=language-bash>python3 scripts/sandbox.py run git push -u origin fix-issue-<number>
    python3 scripts/sandbox.py run gh pr create --draft --title "..." --body "..."
    </CODE></PRE>
    <P>Note: PRs are always created as <STRONG>draft</STRONG> by default. The PR body should include a detailed description of all changes, test results, and link to the issue (Closes #N).</P></LI>
    <LI>
    <P><STRONG>Share the PR link</STRONG> — user reviews on GitHub.</P></LI>
    <LI>
    <P><STRONG>Unlock the issue:</STRONG></P><PRE><CODE class=language-bash>python3 scripts/guardrails.py issue_unlock
    </CODE></PRE></LI></OL>
    <HR>
    
    <H2 id=scripts-reference>Scripts Reference</H2>
    <DIV class=table-scroll-wrapper>
    <TABLE>
    <THEAD>
    <TR>
    <TH>Script</TH>
    <TH>Purpose</TH>
    <TH>Run Without Reading</TH></TR></THEAD>
    <TBODY>
    <TR>
    <TD><CODE>scripts/recommend.py</CODE></TD>
    <TD><STRONG>Primary entry point</STRONG> — fetch, score, and present issues</TD>
    <TD>?</TD></TR>
    <TR>
    <TD><CODE>scripts/fetch_issues.py</CODE></TD>
    <TD>Raw issue fetcher (used internally by recommend.py)</TD>
    <TD>?</TD></TR>
    <TR>
    <TD><CODE>scripts/analyze_issue.py</CODE></TD>
    <TD>Deep analysis of single issue</TD>
    <TD>?</TD></TR>
    <TR>
    <TD><CODE>scripts/create_pr.py</CODE></TD>
    <TD>PR creation wrapper</TD>
    <TD>?</TD></TR>
    <TR>
    <TD><CODE>scripts/guardrails.py</CODE></TD>
    <TD>Guardrail enforcement engine</TD>
    <TD>?</TD></TR>
    <TR>
    <TD><CODE>scripts/sandbox.py</CODE></TD>
    <TD>Safe command execution wrapper</TD>
    <TD>?</TD></TR>
    <TR>
    <TD><CODE>scripts/audit.py</CODE></TD>
    <TD>Action logger</TD>
    <TD>?</TD></TR></TBODY></TABLE></DIV>
    <H2 id=references>References</H2>
    <UL>
    <LI>references/quick-reference.md — GitHub API reference, scoring rubric, test commands</LI>
    <LI>references/guardrails-guide.md — Full guardrails documentation and customization</LI></UL>                                                        
                                 
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