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Agent Skill · 研究检索

hard-fix(硬修复)

hard-fix 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。

hard-fix 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 72,323。

总安装 188周安装 8GitHub Stars 公开资料未说明下载 66核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:hard-fix(硬修复)
来源仓库:https://github.com/nielsmadan/agentic-coding
仓库路径:skills/hard-fix
安装命令:
npx skills add https://github.com/nielsmadan/agentic-coding --skill hard-fix
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/nielsmadan/agentic-coding --skill hard-fix

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

hard-fix 用于查找、检索和筛选相关信息,适合根据关键词快速定位候选结果。适用于 Codex、Claude、Cursor 和 Gemini CLI 中需要线索驱动搜索的任务场景。通过 GitHub 安装,使用 npx skills add 命令添加技能。

使用前需确认权限范围、维护状态,以及是否触发联网、命令执行或文件读写。建议结合来源仓库和原始 README 进一步核验具体功能和限制。

来源仓库内路径:skills/hard-fix

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/nielsmadan/agentic-coding --skill hard-fix;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

hard-fix 怎么安装?

在终端执行 npx skills add https://github.com/nielsmadan/agentic-coding --skill hard-fix。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

hard-fix 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

hard-fix 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

hard-fix 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

hard-fix 用的人多吗?

公开数据是累计安装 188 次,近一周安装 8 次,下载 66 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

hard-fix 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
hard-fix

硬修复 · 本页

188—接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Hard Fix

Comprehensive investigation workflow for bugs that resist normal debugging.

Usage

/hard-fix login keeps failing after auth changes
/hard-fix race condition in checkout - tried 3 fixes already
/hard-fix                              # Uses recent conversation context

Do NOT shortcut this workflow:

  • "I think I already know the fix" -- If you knew, you wouldn't need this skill
  • "Let me just try one more thing first" -- You've already tried. Follow the systematic process
  • "I only need one of these investigation methods" -- Parallel investigation is the point, run ALL agents

Circuit Breaker Rule: If 3 sequential fix attempts have failed for the same issue:

  1. STOP attempting more fixes
  2. Document what was tried and why each failed
  3. This signals a systemic/architectural issue, not a localized bug
  4. Recommend architectural review rather than continuing to patch

Gotchas

  • Phase 0 doc search uses 2>/dev/null on docs/log/ — if the directory doesn't exist, the search silently returns nothing and gives false confidence there are no prior known issues.
  • Phase 7 logging requires user confirmation that the fix works. If the session ends before confirmation, no log is written and the institutional knowledge loop breaks.

Workflow

Phase 0: Pre-Check Internal Documentation

Before full investigation, check internal docs for known issues and gotchas:

Check past issues:

grep -ri "<keywords>" docs/log/ 2>/dev/null | head -10
ls -la docs/log/ 2>/dev/null | grep -i "<related_terms>"

Check project documentation:

grep -ri "<keywords>" docs/ *.md 2>/dev/null | head -10

Look for documented gotchas, known issues, or patterns related to the problem area.

If a matching past issue is found:

  1. Read the full log file
  2. Present the previous solution to the user
  3. Ask: "We encountered this before. Should I apply the previous solution, or run a fresh investigation?"

If relevant documentation is found:

  1. Read the relevant sections
  2. Check if documented patterns/gotchas apply to this issue

If no match or user wants fresh investigation, continue.

Phase 1: Gather Context

Ask clarifying questions if needed:

  • What behavior are you seeing vs. expecting?
  • What fixes have already been tried?
  • When did this start happening? (recent change, always broken, etc.)
  • Are there error messages or logs?

Keep questions minimal - only ask what's essential.

Phase 2: Parallel Investigation

Launch ALL of these simultaneously using the Task tool:

AgentSkill/ToolFocus
Researchresearch-onlineExternal solutions, known issues, library bugs
Debugdebug-logAdd logging to trace the actual execution path
Historyreview-historyGit blame, recent changes, past issue logs
Library SourceRead library codeUndocumented behavior, actual implementation
Opinion 1second-opinionFresh perspective on the problem

For detailed agent prompt templates, see references/templates.md.

Phase 3: Synthesize Findings

Wait for all agents. Combine into a root cause theory with evidence.

The synthesis must trace to mechanism, not stop at symptoms — good synthesis names the root cause with corroborating evidence from multiple agents (debug timing, git history, library source, research). See references/templates.md for BAD/GOOD synthesis examples.

Phase 4: Validate Theory

Run second-opinion with your synthesis and proposed fix. Check for blind spots.

Phase 5: Present to User

For the presentation template, see references/templates.md.

Ask: "Should I proceed with the recommended fix?"

Phase 6: Implement and Verify

  1. Implement the recommended fix
  2. Keep all debug logging in place — do NOT remove debug logs added during investigation
  3. Ask user to test/verify
  4. Wait for user confirmation that the fix worked
  5. Only after user confirms the fix works, remove the debug logging added in Phase 2

Do NOT remove debug logs until user confirms the fix is working. If the fix fails, the logs are essential for the next investigation round. Do NOT proceed to Phase 7 until user confirms the fix is working.

Phase 7: Log for Future Reference (after user confirms fix)

Only after user confirms fix works, write to docs/log/YYYY-MM-DD-{Issue}.md.

For the log template, see references/templates.md.

Examples

Token refresh bug after multiple failed fix attempts:

/hard-fix auth token refresh fails silently after 3 fix attempts

Launches parallel agents: research finds a known axios issue with token queuing, debug-log traces the refresh timing, history reveals the bug started after PR #234 moved refresh to background, and library source confirms axios doesn't queue requests during refresh. Synthesizes a root cause (race condition) with a concrete fix using axios-auth-refresh.

Race condition traced to library update via git history:

/hard-fix race condition in order processing since last deploy

History agent pinpoints a dependency bump that changed default concurrency behavior. Debug logging captures the interleaved execution order, and library source inspection confirms the breaking change in the new version. Presents the version diff and a targeted fix to restore the previous behavior.

Troubleshooting

Parallel investigation agents do not converge on root cause

Solution: Run second-opinion with a consolidated summary of all agent findings and the conflicting theories. If agents still disagree, prioritize the evidence from the debug-log agent (actual runtime behavior) over static analysis, and test the most likely theory first.

Root cause is in a third-party dependency

Solution: Pin the dependency to the last known working version as an immediate fix, then file an issue upstream with reproduction steps. If a patch is needed sooner, fork the dependency or apply a local patch using patch-package (JS) or the equivalent for your ecosystem.

Notes

  • This is a heavyweight process - use for genuinely stuck problems
  • The parallel investigation is key - each source provides different insights
  • Only log confirmed fixes - don't log until user verifies it works
  • Past issue logs are gold - check them first

平台分布

Codex

35.28%
按下载量换算23

Claude

30.93%
按下载量换算20

Cursor

20.73%
按下载量换算14

Gemini CLI

8.52%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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