Turn a vague, intermittent, or environment-specific bug report into a minimal evidence-backed reproduction before proposing a fix.
复制下面这句话,粘贴给 Claude Code、Codex、Cursor 等 AI 编程工具,它会读取安装说明并在你确认后完成安装。
请阅读 https://ai.atlankj.com/install/asset/gh-bug-reproduction-brief-1f9756eaaab3 ,按照其中的说明把「bug-reproduction-brief」安装到你(当前 AI 工具)中。执行前先告诉我将运行的命令和写入的位置,等我确认。
查看 AI 将读取的安装说明正在读取 GitHub 原文…
内容来自 GitHub 原始文件,由原作者维护。在 GitHub 查看
Use this skill when a bug report is incomplete, intermittent, environment-specific, or mixed with an assumed cause. The goal is to prove the smallest observable failure before diagnosis or repair begins.
Capture the exact error, incorrect output, timestamp, affected route or command, and the smallest known input. Preserve relevant logs without secrets or personal data. Label second-hand descriptions as unverified.
Record only facts you can inspect:
Never guess credentials or production configuration.
Write two explicit observable statements:
Expected: [observable result]
Actual: [observable result, including status or error]
Do not put the suspected cause in either statement.
Start from the reported path, then remove unrelated data, services, and steps one at a time. Keep the smallest fixture that still fails. If the failure stops, restore the last removed condition and record it.
Prefer an isolated test, minimal script, or smallest safe request over reproducing against production.
Run the minimal reproduction at least twice where safe. Record commands and outputs. If the failure is intermittent, report the observed frequency and duration instead of calling it deterministic.
A verified reproduction is the deliverable. Do not edit implementation code while building the brief because that can destroy the evidence or mix diagnosis with remediation.
# Bug Reproduction Brief
- Target and commit:
- Environment:
- Expected:
- Actual:
- Minimal steps:
- Minimal fixture:
- Reproduced: yes / no / intermittent
- Evidence:
- Unknowns:
- Safe next hypothesis to test:
Use the Bug Reproduction Brief skill on the failing checkout test. Do not fix it yet. Reduce it to the smallest safe failing fixture and report the exact command evidence, expected result, actual result, and remaining unknowns.
Adapted from the MIT-licensed workflow at https://github.com/skyestrela/ai-agent-skill-preview.