Turn incomplete or intermittent bug reports into a minimal reproduction backed by recorded evidence. Capture the environment, expected and actual results, commands, and remaining unknowns in a brief before diagnosis or repair.
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Turn a vague, intermittent, or environment-specific bug report into a minimal evidence-backed reproduction before proposing a fix.
安装前可以查看交给 AI 代理的指令内容。
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.
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Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
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Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
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Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
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Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
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Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.
中文简介正在准备中。当前显示英语简介。