agent-incident-postmortem
适合分析错误输出、超量调用或提示注入等复杂事件
复盘中可能涉及敏感日志或用户数据，需合规处理
为代理/LLM事故生成无责复盘并分析根因与改进项
帮你把 AI 代理/LLM 干出错的事情写成一份无责复盘：把用户看到的幻觉信息、滥用工具、被注入的 prompt、成本飙高或代理自作主张的过程还原成时间线，分层分析根因并给出可执行的改进和回归用例。需要把一次偶发但有害的行为交代清楚、找出为什么发生并避免重现时用。它针对非确定性系统，强调用 trace 而不是单纯复现步骤来查因果。
▸ 展开 SKILL.md 英文原文
Run a blameless postmortem for an incident caused by an AI agent or LLM feature — hallucinated facts shipped to users, runaway tool use, prompt injection, cost blowouts, or wrong actions taken autonomously. Use when asked to write up an AI incident, analyse why an agent did something wrong, or produce corrective actions after an LLM failure. Produces a structured postmortem with trace reconstruction, a root-cause layer analysis, and corrective actions including a permanent regression case. For non-AI production incidents use incident-postmortem.
帮我安装这个 skill：https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/main/skills/agent-incident-postmortem/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/main/skills/agent-incident-postmortem/SKILL.md"# Agent Incident Postmortem Skill AI incidents differ from outages: the system didn't go down — it did something wrong, confidently, and maybe only once. This skill adapts blameless postmortem practice to nondeterministic systems, where "can we reproduce it?" needs traces, not just steps. ## What This Skill Produces - A **blameless postmortem document** with timeline and user/business impact - A **trace reconstruction** of what the agent saw, decided, and did - A **root-cause analysis across the AI failure layers** (not "the model hallucinated" as a conclusion) - **Corrective actions** — always including a new permanent case in the regression suite ## Required Inputs Ask for (if not alr