Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install m2ai-st-metro-skill-forge-skills-failure-postmortemgit clone https://github.com/m2ai-st-metro/skill-forge.gitcp skill-forge/SKILL.MD ~/.claude/skills/m2ai-st-metro-skill-forge-skills-failure-postmortem/SKILL.md--- name: failure-postmortem description: Guide a structured AI system failure post-mortem using 6 named failure patterns, producing a publishable incident report. --- # Failure Post-Mortem Builder Build a structured post-mortem for an AI system failure. Walks through diagnosis using 6 named failure patterns and produces a publishable incident report. ## Trigger Use when the user says "post-mortem", "failure analysis", "what went wrong with the agent", "debug this failure", or describes an AI system that produced bad output. ## Phase 1: Incident Capture Ask the user for: 1. **What happened** -- the observed bad behavior or output 2. **What was expected** -- the correct behavior 3. **System context** -- which agent/model/pipeline, what inputs triggered it 4. **Impact** -- who was affected, what was the blast radius Keep questions tight. One round of clarification max. ## Phase 2: Failure Pattern Classification Classify the failure against these 6 patterns (multiple can apply): | Pattern | Description | Diagnostic Signal | |---------|-------------|-------------------| | **Context Degradation** | Agent lost track of critical context mid-task due to window limits, compaction, or prompt structure | Output quality dropped partway through; early steps were fine | | **Specification Drift** | The spec/prompt was ambiguous or incomplete; agent filled gaps with assumptions | Agent did something "reasonable" but wrong; spec didn't define the edge case | | **Sycophantic Confirmation** | Agent agreed with flawed premises or user errors instead of pushing back | User provided wrong info; agent incorporated it without challenge | | **Tool Selection Error** | Agent chose the wrong tool, wrong API, or wrong approach for the task | Right intent, wrong execution method; tool existed but wasn't selected | | **Cascade Failure** | One error propagated through multiple steps, each compounding the damage | Small initial mistake; large final deviation; intermediate steps didn't catch it | | **Silent Failure** | Agent completed without errors but output was wrong; no signal that anything failed | "Success" with bad results; no errors, no warnings, no escalation | For each matching pattern, explain: - Why this pattern fits - Which specific moment in the execution triggered it - What guardrail was missing ## Phase 3: Root Cause Identify the deepest cause. Use the "5 Whys" technique: 1. Why did the failure occur? -> [pattern-level answer] 2. Why was that possible? -> [missing guardrail / spec gap] 3. Why was that guardrail missing? -> [process / design gap] 4. Continue until you hit a systemic cause 5. State the root cause in one sentence ## Phase 4: Post-Mortem Report Generate a structured report: ```markdown # AI Failure Post-Mortem: [Short Title] **Date**: [date] **System**: [agent/pipeline name] **Severity**: [LOW / MEDIUM / HIGH / CRITICAL] **Status**: [investigating / mitigated / resolved] ## Incident Summary [2-3 sentences: what happened, what was expected, what was the impact] ## Timeline - [timestamp/step] -- [what happened] - ... ## Failure Patterns Identified ### [Pattern Name] [explanation, evidence, missing guardrail] ## Root Cause [one-sentence root cause from 5 Whys] ## Corrective Actions | Action | Type | Priority | Owner | |--------|------|----------|-------| | [specific fix] | [prevent / detect / mitigate] | [P0-P3] | [who] | ## Lessons Learned - [insight that applies beyond this specific incident] ``` ## Phase 5: Verification - Confirm the report captures the user's understanding of what happened - Ask if any corrective actions should be implemented now - Offer to save the report to the vault or a project directory ## Source Extracted from Nate Kadlac newsletter (2026-03-26) -- "The K-Shaped AI Labor Market" -- failure pattern taxonomy for AI system quality judgment.