| name | post-mortem-learning |
| description | Analyze failures and suboptimal deliveries to identify root causes, contributing factors, and raw lessons. Activate after significant delivery failures, repeated QA failures, or when patterns of issues need to be understood. |
| license | MIT |
| compatibility | Works with any filesystem-based AI coding agent |
| metadata | {"author":"gaai-framework","version":"1.0","category":"cross","track":"cross-cutting","id":"SKILL-POST-MORTEM-LEARNING-001","updated_at":"2026-02-26T00:00:00.000Z","status":"experimental"} |
| inputs | ["failed_or_degraded_story_results","qa_reports","contexts/artefacts/** (delivered)","contexts/memory/decisions/**","contexts/rules/** (applied)"] |
| outputs | ["root_cause_analysis","contributing_factors","raw_lessons","failure_scenarios","improvement_candidates"] |
Post-Mortem Learning
Purpose / When to Activate
Activate after:
- Significant delivery failures
- Repeated QA failures on the same area
- Delivery that missed its success metrics
- Patterns of issues that need systemic understanding
Process
- Reconstruct what happened end-to-end
- Compare expected vs actual outcomes
- Identify: technical causes, contextual gaps, decision errors, rule weaknesses
- Produce concrete failure narratives
- Extract raw lessons (non-generalized) — specific to this failure
Outputs
- Root cause analysis report
- Failure timeline
- Mapped rule gaps
- Raw lesson list (specific, not generic)
- Candidate improvement areas (for backlog or rules)
Quality Checks
- Root causes are specific, not generic ("insufficient testing" is invalid — "acceptance criterion #3 had no test coverage" is valid)
- Lessons are raw and specific, not platitudes
- Failure timeline is traceable
- Rule gaps link to specific rule files
Non-Goals
This skill must NOT:
- Propose solutions (produces inputs for backlog and rules, not fixes)
- Assign blame
- Generalize into vague lessons
Makes failures understandable and actionable. Enables serious continuous improvement.
