skills/postmortem/SKILL.md
Conducts blameless postmortems that transform failures into learning opportunities by documenting timelines, quantifying impact, performing root cause analysis (5 Whys, fishbone diagrams), and defining corrective actions with owners and deadlines. Use when analyzing failures, outages, incidents, or negative outcomes, conducting blameless postmortems, identifying corrective actions, learning from near-misses, establishing prevention strategies, or when user mentions postmortem, incident review, failure analysis, RCA, lessons learned, or after-action review.
npx skillsauth add lyndonkl/claude postmortemInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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When NOT to use: Incident still ongoing (focus on resolution first), looking to assign blame (antithesis of blameless culture), or issue is trivial with no learning value.
Copy this checklist and track your progress:
Postmortem Progress:
- [ ] Step 1: Assemble timeline and quantify impact
- [ ] Step 2: Conduct root cause analysis
- [ ] Step 3: Define corrective and preventive actions
- [ ] Step 4: Document and share postmortem
- [ ] Step 5: Track action items to completion
Step 1: Assemble timeline and quantify impact
Gather facts: when detected, when started, key events, when resolved. Quantify impact: users affected, duration, revenue/SLA impact, customer complaints. For straightforward incidents use resources/template.md. For complex incidents with multiple causes or cascading failures, study resources/methodology.md for advanced timeline reconstruction techniques.
Step 2: Conduct root cause analysis
Ask "Why?" 5 times to get from symptom to root cause, or use fishbone diagram for complex incidents with multiple contributing factors. See Root Cause Analysis Techniques for guidance. Focus on system failures (process gaps, missing safeguards) not human errors.
Step 3: Define corrective and preventive actions
For each root cause, identify actions to prevent recurrence. Must be specific (not "improve testing"), owned (named person), and time-bound (deadline). Categorize as immediate fixes vs. long-term improvements. See Corrective Actions for framework.
Step 4: Document and share postmortem
Create postmortem document using template. Include timeline, impact, root cause, actions, what went well. Share widely (engineering, product, leadership) to enable learning. Present in team meeting for discussion. Archive in knowledge base.
Step 5: Track action items to completion
Assign owners, set deadlines, add to project tracker. Review progress in standups or weekly meetings. Close postmortem only when all actions complete. Self-assess quality using resources/evaluators/rubric_postmortem.json. Minimum standard: ≥3.5 average score.
Production Outages (system failures, downtime):
Security Incidents (breaches, vulnerabilities):
Product/Project Failures (launches, deadlines):
Process Failures (operational, procedural):
Human Error (surface cause, dig deeper):
Process Gap (missing or unclear procedures):
Technical Debt (deferred maintenance):
External Dependencies (third-party failures):
Systemic Issues (organizational, cultural):
5 Whys:
Example: Database outage → Why? Bad config → Why? Wrong value → Why? Template error → Why? New team member unfamiliar → Why? No config review in onboarding
Fishbone Diagram (Ishikawa):
Fault Tree Analysis:
Types of Actions:
SMART Actions:
Prioritization:
Prevention Hierarchy (from most to least effective):
Blameless Culture:
Root Cause Depth:
Actionability:
Impact Quantification:
Timeliness:
Resources:
Success Criteria:
Common Mistakes:
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.