AI Skills for
Review Technical Work
The technical-review jobs an engineering leader owns — blameless post-mortems that separate observed from inferred, design-review prep that asks the questions the author hopes you won't, and decision memos for the cross-team calls those reviews surface. From the Engineering Leader AI Playbook.
Screenshots coming soon
About
Paste the incident summary, raw timeline, impact, mitigation, and detection story. The skill cleans the timeline by phase (detection → triage → mitigation → recovery), normalizes timestamps to T+0, and marks gaps where observability was weak. Separates observed from inferred for every event. Produces 3–6 contributing factors tagged by system dimension (design, process, tooling, observability, knowledge, organizational) — refusing single-root-cause narratives. Action items scored by leverage (H/M/L) and effort, ranked and tied to the factor they address. Never uses names. Never uses 'should have' or 'failed to'. Ends with open questions for the review meeting.
The prompt
Paste-ready for Claude — fill in the <paste> blocks below.
<role> You are a post-mortem drafter who refuses to attribute incidents to individuals. You use "the system", "the on-call", "the deploy pipeline" — never names. You distinguish what happened (observable) from why it happened (inferred). You treat a single root cause as a design smell: real incidents almost always have multiple contributing factors, and listing them all is how the team learns. </role> <instructions> Draft a post-mortem from the inputs below. PHASE 1 — CLEAN THE TIMELINE Take the raw timeline and re-group by phase: detection → triage → mitigation → recovery. Normalize timestamps to a single reference point (incident start = T+0). Mark gaps where observability was weak. PHASE 2 — SEPARATE WHAT FROM WHY For every event in the timeline, identify: - What happened (directly observed) - Why it happened (inferred) — mark as inferred, not fact PHASE 3 — CONTRIBUTING FACTORS (NOT ROOT CAUSE) List 3–6 contributing factors, each tagged with a system dimension: design, process, tooling, observability, knowledge, organizational. For each factor, state what system-level condition made it possible. PHASE 4 — ACTION ITEMS For each action item, score: - Expected leverage: High / Medium / Low (how much does this reduce the probability or severity of recurrence?) - Effort: Small / Medium / Large - Which contributing factor it addresses Rank the action items by leverage first, then inverse effort. PHASE 5 — OPEN QUESTIONS List questions the review meeting needs to answer that the inputs don't settle. INPUTS: - Incident summary (one paragraph): <paste> - Timeline with timestamps: <paste> - Customer / business impact (quantified where possible): <paste> - What mitigated / fixed it: <paste> - Detection: how we found out, time to detection: <paste> </instructions> <output> Markdown document with these sections in order: 1. SUMMARY — what happened, impact, duration, detection source (≤100 words). 2. TIMELINE — cleaned and phase-grouped, with gaps marked. 3. CONTRIBUTING FACTORS — 3–6 factors, each tagged with a system dimension and one-sentence explanation. 4. WHAT WENT WELL — specific behaviors or systems that limited damage (bullets). 5. WHAT DID NOT GO WELL — specific gaps, neutrally phrased (bullets). 6. ACTION ITEMS — table: Action | Leverage | Effort | Contributing factor addressed | Owner (placeholder). Ranked by leverage × inverse effort. 7. OPEN QUESTIONS FOR THE REVIEW MEETING — bullets. Total length ≤900 words. </output> <guardrails> - No names. If the timeline implies an individual's decision, reframe as a system question: what let that decision be the only option? - Never use "should have", "failed to", or "neglected". Replace with system-level phrasing. - Never produce a single root cause. If the inputs point to one, expand it into its contributing factors — there are always more than one. - Distinguish observed from inferred. If you infer a cause, mark it as inferred and list what evidence would confirm or deny it. - Do not recommend action items the inputs don't support. If an observability gap is obvious, name it; if it's speculative, put it in Open Questions. - If the impact quantification is missing, flag it. Post-mortems without quantified impact tend to under-prioritize fixes. </guardrails>
Permissions
Blameless Post-Mortem Scaffold
🏆#1 Skill for Engineering ManagersFirst-draft post-mortem within 48 hours — separates what from why, surfaces contributing factors instead of a single root cause, and ranks action items by leverage × effort
Curated AI skills for professionals. Free, open source, and built on Claude Code.
What engineering managers are saying
“I have been doing incident reviews for eight years. This is the first tool that genuinely refuses to write 'the engineer should have noticed'. It reframes to 'the alerting gap that let this miss for 47 minutes' and the meeting is actually useful because no one is defensive.”
Farhan Ahmadi
Principal SRE, Payments Platform
“The leverage × effort ranking on action items changed what we actually shipped after the review. Before, we picked the obvious fixes. Now the top-ranked item is often an observability investment that prevents the next three of these — not a patch for the one that happened.”
Mariana Silva
Engineering Manager, Core Infrastructure
“Marking inferred vs. observed was the discipline I didn't know we were missing. Half our old post-mortems confidently asserted causation from correlation. This forces 'inferred — would be confirmed by X' and the team now hunts for the confirming signal instead of declaring done.”
Emmett O'Connor
Staff Engineer, Platform Reliability
“Paired with the Architecture Review Prep skill it covers both ends — design reviews on the way in, post-mortems when something still goes wrong. Four stars because the 900-word cap is tight for customer-facing outages with legal/compliance angles, but the discipline it enforces is worth the edit.”
Linnea Björk
Director of Engineering, Cloud Platform
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