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Blameless Incident Learning Review
Builds an evidence-based incident timeline, contributing-factor analysis, and corrective plan without blame or invented causality.
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See it in action
You say
Use incident-learning-review on these Slack logs and Datadog exports. Build the default package, cite every conclusion, and do not fill in missing timestamps.
Your agent does
Evidence Ledger
[S1] Slack Log #ops-incidents (10:05-10:45) [S2] Datadog Export: Latency Spike (10:08)
Verified Timeline
- 10:08: [S2] Latency exceeds 5s threshold.
- 10:12: [S1] @dev-1 reports 500 errors in US-East-1.
- 10:15: [Inference] Possible DB lock initiated.
Gaps
- Exact time of DB lock initiation is unverified in logs.
What you get
About this skill
The problem
Post-mortems often suffer from hindsight bias, invented causality, and missing evidence that gets "smoothed over" during drafting. Manual incident reviews struggle to maintain a strict audit trail between raw logs and final reports.
What it does
- Builds an evidence ledger with unique source IDs and locators for every material claim.
- Constructs a verified timeline that distinguishes between observations, hypotheses, and causal claims.
- Identifies and preserves conflicting data points in a dedicated conflict log instead of forced reconciliation.
- Generates an action register focused on systemic changes rather than individual blame.
- Flags specific information gaps that require further evidence or human decision-making.
Why this beats prompting it yourself
Generic prompts tend to hallucinate missing timestamps or bridge gaps with plausible-sounding but unverified narratives. This skill enforces a rigorous quality gate system that prioritizes "no false completion," ensuring every conclusion is traceable to your specific logs and telemetry.
Use cases
- Writing blameless post-mortems after production outages or security incidents.
- Synthesizing multiple chat logs, PagerDuty alerts, and CloudWatch metrics into a single source of truth.
- Creating auditable incident reports for compliance or executive review.
- Mapping contributing factors in complex system failures where a single "root cause" is insufficient.
Known limitations
Does not make legal conclusions or attribute individual fault. Requires structured source materials to generate a high-confidence timeline.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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