Postmortem Writer

    1

    Transforms incident logs and alert timelines into blameless, evidence-backed postmortem reports.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    Postmortem Writer

    Example session with this skill installed

    Draft a postmortem for the API outage yesterday. Here is the Slack export from #incident-api-104 and the PagerDuty alert timeline. Focus on why the auto-scaler failed to trigger.

    • Read your context and instructions
    • Compiled the postmortem writer
    • Generated the document

    I have analyzed the Slack transcript and PagerDuty events. I normalized 42 events into a UTC timeline and identified a 12-minute gap between the first 5xx spikes and the initial page.

    Incident Report: API Scaling Failure (SEV-2)

    Summary: The production API experienced 100% error rates for 18 minutes due to an auto-scaler configuration lockout.

    MetricValue
    MTTD4m 12s
    MTTA2m 45s
    MTTR18m 22s
    SeveritySEV-2

    Timeline Summary

    • 2023-10-12 14:02:10 UTC: First 503 errors detected in ap-southeast-2.
    • 2023-10-12 14:06:22 UTC: PagerDuty alert #882 fired (High Urgency).
    • 2023-10-12 14:09:07 UTC: On-call engineer acknowledged alert.
    • 2023-10-12 14:15:30 UTC: Root cause identified: Terraform lock held by stale CI job prevented scaling.
    • 2023-10-12 14:20:32 UTC: Manual lock release; scaling resumed.

    Root Cause Analysis

    • Trigger: Sudden traffic spike exceeded current node capacity.

    Root Cause: A failed CI pipeline left a global state lock on the production workspace, preventing the auto-scaler from updating the desired instance count.

    Contributing Factor: The auto-scaler's health check did not alert when Terraform operations failed due to locking.

    Action Items

    • Prevents: Implement 10-minute TTL on Terraform state locks. (Owner: Platform Team)
    • Detects: Add CloudWatch alarm for ScalingFailed events in the provider log. (Owner: SRE)
    • Mitigate: Document manual state unlock procedure in the emergency runbook. (Owner: On-call)

    Next steps

    1. Review the 4-minute gap between error spikes and the PagerDuty alert; the threshold may be too high.
    2. Assign owners and due dates to the action items above.
    3. Verify if the stale CI job (13:45 UTC) is a recurring failure.

    postmortem-writerwkr.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Reconstruct incident timelines from disparate Slack and PagerDuty sources.Calculate precise MTTD/MTTR metrics from raw event timestamps.Draft blameless RCA documents focusing on system failures over human error.Audit existing drafts for timeline gaps and unsupported causal claims.

    About this skill

    The problem

    Engineering teams lose hours manually reconstructing incident timelines from Slack logs and PagerDuty alerts. These documents often suffer from subjective bias, blame-coded language, and metrics that don't actually match the evidence.

    What it does

    • Normalizes and merges timestamps from Slack, PagerDuty, and log files into a single UTC timeline.
    • Calculates precise MTTD, MTTA, and MTTR metrics directly from tagged incident events.
    • Identifies the technical trigger, root cause, and contributing factors using blameless 5-Whys analysis.
    • Generates a structured Markdown report with categorized action items (prevents, detects, mitigates).
    • Flags timeline gaps and unsupported causal claims for human verification.

    Frameworks & tools

    Python-based scripts for timeline parsing and report rendering. Supports JSON exports from Slack, PagerDuty, and Opsgenie, plus CSV and plain text logs.

    Why this beats prompting it yourself

    Generic LLMs often hallucinate timestamps or "guess" root causes when data is missing. This skill enforces strict evidence-based drafting, ensuring every claim in the report is backed by a specific entry in the normalized timeline while strictly maintaining a blameless engineering culture.

    Use cases

    • Drafting SEV1/SEV2 root cause analysis documents after a system outage.
    • Auditing human-written postmortems for timeline gaps or blame-heavy language.
    • Converting messy incident Slack channels into readable executive summaries.
    • Generating standardized incident metrics for quarterly reliability reviews.

    Known limitations

    Does not connect directly to external APIs. Users must provide text or JSON exports of incident data. Does not perform automated code-level debugging.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Recently published to Agensi

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    Verified clean 5 days ago

    • Passed all security checks, Safe to install

    Listed5 days ago

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