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    Blameless Incident Learning Review

    1

    Builds an evidence-based incident timeline, contributing-factor analysis, and corrective plan without blame or invented causality.

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    Blameless Incident Learning Review

    Blameless Incident Learning Review

    Example session with this skill installed

    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.

    • Read your context and instructions
    • Compiled the blameless incident learning

    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.

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

    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 use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Verified clean 1 month ago

    • Passed all security checks, Safe to install

    Listed1 month ago

    What's inside

    Frequently Asked Questions