incident-postmortem

A guide for reviewing a resolved production incident, such as an outage or serious failure, without blaming individuals. It reconstructs what happened and records why it happened.

In plain words
What is it for?
Use it to build an incident timeline, identify systemic root causes, and create specific follow-up tasks with owners, dates, and checks.
Why use it?
It turns a stressful failure into a clear record of causes, contributing conditions, detection, response, and ways to reduce repeat incidents.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/jayrha/agentskills/incident-postmortem
Any agent
npx skills add JayRHa/AgentSkills --skill incident-postmortem
Clone the repo
git clone --depth 1 https://github.com/JayRHa/AgentSkills

Made for: Claude Code, Codex.

Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,775 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00108 $0.01775
Opus 5 $0.00054 $0.00888
Sonnet 5 $0.00022 $0.00355
Haiku 4.5 $0.00011 $0.00178

Measured 2d ago against content hash 0beeadcf1460, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

incident-postmortem scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/timeline_builder.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

incident-postmortem/SKILL.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Incident Postmortem

Overview

Keywords: postmortem, post-mortem, RCA, root cause analysis, blameless, incident review, outage retro, SEV1, SEV2, contributing factors, action items, five whys, timeline, MTTR, COE, incident report, learning review.

This skill turns a resolved incident into a durable learning artifact. It enforces a blameless stance (focus on systems and conditions, never individuals), drives toward systemic root causes (not just the proximate trigger), and converts findings into specific, owned, dated action items with verification. The output is a single postmortem document plus a list of trackable follow-ups.

A good postmortem answers five questions: What happened? What was the impact? Why did it happen? How was it detected and resolved? How do we prevent recurrence (and reduce time-to-detect/time-to-resolve next time)?

Use templates/postmortem.md as the document skeleton, references/root-cause-techniques.md for the analysis methods, references/blameless-language.md to rewrite blame into systems language, and scripts/timeline_builder.py to assemble a clean timeline and compute incident metrics.

Workflow

  1. Gather the raw record. Collect the incident channel/war-room transcript, alert timestamps, deploy and config-change logs, dashboards/graphs, the first customer report, and the resolution moment. Note the SEV/severity level and the systems affected. Do not start writing prose yet — collect facts.

  2. Build the timeline. Convert every relevant event into a timestamp | actor/system | event row. Use UTC and ISO-8601. Run scripts/timeline_builder.py to sort events, normalize timestamps, and auto-derive the key metrics (time-to-detect, time-to-mitigate, time-to-resolve, total duration). The timeline is the spine of the document — get it right before reasoning about causes.

  3. Establish impact. Quantify in user/business terms: affected users or %, requests failed, revenue/SLA/error-budget burned, duration of degradation. Vague impact ("some users affected") undermines prioritization — push for numbers or explicit estimates with their basis.

Read the full file on GitHub · 75 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 75 lines · 108 tokens per session scan A 0beeadcf1460

Subscribe to this mod's changes

incident-postmortem is a skill published in the GitHub repository JayRHa/AgentSkills (4 stars, last pushed 1mo ago), licensed MIT. It adds 108 tokens to every session and 1,775 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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