incident-postmortem
Skill0 starsChanged 6 days agoMITView on GitHub
Write a blameless postmortem or incident report from logs, alerts, chat transcripts, tickets and notes. Use when the user says "write the postmortem", "incident report", "RCA", "what happened during the outage", or pastes an incident timeline or Slack thread.
20 other projects use a version of this.
Adopted by
Most recently taken up by
- LIXIAO199115/skillrigtheir own version, first seen Nov 15, 2026
- nisarth/claude-skills-storetheir own version, first seen Nov 8, 2026
- jaskirat1616/grok-skillstheir own version, first seen Nov 1, 2026
- github/awesome-copilottheir own version, first seen Oct 25, 2026
- pathiec92/claude-skillstheir own version, first seen Oct 25, 2026
- fattain-naime/engineering-docstheir own version, first seen Oct 24, 2026
Used with
Projects with it also use systematic-debugging.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
OpenAISkillsystematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Also in 3 projects that use this
Other versions20 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 20
- github/awesome-copilot★ 41ksame name
- proflead/codex-skills-library★ 153same name
- logly/mureo★ 64same name
- anthropics/claude-tag-plugins★ 60same name
- johnqtcg/awesome-skills★ 30same name
- jaskirat1616/grok-skills★ 22same name
- JayRHa/agent-skills★ 12same name
- JayRHa/AgentSkills★ 12same name
- z-Zihan/Evir★ 11same name
- fattain-naime/engineering-docs★ 6same name
10 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository24 other files it gives its agents.
- api-docsSkill
- changelogSkill
- ci-failure-fixSkill
- code-reviewSkill
- commit-messageSkill
- competitor-analysisSkill
- data-analystSkill
- debug-detectiveSkill
- dependency-upgradeSkill
- dockerizeSkill
- email-replierSkill
- eval-promptSkill
- meeting-notesSkill
- migration-safetySkill
- pr-descriptionSkill
- product-specSkill
- readme-writerSkill
- refactor-safelySkill
- research-briefSkill
- security-checkSkill
- seo-articleSkill
- sql-helperSkill
- support-replySkill
- write-testsSkill
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 4 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 17 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
Use one time zone throughout (UTC unless the team uses another) and convert every timestamp to it.
- Distinctive: line 20
start of customer impact
- Very specific: line 27
Never invent or round a time you do not have.
- Distinctive: line 40
What went well: keep it; it shows what to protect.
- Distinctive: line 42
If the evidence does not prove a cause, write "most likely" and what would confirm it.
“Common” is measured: kept by at least 6 in 10 of the 17 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Incident postmortem
The goal is learning, not blame. Describe what systems and processes allowed the failure, never "X made a mistake".
1. Collect and separate facts
- Gather every source provided: alerts, logs, deploy history, chat, tickets, status page updates.
- Mark each statement as confirmed (seen in logs, metrics or a timestamped message) or reported (someone said so). Keep that distinction in the document.
- Use one time zone throughout (UTC unless the team uses another) and convert every timestamp to it.
2. Build the timeline
Key moments, each with time, event and source:
- trigger (the change or event that started it)
- start of customer impact
- detection (and how: alert, customer, employee)
- escalation and responders joining
- mitigation steps, including the ones that did not work
- impact ends
- full resolution
Never invent or round a time you do not have. Write [time unknown] and add it to the open questions.
3. Impact
- Who was affected, what they could not do, for how long.
- Measured numbers (failed requests, affected users, orders, revenue) only from data provided. Estimates must be labelled as estimates, with the method.
- Detection time (start to detection) and resolution time (start to end).
4. Causes
- Root cause: the underlying condition that, if it had not existed, would have prevented the incident. Ask "why" until you reach something a team can change (a missing check, a design gap, an unclear process), not a person.
- Trigger: what set it off. Often different from the root cause.
- Contributing factors: what made it worse or slower to fix (missing alert, confusing dashboard, runbook out of date, on-call could not access X).
- What went well: keep it; it shows what to protect.
If the evidence does not prove a cause, write "most likely" and what would confirm it.
5. Action items
Each one: specific action, owner (a role or [owner]), priority, due date or [date], and which cause it addresses. Prefer actions that prevent or detect the whole class of failure over one-off patches. Five strong items beat fifteen vague ones.
Output
- Title, date, severity, status, author
[name]. - Summary (3-4 sentences a non-engineer can read).
- Impact.
- Timeline (table).
- Root cause, trigger, contributing factors.
- What went well / what went badly / where we got lucky.
- Action items (table).
- Open questions and missing data.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
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Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

