agentleFS
kaandgnshkaandgnsh/Claude-Blue/skills/incident-response/after-action/SKILL.md

after-action

Skill0 starsChanged 14 days agoMITView on GitHub

Run a blameless post-incident review that produces a factual timeline, root and contributing causes, what worked, and tracked corrective actions with owners. Use after a significant incident, near miss or exercise, or when lessons-learned items keep not getting done.

2 other projects use a version of this.

Adopted by

Most recently taken up by

The file

Files
SKILL.md

After-Action Review

Workflow

  1. Schedule within 2 weeks of closure while memory is fresh; include responders, system owners, and decision-makers.
  2. Build the factual timeline first (from the case record) and circulate it before the meeting for corrections.
  3. Walk the timeline, asking at each phase: what did we know, what did we do, what would have let us act sooner or better?
  4. Measure: time to detect (first malicious event → first alert), time to triage, time to contain, time to recover; detection source (internal alert, third party, user report).
  5. Identify causes with "5 whys" or a contributing-factor list across: prevention control, detection gap, process, people/training, tooling, third party. Avoid naming individuals as root cause.
  6. Corrective actions: specific, owned, dated, prioritized; each tied to a cause. Track in the risk/ticket system and report closure.

Questions that surface real gaps

  • Which attacker step had no detection? Which had a detection that was ignored or closed?
  • Where did we lose time waiting (approvals, access, contacts, vendor)?
  • Did the out-of-band channel and contact list work?
  • What telemetry did we wish we had? Was retention long enough?
  • Would this have been prevented by a control we believed was in place?

Output template

After-action — <incident id> — <date>
Summary: <>
Metrics: TTD <> | TTT <> | TTC <> | TTR <> | detection source <>
Timeline: <link>
What worked: <>
Contributing factors: <factor> → <evidence>
Corrective actions:
 # | action | cause addressed | owner | due | priority | ticket
Detection gaps → new detections: <>

Related skills

incident-report-writing, detection-lifecycle, attack-coverage-mapping, risk-register

References

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

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.