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incident-response

When something breaks in production: triage the severity, gather evidence, identify root cause, deploy a fix or mitigation, and write a post-mortem. Provides a calm, structured process for high-stress moments.

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<!-- TÜRKÇE AÇIKLAMA ─────────────── Bu skill, production'da bir şey patladığında sakin ve yapılandırılmış bir kriz yönetimi sağlar. Şiddeti sınıflandır → kanıtları topla → kök nedeni bul → düzelt → post-mortem yaz. Panikle atılan komutlar sorunu büyütür; bu skill önce düşün, sonra hareket et prensibini uygular. NE ZAMAN: Production çöktüğünde, error spike'ta, güvenlik ihlali şüphesinde, deployment sonrası beklenmedik regresyonda. ÇIKTI: .agent/incidents/INC-<tarih>-<slug>.md — timeline + root cause + action items. -->

Incident Response Skill

When to Trigger

  • Production is down or severely degraded
  • Error rate spikes significantly above baseline
  • A customer reports a critical bug in production
  • A security breach or data leak is suspected
  • A deployment caused an unexpected regression

Step-by-Step Process

1. Declare and Classify the Incident

First: breathe. Then classify.

SeverityDefinitionResponse Time
P1 — CriticalFull outage, data loss, security breachImmediate
P2 — HighMajor feature broken, significant users affected< 30 min
P3 — MediumPartial degradation, workaround exists< 2 hours
P4 — LowMinor bug, cosmetic issueNext sprint

Open .agent/incidents/INC-<YYYY-MM-DD>-<slug>.md and write:

# Incident: <short title>
**Date:** <YYYY-MM-DD HH:MM UTC>
**Severity:** P<N>
**Status:** Investigating
**Incident Commander:** <name>

2. Triage — Gather Evidence Fast

Don't guess. Collect data first.

Checklist:

  • What is the user-visible symptom?
  • When did it start? (check monitoring, not just "someone reported it")
  • What changed recently? (last deploy, config change, dependency update)
  • What does the error log say? (get exact error messages + stack traces)
  • Is it affecting all users or a subset?
  • Is there an automated alert, or was this user-reported?

Commands to run immediately:

# Recent deploys
git log --oneline -20

# Check running process / container health
# (adjust for your stack)
docker ps
systemctl status <service>

# Tail live logs
docker logs -f <container> --tail 100
journalctl -u <service> -n 100 -f

Add findings to the incident file under ## Evidence.

3. Contain the Blast Radius

Before fixing, limit the damage:

  • If a bad deploy caused it: roll back immediately
    git revert HEAD
    # or redeploy previous known-good version
    
  • If a specific feature is failing: toggle it off via feature flag
  • If data is being corrupted: take the affected service offline temporarily
  • If a secret was exposed: rotate it immediately, then investigate

Document every action taken in the incident file with timestamps.

4. Root Cause Analysis

Once contained, find the real cause — not just the symptom.

Use the 5 Whys technique:

Why did users see a 500 error?
→ Because the database query timed out.
Why did the query time out?
→ Because a new index was missing after the migration.
Why was the index missing?
→ Because the migration script didn't include CREATE INDEX.
Why didn't the migration script include it?
→ Because we don't have a convention for checking query plans before deploy.
→ Root cause: Missing pre-deploy query plan review process.

The root cause is almost never the immediate technical failure — it's the process gap that allowed it.

5. Fix and Deploy

Write the fix:

  • Smallest possible change that resolves the issue
  • Add a test that would have caught this
  • Double-check the fix in staging before production

Deploy:

  • Monitor metrics during deploy
  • Keep someone watching logs for 15 minutes post-deploy
  • Confirm symptom is resolved with a real user test

6. Write the Post-Mortem

Within 24h of resolution, write a post-mortem in .agent/incidents/INC-<date>-<slug>.md:

# Post-Mortem: <title>

## Summary
<2-3 sentences: what happened, how long, how many users affected>

## Timeline
| Time (UTC) | Event |
|------------|-------|
| HH:MM | Issue first occurred (inferred from logs) |
| HH:MM | First alert / user report |
| HH:MM | Incident declared |
| HH:MM | Mitigation applied |
| HH:MM | Root cause identified |
| HH:MM | Fix deployed |
| HH:MM | Incident resolved |

## Root Cause
<The actual root cause — not just the symptom>

## What Went Well
- <something that helped — good monitoring, fast rollback, etc.>

## What Went Poorly
- <something that made it worse — slow detection, unclear runbook, etc.>

## Action Items

| Action | Owner | Due Date |
|--------|-------|----------|
| Add missing index to migration convention | <name> | <date> |
| Add test for query performance | <name> | <date> |
| Set up alert for DB query p99 latency | <name> | <date> |

7. Update the Knowledge Base

After the post-mortem, use the knowledge-base-update skill to create a gotcha entry for the root cause so future agents and developers know about it.

Rules

  • Contain first, diagnose second. Stopping the bleeding is more important than understanding the wound.
  • No blame in post-mortems. Focus on process gaps, not people.
  • Every incident generates at least one action item. If nothing changes, it will happen again.
  • Never declare an incident resolved until a real user confirms the fix. Metrics can lie; users don't.
  • All P1/P2 incidents require a post-mortem. No exceptions.

리뷰

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