incident-response

name	incident-response
description	Structured incident workflow: severity classification, triage, diagnosis, mitigation, postmortem, and prevention. Template-driven with blameless review.

Incident Response

Structured framework for handling production incidents with blameless postmortems.

When to Invoke

• Production incidents (P0-P3)
• Service degradation or outages
• Post-incident analysis and learning
• Improving incident response processes

Severity Classification

Severity	Impact	Response Time	Examples
P0	Complete outage, data loss risk	Immediate (< 15 min)	Service down, data corruption
P1	Major degradation, many users affected	< 30 min	Core feature broken, severe performance
P2	Partial degradation, some users	< 2 hours	Non-critical feature broken, slow queries
P3	Minor issue, workaround available	< 1 business day	UI glitch, minor performance

Incident Workflow

1. Detect & Alert

• Automated monitoring triggers alert
• User reports issue
• On-call engineer acknowledges

2. Triage

• Classify severity (P0-P3)
• Assess blast radius (users, services, data)
• Identify incident commander
• Open communication channel

3. Diagnose

• Form hypotheses (use debugging-protocol skill)
• Collect evidence (logs, traces, metrics)
• Identify root cause
• Document timeline

4. Mitigate

• Implement immediate fix (rollback, feature flag, hotfix)
• Verify mitigation effectiveness
• Communicate status to stakeholders
• Continue monitoring

5. Resolve

• Confirm service fully recovered
• Close incident
• Schedule postmortem (within 48 hours for P0-P2)

Postmortem Template

# Incident Postmortem: {title}
Date: {date}
Severity: P{0-3}
Duration: {start} → {resolved}
Author: {name}

## Summary
{1-2 sentence impact summary}

## Timeline
| Time | Event |
|---|---|
| HH:MM | {event description} |

## Root Cause
{description with evidence}

## Contributing Factors
- {factor with context}

## What Went Well
- {positive observation}

## What Could Be Improved
- {improvement area}

## Action Items
| Action | Owner | Due Date | Status |
|---|---|---|---|
| {specific action} | @{person} | YYYY-MM-DD | Open |

Principles

• Blameless — focus on systems and processes, not individuals
• Evidence-based — every claim backed by data (logs, traces, metrics)
• Action items are SMART — specific, measurable, assigned, realistic, time-bound
• Share learnings — postmortems are public within the organization

Pre-Mortem Analysis

Proactive failure analysis: imagine the feature has already failed, then work backward to identify why.

When to Invoke

• After DESIGN phase, before BUILD (in feature pipelines)
• For high-risk features (auth, payments, data migrations, public API changes)
• When requested as standalone risk assessment (Template L)

Pre-Mortem Protocol

Step 1: Assume Failure

The feature has launched and failed catastrophically. Work backward:

• "The auth system was bypassed" — how?
• "Data was corrupted during migration" — what went wrong?
• "The service went down under load" — where was the bottleneck?

Step 2: Failure Mode Identification

For each component in the DESIGN output, enumerate:

1. What could break? — specific failure scenarios, not vague risks
2. How likely? — based on complexity, blast radius, novelty
3. How severe? — data loss, security breach, downtime, degraded UX

Step 3: Cross-Domain Risk Assessment

Each specialist brings a unique failure lens:

Agent	Risk Domain	Example Findings
incident-responder	Operational failures, cascade risks, recovery gaps	"No rollback path for schema migration"
security-engineer	Threat model, attack vectors, auth bypass	"JWT validation missing on webhook endpoint"
performance-engineer	Scalability limits, resource exhaustion	"Unbounded query on user list with no pagination"
database-expert	Data integrity, migration reversibility, concurrency	"Non-idempotent migration with no down script"

Include agents based on feature risk profile:

• Always: incident-responder (anchor)
• Auth/security-sensitive: + security-engineer
• High-traffic/scalable: + performance-engineer
• Data-heavy/migration: + database-expert

Step 4: Produce Findings

# Pre-Mortem: {feature name}
Date: {date}
Analysts: {agent list}

## Risk Assessment Summary
| Risk | Severity | Likelihood | Mitigation |
|------|----------|-----------|------------|
| {failure scenario} | Critical/High/Medium/Low | High/Medium/Low | {recommendation} |

## Failure Modes
### {failure scenario title}
- **Trigger**: {what causes this failure}
- **Blast Radius**: {what else breaks}
- **Detection**: {how would we know — monitoring, alerts, logs}
- **Recovery**: {rollback path, data recovery, feature flags}
- **Mitigation**: {what BUILD agents should do to prevent this}

## Monitoring Gaps
- {gap}: {recommendation for devops-engineer}

## Design Revision Recommendations
- {if any DESIGN changes are needed before BUILD proceeds}

Pre-Mortem Decision Flow

• Findings are advisory — BUILD proceeds with risk-aware context
• If any finding is Critical severity + High likelihood → escalate to user before BUILD
• Monitoring gap findings → forwarded to devops-engineer during or after BUILD
• Design revision recommendations → returned to architect for contract updates before BUILD

Related

• Debugging Protocol @.agents/skills/debugging-protocol/SKILL.md
• Logging Implementation @.agents/skills/logging-implementation/SKILL.md
• Monitoring and Alerting Principles @.agents/rules/monitoring-and-alerting-principles.md