Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add mukul975/Anthropic-Cybersecurity-Skills/conducting-post-incident-lessons-learnedCompatible with
Supported agents
| name | conducting-post-incident-lessons-learned |
| description | Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response. |
| domain | cybersecurity |
| subdomain | incident-response |
| tags | - incident-response - lessons-learned - post-incident - after-action-review - process-improvement |
| mitre_attack | - T1190 - T1566 - T1078 |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - RS.MA-01 - RS.MA-02 - RS.AN-03 - RC.RP-01 |
# Export incident timeline from ticketing system
curl -s "https://thehive.local/api/v1/case/$CASE_ID/timeline" \
-H "Authorization: Bearer $THEHIVE_API_KEY" | jq '.' > incident_timeline.json
# Extract detection and response metrics from SIEM
index=notable incident_id="IR-2024-042"
| stats min(_time) as first_alert, max(_time) as last_alert,
count as total_alerts, dc(src) as unique_sources
# Compile all responder actions and timestamps
grep -E "timestamp|action|analyst" /var/log/ir/IR-2024-042/*.json | \
python3 -m json.tool > compiled_actions.json
Structured Agenda (90 minutes):
1. Incident summary (5 min) - Factual overview
2. Timeline walkthrough (20 min) - Chronological events
3. What worked well (15 min) - Positive outcomes
4. What needs improvement (15 min) - Gaps and failures
5. Root cause analysis (15 min) - 5 Whys or fishbone
6. Action items (10 min) - Specific improvements with owners
7. Playbook updates (10 min) - Changes to IR procedures
Blameless Principles:
- Focus on systems and processes, not individuals
- Assume best intentions with available information
- Seek to understand, not to blame
# 5 Whys analysis example:
# Why 1: Why did ransomware encrypt production servers?
# Answer: Attacker had domain admin credentials
# Why 2: Why did attacker have domain admin credentials?
# Answer: Kerberoasted a service account and cracked it
# Why 3: Why was the service account password crackable?
# Answer: Used a 12-character dictionary-based password
# Why 4: Why was the service account password weak?
# Answer: No enforcement of service account password policy
# Why 5: Why was there no service account password policy?
# Answer: PAM was not implemented for service accounts
# ROOT CAUSE: Lack of privileged access management
from datetime import datetime
events = {
'compromise': '2024-01-10 14:00:00',
'detection': '2024-01-15 08:30:00',
'triage': '2024-01-15 08:45:00',
'containment': '2024-01-15 09:30:00',
'eradication': '2024-01-16 14:00:00',
'recovery': '2024-01-18 16:00:00',
'closure': '2024-01-25 10:00:00',
}
fmt = '%Y-%m-%d %H:%M:%S'
times = {k: datetime.strptime(v, fmt) for k, v in events.items()}
print(f"Dwell Time: {times['detection'] - times['compromise']}")
print(f"MTTD: {times['triage'] - times['detection']}")
print(f"MTTC: {times['containment'] - times['detection']}")
print(f"MTTR: {times['recovery'] - times['eradication']}")
print(f"Total Duration: {times['closure'] - times['detection']}")
# Create tracked action items in project management
curl -X POST "https://jira.local/rest/api/2/issue" \
-H "Authorization: Bearer $JIRA_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"fields": {
"project": {"key": "SEC"},
"summary": "Implement PAM for service accounts (IR-2024-042)",
"issuetype": {"name": "Task"},
"priority": {"name": "High"},
"assignee": {"name": "security_engineer"},
"duedate": "2024-03-15"
}
}'
# New Sigma detection rule based on incident learnings
title: Kerberoasting Activity Detected
status: stable
description: Detects Kerberoasting based on IR-2024-042 lessons
logsource:
product: windows
service: security
detection:
selection:
EventID: 4769
TicketEncryptionType: '0x17'
condition: selection
level: high
tags:
- attack.credential_access
- attack.t1558.003
| Concept | Description |
|---|---|
| Blameless Post-Mortem | Reviewing incidents focusing on systems, not blaming individuals |
| Root Cause Analysis | Identifying the fundamental reason the incident occurred |
| 5 Whys | Iterative questioning technique to find root cause |
| MTTD | Mean Time to Detect - time from compromise to detection |
| MTTC | Mean Time to Contain - time from detection to containment |
| MTTR | Mean Time to Recover - time from eradication to full recovery |
| Continuous Improvement | Iterating on IR processes based on real incident data |
| Tool | Purpose |
|---|---|
| TheHive/ServiceNow | Incident timeline and documentation |
| Jira/Azure DevOps | Action item tracking |
| Confluence/SharePoint | Lessons learned documentation |
| Splunk/Elastic | Incident metrics and detection improvement |
| Sigma | Detection rule development |
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionconducting-post-incident-lessons-learnedExecute the skills CLI command in your project's root directory to begin installation:
epx add mukul975/Anthropic-Cybersecurity-Skills/conducting-post-incident-lessons-learnedFetches conducting-post-incident-lessons-learned from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/conducting-post-incident-lessons-learned
Restart Cursor to activate conducting-post-incident-lessons-learned. Access via /conducting-post-incident-lessons-learnedin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
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conducting-post-incident-lessons-learned has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added conducting-post-incident-lessons-learned from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: conducting-post-incident-lessons-learned is focused, and the summary matches what you get after install.
conducting-post-incident-lessons-learned reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for conducting-post-incident-lessons-learned matched our evaluation — installs cleanly and behaves as described in the markdown.
conducting-post-incident-lessons-learned fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added conducting-post-incident-lessons-learned from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
conducting-post-incident-lessons-learned reduced setup friction for our internal harness; good balance of opinion and flexibility.
conducting-post-incident-lessons-learned fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend conducting-post-incident-lessons-learned for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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