Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.
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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:
Fetches conducting-post-incident-lessons-learned from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate conducting-post-incident-lessons-learned. Access via /conducting-post-incident-lessons-learned in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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| 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 |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
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✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
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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