conducting-post-incident-lessons-learned▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.
| 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 |
Conducting Post-Incident Lessons Learned
When to Use
- After any security incident has been fully resolved and recovery completed
- Following tabletop exercises or IR simulations
- After significant near-miss events
- Quarterly review of accumulated incident trends
- When IR playbooks need updating based on real-world experience
Prerequisites
- Incident fully resolved (containment, eradication, recovery complete)
- Incident timeline and documentation gathered
- All incident responders available for review session
- Meeting space for collaborative discussion
- Incident ticketing system data for metrics analysis
Workflow
Step 1: Gather Incident Data
# 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
Step 2: Conduct Blameless Post-Mortem Meeting
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
Step 3: Perform Root Cause Analysis
# 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
Step 4: Calculate Response Metrics
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']}")
Step 5: Document Findings and Create Action Items
# 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"
}
}'
Step 6: Update Playbooks and Detection Rules
# 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
Key Concepts
| 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 |
Tools & Systems
| 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 |
Common Scenarios
- Ransomware Post-Mortem: Review entire kill chain from initial access to encryption. Identify detection gaps and backup failures.
- Phishing Campaign Review: Analyze why users clicked, why email filters missed it, and how to improve training.
- Cloud Misconfiguration Incident: Review IaC pipeline, CSPM coverage, and change management process.
- Insider Threat Review: Examine DLP effectiveness, access control gaps, and user monitoring capabilities.
- Third-Party Breach Impact: Review vendor risk assessment process and data sharing agreements.
Output Format
- Post-incident review meeting minutes
- Root cause analysis document
- Incident metrics report (MTTD, MTTC, MTTR)
- Action items list with owners and deadlines
- Updated IR playbooks and detection rules
- Executive summary for leadership
How to use conducting-post-incident-lessons-learned on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add conducting-post-incident-lessons-learned
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches conducting-post-incident-lessons-learned from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate conducting-post-incident-lessons-learned. Access the skill through slash commands (e.g., /conducting-post-incident-lessons-learned) or your agent's skill management interface.
Security & Verification Notice
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.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★53 reviews- ★★★★★Chaitanya Patil· Dec 24, 2024
conducting-post-incident-lessons-learned has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aditi Okafor· Dec 20, 2024
We added conducting-post-incident-lessons-learned from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Naina Patel· Dec 16, 2024
Solid pick for teams standardizing on skills: conducting-post-incident-lessons-learned is focused, and the summary matches what you get after install.
- ★★★★★Tariq Menon· Dec 16, 2024
conducting-post-incident-lessons-learned reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hiroshi Shah· Dec 4, 2024
Registry listing for conducting-post-incident-lessons-learned matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Omar Li· Dec 4, 2024
conducting-post-incident-lessons-learned fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Maya Rahman· Nov 23, 2024
We added conducting-post-incident-lessons-learned from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Piyush G· Nov 15, 2024
conducting-post-incident-lessons-learned reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Mia Wang· Nov 11, 2024
conducting-post-incident-lessons-learned fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Evelyn Thompson· Nov 7, 2024
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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