conducting-post-incident-lessons-learned
Helps your agent review security incidents. It identifies what went wrong and what worked to improve future responses.
What it does
Helps understand what went wrong and what went well after a security incident. The goal is to learn from mistakes to improve future security, without assigning blame.
When to use it
- ✓After resolving a security issue.
- ✓After simulating an attack.
- ✓When something almost went wrong.
Example of what to ask your AI
“Let's review the data breach incident. What did we learn and how can we prevent it from happening again?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI gathers information about the incident, like what happened and when. Then, it helps organize a meeting to discuss what worked, what failed, and what steps to take to improve security.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learnedOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/conducting-post-incident-lessons-learned/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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 |
|---|
…
FAQ
What is a 'blameless' meeting?
It's a meeting focused on understanding how systems and processes failed, rather than pointing fingers at specific individuals.
What are metrics like MTTD, MTTC, and MTTR?
These are important times: how long it took to detect the problem (MTTD), to contain it (MTTC), and to fix it (MTTR).
What is the purpose of the '5 Whys'?
It's a technique to find the root cause of a problem by repeatedly asking 'why?' until you reach the origin.
Details
Original description by the author
Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.
Popularity
- GitHub stars
- 35k
- Ranking position
- #5585
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
More skills from this repository
abusing-dpapi-for-credential-access
Gives your AI agent ready-made instructions for a specific task, so it does it better and faster.
mukul975/Anthropic-Cybersecurity-Skills
abusing-shadow-credentials-for-privesc
Teaches your AI agent to write clear, well-structured text.
mukul975/Anthropic-Cybersecurity-Skills
achieving-cmmc-level-2-compliance
Helps your agent prepare an environment for CMMC Level 2 compliance. It implements security requirements and manages necessary documentation.
mukul975/Anthropic-Cybersecurity-Skills
acquiring-disk-image-with-dd-and-dcfldd
Helps your agent create exact copies of hard drives. This is useful for ensuring no information is lost or altered during investigations.
mukul975/Anthropic-Cybersecurity-Skills
Similar skills
dashboard-builder
Helps your agent create useful monitoring dashboards. It turns data into charts that answer important system questions.
affaan-m/ECC
dev-team
Simulates a collaborative development team where different roles respond to a problem together. It's useful for getting multiple perspectives without manually switching agents.
affaan-m/ECC
ecc-council
Has your agent convene a four-voice council for difficult decisions. It's useful when there are multiple good options and debate is needed before choosing.
affaan-m/ECC