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conducting-post-incident-lessons-learned

Facilitate structured post-incident reviews to identify root causes,

Install / Use

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learned

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

95/100

Category

Security

Supported Platforms

Universal

Our assessment of conducting-post-incident-lessons-learned

conducting-post-incident-lessons-learned scores 95/100 on our quality scale, 159th of 544 Security skills we index (top 30%).

Its SKILL.md is 6.5 KB long, well organised into 31 sections with 6 code examples: a thorough specification that gives an agent plenty to work with.

With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
12/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 25 days ago, so conducting-post-incident-lessons-learned is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

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Frequently asked questions

How do I install conducting-post-incident-lessons-learned?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learned. The install tabs above show the steps for each supported agent.
Which AI agents does conducting-post-incident-lessons-learned work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is conducting-post-incident-lessons-learned safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is conducting-post-incident-lessons-learned still maintained?
The repository was last updated 25 days ago, so conducting-post-incident-lessons-learned is actively maintained.

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:
  • T1566
  • T1486
  • T1059
  • 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

  1. Ransomware Post-Mortem: Review entire kill chain from initial access to encryption. Identify detection gaps and backup failures.
  2. Phishing Campaign Review: Analyze why users clicked, why email filters missed it, and how to improve training.
  3. Cloud Misconfiguration Incident: Review IaC pipeline, CSPM coverage, and change management process.
  4. Insider Threat Review: Examine DLP effectiveness, access control gaps, and user monitoring capabilities.
  5. 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

Related Skills

View on GitHub
GitHub Stars33.3k
CategorySecurity
Updated25d ago
Forks4.0k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions