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Agent skill

Conducting Post Incident Lessons Learned

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.

Apache-2.0Auto-check passedDevOps & Cloud

Install Conducting Post Incident Lessons Learned

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learned -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills conducting-post-incident-lessons-learned --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/conducting-post-incident-lessons-learned .claude/skills/conducting-post-incident-lessons-learned && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
conducting-post-incident-lessons-learned
GitHub stars
34k
Token cost
~1.7k tokens
SKILL.md length
327 words
Files
8 (incl. scripts, references, assets)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.

  • Works in 6 steps: Gather Incident Data → Conduct Blameless Post-Mortem Meeting → Perform Root Cause Analysis → …
  • Tasks that involve Root cause analysis
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls curl, jq and python3; needs THEHIVE_API_KEY and JIRA_TOKEN

What it does

Conducting Post Incident Lessons Learned is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/api-reference.md` and `references/standards.md`).

It sits in DevOps & Cloud, covering Root cause analysis, Runbooks and postmortems and Incident response. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Root cause analysis
  • Tasks that involve Runbooks and postmortems
  • Tasks that involve Incident response

Example prompts

  • “/conducting-post-incident-lessons-learned”

Requirements

  • Python 3
  • A credential in THEHIVE_API_KEY
  • A credential in JIRA_TOKEN

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Gather Incident Data
  2. Conduct Blameless Post-Mortem Meeting
  3. Perform Root Cause Analysis
  4. Calculate Response Metrics
  5. Document Findings and Create Action Items
  6. Update Playbooks and Detection Rules

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • curl
    • jq
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use curl, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • THEHIVE_API_KEY
    • JIRA_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Conducting Post Incident Lessons Learned loads about 1.7k tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 55 tokens; SKILL.md has 327 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 327 words, ~1,670 tokens.

Download SKILL.mdSave it as .claude/skills/conducting-post-incident-lessons-learned/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
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
bash
# 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
bash
# 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
python
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
bash
# 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
yaml
# 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

ConceptDescription
Blameless Post-MortemReviewing incidents focusing on systems, not blaming individuals
Root Cause AnalysisIdentifying the fundamental reason the incident occurred
5 WhysIterative questioning technique to find root cause
MTTDMean Time to Detect - time from compromise to detection
MTTCMean Time to Contain - time from detection to containment
MTTRMean Time to Recover - time from eradication to full recovery
Continuous ImprovementIterating on IR processes based on real incident data

Tools & Systems

ToolPurpose
TheHive/ServiceNowIncident timeline and documentation
Jira/Azure DevOpsAction item tracking
Confluence/SharePointLessons learned documentation
Splunk/ElasticIncident metrics and detection improvement
SigmaDetection 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

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references, assets) in skills/conducting-post-incident-lessons-learned of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • assets/template.md
  • references/api-reference.md
  • references/standards.md
  • references/workflows.md
  • scripts/agent.py
  • scripts/process.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

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Conducting Post Incident Lessons Learned compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Conducting Post Incident Lessons Learned this skillmukul975/Anthropic-Cybersecurity-Skills34k—~1.7kAutomated safety check: PassApache-2.0
Post-Incident DebriefVeryGoodOpenSource/vgv-wingspan109—~1.9kAutomated safety check: PassMIT
Incident Postmortemgithub/awesome-copilot40k—~1.8kAutomated safety check: PassMIT
Incident Postmortemrevfactory/harness-1001.3k—~1.8kAutomated safety check: PassApache-2.0
Incident Response LifecycleLeoYeAI/openclaw-master-skills2.2k—~5kAutomated safety check: PassApache-2.0
Post Mortemthananon/9arm-skills3.2k—~3.4kAutomated safety check: PassNone

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Categories

Questions about Conducting Post Incident Lessons Learned

What does Conducting Post Incident Lessons Learned do?

Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response. Conducting Post Incident Lessons Learned is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.

When should I use Conducting Post Incident Lessons Learned?

Conducting Post Incident Lessons Learned fits situations like: tasks that involve Root cause analysis; tasks that involve Runbooks and postmortems; tasks that involve Incident response.

How do I install Conducting Post Incident Lessons Learned in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learned -a claude-code`. Or copy the skill folder (skills/conducting-post-incident-lessons-learned in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/conducting-post-incident-lessons-learned in your project. Claude Code loads it when a task matches its description.

How do I install Conducting Post Incident Lessons Learned in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learned -a codex`. Or copy the skill folder (skills/conducting-post-incident-lessons-learned in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/conducting-post-incident-lessons-learned in your project. Codex loads it when a task matches its description.

Can I use Conducting Post Incident Lessons Learned in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill conducting-post-incident-lessons-learned -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/conducting-post-incident-lessons-learned, .gemini/skills/conducting-post-incident-lessons-learned, .github/skills/conducting-post-incident-lessons-learned and .opencode/skills/conducting-post-incident-lessons-learned in your project.

What does Conducting Post Incident Lessons Learned need to run?

Going by SKILL.md and its folder, Conducting Post Incident Lessons Learned needs Python for the scripts in its folder, the command-line tools its instructions call (curl, jq and python3) and credentials named THEHIVE_API_KEY and JIRA_TOKEN. Our summary lists: Python 3; A credential in THEHIVE_API_KEY; A credential in JIRA_TOKEN.

Does Conducting Post Incident Lessons Learned access the network?

SKILL.md contains no URLs. Its commands use curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Conducting Post Incident Lessons Learned safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Conducting Post Incident Lessons Learned use?

Conducting Post Incident Lessons Learned is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Conducting Post Incident Lessons Learned use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.2k tokens, read only when the agent opens those files.

What are the alternatives to Conducting Post Incident Lessons Learned?

Skills that share tags, products or a category with Conducting Post Incident Lessons Learned: Post-Incident Debrief (VeryGoodOpenSource/vgv-wingspan, 109 stars), Incident Postmortem (github/awesome-copilot, 40k stars), Incident Postmortem (revfactory/harness-100, 1.3k stars) and Incident Response Lifecycle (LeoYeAI/openclaw-master-skills, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Conducting Post Incident Lessons Learned?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.