conducting-post-incident-lessons-learned

conducting-post-incident-lessons-learned is a skill for Claude Code, Codex from Youngmaidainon/Agent-Level-Up. It costs 40 tokens per session (1,712 once invoked), scanned A, a copy of conducting-post-incident-lessons-learned, MIT.

A structured review held after a security incident or exercise to record what happened, what worked, what failed, and what should change.

In plain words
What is it for?
Use it to review timelines, responder actions, detection and recovery metrics, and updates needed for incident-response playbooks.
Why use it?
It turns an incident into concrete improvements instead of allowing the same response problems to happen again.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to review timelines, responder actions, detection and recovery metrics, and updates needed for incident-response playbooks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/youngmaidainon/agent-level-up/conducting-post-incident-lessons-learned
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Youngmaidainon/Agent-Level-Up --skill conducting-post-incident-lessons-learned
Clone the repo
git clone --depth 1 https://github.com/Youngmaidainon/Agent-Level-Up

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for conducting-post-incident-lessons-learned

README.md
[![agentmods](https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/conducting-post-incident-lessons-learned/github.svg)](https://agentmods.dev/skills/youngmaidainon/agent-level-up/conducting-post-incident-lessons-learned)
Your own site
<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/conducting-post-incident-lessons-learned"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/conducting-post-incident-lessons-learned/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for conducting-post-incident-lessons-learned

Your own site · 80×15
<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/conducting-post-incident-lessons-learned"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/conducting-post-incident-lessons-learned.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,712 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.01712
Opus 5 $0.00020 $0.00856
Sonnet 5 $0.00008 $0.00342
Haiku 4.5 $0.00004 $0.00171

Measured 7d ago against content hash d525f1db0c8a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

conducting-post-incident-lessons-learned scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/agent.py, scripts/process.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s "https://thehive.local/api/v1/case/$CASE_ID/timeline" \
Origin

This is a copy

100% identical to conducting-post-incident-lessons-learned — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

cyber-security/ctf/conducting-post-incident-lessons-learned/SKILL.md · 192 lines

How it starts

The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on 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

Read the full file on GitHub · 192 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 192 lines · 40 tokens per session scan A d525f1db0c8a

Subscribe to this mod's changes

conducting-post-incident-lessons-learned is a skill published in the GitHub repository Youngmaidainon/Agent-Level-Up (3 stars, last pushed 16d ago), licensed MIT. It adds 40 tokens to every session and 1,712 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to conducting-post-incident-lessons-learned, differing in 0 lines, and is treated as a copy.

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