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.
npx skills add 26zl/cybersec-toolkit --skill conducting-post-incident-lessons-learnedgit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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.
[](https://agentmods.dev/skills/26zl/cybersec-toolkit/conducting-post-incident-lessons-learned)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/conducting-post-incident-lessons-learned"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/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.
<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/conducting-post-incident-lessons-learned"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/conducting-post-incident-lessons-learned.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 120 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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.
| Model | Per session | Once 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 | $0.00171 |
Grade A, and why
conducting-post-incident-lessons-learned scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://thehive.local/api/v1/case/$CASE_ID/timeline" \ Copies of this mod
1 near-identical copy found in the catalogue:
- conducting-post-incident-lessons-learned — 100% identical, 0 lines differ
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
What ships with it
7 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.
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.
- 7d ago First seen · 192 lines · 40 tokens per session scan A d525f1db0c8a
conducting-post-incident-lessons-learned is a skill published in the GitHub repository 26zl/cybersec-toolkit (52 stars, last pushed yesterday), 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). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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