building-incident-response-playbook

building-incident-response-playbook is a skill for Claude Code, Codex from adriannoes/awesome-agentic-ai. It costs 94 tokens per session (2,337 once invoked), scanned A, original, MIT.

A guide for writing reusable incident-response playbooks: step-by-step procedures for handling specific security events. It includes decisions, escalation rules, assigned responsibilities, and possible integration with SOAR platforms, which automate security workflows.

In plain words
What is it for?
Use it to document responses to common attack scenarios, prepare for compliance audits, assess response gaps, or automate parts of response in a SOAR system. It is for reusable procedures, not one-off investigation reports.
Why use it?
Responders may lose time or make inconsistent decisions when there is no agreed procedure. A playbook makes responsibilities and actions clear before an incident occurs.

Skill for Claude CodeCodex

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

Good fit Use it to document responses to common attack scenarios, prepare for compliance audits, assess response gaps, or automate parts of response in a SOAR system. It is for reusable procedures, not one-off investigation reports.

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Install with agentmods
npx agentmods add skills/adriannoes/awesome-agentic-ai/building-incident-response-playbook
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 adriannoes/awesome-agentic-ai --skill building-incident-response-playbook
Clone the repo
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-ai

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 building-incident-response-playbook

README.md
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Your own site
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Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found 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 $0.00094 $0.02337
Opus 5 $0.00047 $0.01169
Sonnet 5 $0.00019 $0.00467
Haiku 4.5 $0.00009 $0.00234

Measured 12d ago against content hash a30ed65e7fa5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

building-incident-response-playbook scanned grade A with 0 findings 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 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

3 near-identical copies found in the catalogue:

cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/building-incident-response-playbook/SKILL.md · 271 lines

How it starts

The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Building Incident Response Playbooks

When to Use

  • Establishing or maturing an incident response program from scratch
  • Documenting procedures for a new incident type after a novel attack
  • Automating response workflows in a SOAR platform (Cortex XSOAR, Splunk SOAR)
  • Preparing for compliance audits requiring documented IR procedures (SOC 2, PCI-DSS, HIPAA)
  • Conducting a gap analysis of existing IR capabilities against specific threat scenarios

Do not use for one-time ad hoc investigations; playbooks are reusable procedure documents, not case-specific reports.

Prerequisites

  • Organizational risk assessment identifying top incident scenarios by likelihood and impact
  • NIST SP 800-61r3 or SANS PICERL framework adopted as the organizational IR standard
  • Asset inventory with business criticality ratings and data classification
  • RACI chart defining roles: Incident Commander, SOC analysts, system administrators, legal, communications
  • Existing detection capabilities inventory (SIEM rules, EDR detections, IDS signatures)
  • SOAR platform access if building automated playbooks

Workflow

Step 1: Select and Scope the Incident Type

Define the specific scenario the playbook will address:

  • Identify the top incident types based on organizational risk assessment and historical data
  • Scope each playbook to a single incident type for clarity (do not combine unrelated scenarios)
  • Define trigger conditions that activate the playbook

Common playbook types:

Priority Playbooks (build first):
1. Ransomware incident response
2. Phishing/credential compromise
3. Business email compromise
4. Malware infection
5. Data breach/exfiltration
6. DDoS attack
7. Insider threat
8. Account takeover
9. Web application compromise
10. Cloud infrastructure compromise

Step 2: Define the Playbook Structure

Every playbook should follow a consistent structure:

PLAYBOOK TEMPLATE
━━━━━━━━━━━━━━━━
1. Playbook Metadata
   - Name, version, owner, last review date
   - Trigger conditions
   - Severity criteria

2. RACI Matrix
   - Who is Responsible, Accountable, Consulted, Informed for each step

3. Detection & Triage
   - How the incident is detected
   - Initial triage checklist
   - Severity classification criteria

4. Containment
   - Short-term containment actions
   - Long-term containment actions
   - Evidence preservation requirements

5. Eradication
   - Root cause identification
   - Malware/threat removal steps
   - Verification procedures

6. Recovery
   - System restoration steps
   - Validation criteria
   - Monitoring requirements post-recovery

7. Post-Incident
   - Lessons learned meeting trigger
   - Report template
   - Detection improvement actions

8. Communication
   - Internal notification matrix
   - External notification requirements (regulators, customers, law enforcement)
   - Status update cadence

9. Appendices
   - Tool-specific procedures
   - Contact lists
   - Evidence collection checklists

Read the full file on GitHub · 271 lines

Files

What ships with it

3 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. 12d ago First seen · 271 lines · 94 tokens per session scan A a30ed65e7fa5

Subscribe to this mod's changes

building-incident-response-playbook is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 94 tokens to every session and 2,337 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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