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CC building-ransomware-playbook-with-cisa-framework

Builds a structured ransomware incident response playbook aligned with the CISA StopRansomware Guide and NIST Cybersecurity Framework, covering preparation, detection, containment, eradication, recovery, and post-incident phases with actionable checklists. Use when creating or updating a ransomware playbook, running a CISA-aligned readiness assessment, or validating response steps during a tabletop exercise.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 1 819 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Builds a structured ransomware incident response playbook aligned with the CISA StopRansomware Guide and NIST Cybersecurity Framework, covering preparation…

As a process C 55/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
86/100
safety, quality, tests
Safety 60%
90
Quality 40%
79
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 10

    ✓ No critical or high findings

    Medium and low: 10
    • low Risky intent intent-offensive-security references/api-reference.md:1
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # API Reference: CISA Ransomware Playbook Framework
    • low Risky intent intent-offensive-security references/api-reference.md:7
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      URL: https://www.cisa.gov/stopransomware/ransomware-guide
    • low Risky intent intent-offensive-security references/api-reference.md:14
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Part 1 | Ransomware and Data Extortion Prevention Best Practices | Preparation |
    • low Risky intent intent-offensive-security references/api-reference.md:15
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Part 2 | Ransomware and Data Extortion Response Checklist | Response |
    • low Risky intent intent-offensive-security references/api-reference.md:31
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | NIST Function | Ransomware Application |
    • low Risky intent intent-offensive-security SKILL.md:2
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      name: building-ransomware-playbook-with-cisa-framework
    • low Risky intent intent-offensive-security SKILL.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      description: Builds a structured ransomware incident response playbook aligned with
    • low Risky intent intent-offensive-security SKILL.md:6
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      checklists. Use when creating or updating a ransomware playbook, running a CISA-aligned
    • low Risky intent intent-offensive-security SKILL.md:9
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      subdomain: ransomware-defense
    • low Risky intent intent-offensive-security SKILL.md:11
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - ransomware

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"
    • note frontmatter-key unknown frontmatter key "mitre_f3"

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1819 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 411: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (5 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.