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CB implementing-ot-incident-response-playbook

Develops OT-specific incident response playbooks using a SANS PICERL-based Python engine that classifies incident severity (safety, process, access, recon) and coordinates IT SOC, OT engineering, and plant operations, aligned with IEC 62443 and NIST SP 800-82. Use when building ICS/SCADA incident response procedures for the first time, preparing for OT ransomware scenarios, or aligning IR with IEC 62443/NERC CIP reporting requirements.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 4 026 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Develops OT-specific incident response playbooks using a SANS PICERL-based Python engine that classifies incident severity (safety, process, access, recon)…

As a process B 71/100 · Nearly there — weak spots: failures and branches, progress reporting

ProcedureSecurityData and analyticstype 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
89/100
safety, quality, tests
Safety 60%
94
Quality 40%
82
Run on models
none yet
Process rating
B
71/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
50
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 · 6

    ✓ No critical or high findings

    Medium and low: 6
    • low Risky intent intent-offensive-security references/api-reference.md:8
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Malware/Ransomware | P1 - Critical | OT network malware (EKANS, Triton) | Immediate |
    • low Risky intent intent-offensive-security SKILL.md:7
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      the first time, preparing for OT ransomware scenarios, or aligning IR with IEC
    • low Risky intent intent-offensive-security SKILL.md:44
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - When preparing for OT ransomware scenarios like EKANS or LockerGoga
    • low Risky intent intent-offensive-security SKILL.md:87
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      RANSOMWARE = "ransomware"
    • low Risky intent intent-offensive-security SKILL.md:159
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      "Revise network segmentation if lateral movement was successful",
      detector
    • low Risky intent intent-offensive-security SKILL.md:217
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      OTIncidentCategory.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"

    Process rating: all ten parameters 71/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4026 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 18 steps
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. No mutating operations

    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
    • -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 439: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 18 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.