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Configures Windows Group Policy Objects to block ransomware execution and lateral spread, covering AppLocker rules, Software Restriction Policies, Controlled Folder Access, attack surface reduction rules, and network protection settings. Use when hardening Windows endpoints against ransomware via GPO, configuring AppLocker or Controlled Folder Access, or building endpoint protection policies through Group Policy.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 2 012 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Configures Windows Group Policy Objects to block ransomware execution and lateral spread, covering AppLocker rules, Software Restriction Policies, Controlled…

As a process C 57/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

ProcedureSecuritytype 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%
93
Quality 40%
75
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Running it twice w 4
30
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 · 7

    ✓ No critical or high findings

    Medium and low: 7
    • 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: Anti-Ransomware Group Policy
    • low Risky intent intent-offensive-security references/api-reference.md:80
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ### Key Anti-Ransomware ASR Rule GUIDs
    • low Risky intent intent-offensive-security SKILL.md:2
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      name: implementing-anti-ransomware-group-policy
    • low Risky intent intent-offensive-security SKILL.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      description: 'Configures Windows Group Policy Objects to block ransomware execution
      detector
    • low Risky intent intent-offensive-security SKILL.md:6
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Use when hardening Windows endpoints against ransomware via GPO, configuring AppLocker
    • low Risky intent intent-offensive-security SKILL.md:10
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      subdomain: ransomware-defense
    • low Risky intent intent-offensive-security SKILL.md:12
      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"
    • note edit-residue the text marks something as outdated (lines 180): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 57/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, 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 2012 tokens
    • 100Progress reporting. Reports progress

    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
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • -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 416: 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: 75.