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BB implementing-zero-trust-network-access

Configures Zero Trust Network Access (ZTNA) in AWS, Azure, and GCP using identity-aware proxies, micro-segmentation, and continuous verification with conditional access policies, replacing VPN-based access with BeyondCorp-style architectures. Use when replacing VPN remote access with identity-based controls, limiting lateral movement via micro-segmentation, or exposing cloud workloads to authenticated users without public internet exposure.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 3 128 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Configures Zero Trust Network Access (ZTNA) in AWS, Azure, and GCP using identity-aware proxies, micro-segmentation, and continuous verification with…

As a process B 70/100 · Nearly there — weak spots: failures and branches, running it twice, progress reporting

ProcedureAWSGoogle CloudAzureKubernetesSecuritytype 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
B
90/100
safety, quality, tests
Safety 60%
95
Quality 40%
82
Run on models
none yet
Process rating
B
70/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Risky intent intent-offensive-security SKILL.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      description: Configures Zero Trust Network Access (ZTNA) in AWS, Azure, and GCP using identity-aware proxies, micro-segmentation, and continuous verification with conditional access policies, replacin
    • low Risky intent intent-offensive-security SKILL.md:33
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - When implementing micro-segmentation to limit lateral movement within cloud networks
    • low Risky intent intent-offensive-security SKILL.md:299
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Micro-Segmentation | Network security technique that creates fine-grained security zones around individual workloads or applications to limit lateral movement |
    • low Risky intent intent-offensive-security SKILL.md:321
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      4. Implement micro-segmentation using security groups to limit lateral movement between application tiers
    • low Risky intent intent-offensive-security SKILL.md:353
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Lateral movement paths eliminated:   85%

    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 edit-residue the text marks something as outdated (lines 300): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 70/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 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
    • 100Tools and files. No external tools needed
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3128 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
    • -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 444: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 22 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +1License stated

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