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CB conducting-wireless-network-penetration-test

Conducts authorized wireless network penetration tests to assess the security of WiFi infrastructure by testing for weak encryption protocols, captive portal bypasses, evil twin attacks, WPA2/WPA3 handshake capture, rogue access point detection, and client-side attacks. The tester evaluates wireless authentication, network segmentation, and the effectiveness of wireless intrusion detection systems. Activates for requests involving wireless pentest, WiFi security assessment, WPA2/WPA3 testing, or rogue access point detection.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 2 556 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Conducts authorized wireless network penetration tests to assess the security of WiFi infrastructure by testing for weak encryption protocols, captive portal…

As a process B 76/100 · Nearly there — weak spots: running it twice, progress reporting

AnalyzerSecuritytype 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
76/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
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:1
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # API Reference: Wireless Network Penetration Testing Agent
    • low Risky intent intent-offensive-security references/api-reference.md:20
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # Run wireless pentest
    • low Risky intent intent-offensive-security SKILL.md:8
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Activates for requests involving wireless pentest, WiFi security assessment, WPA2/WPA3
    • low Risky intent intent-offensive-security SKILL.md:15
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - wireless-pentest
    • low Risky intent intent-offensive-security SKILL.md:35
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # Conducting Wireless Network Penetration Test
    • low Secrets in code secret-password-literal SKILL.md:142
      Hard-coded password / key literal (may be an example)
      2. IoT-Net WPA2-PSK handshake captured and cracked in 3 minutes (password: Company2024!)

    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 76/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 5 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 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. 57 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2556 tokens
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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 530: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 57 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +1License stated

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