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CB executing-phishing-simulation-campaign

Executes authorized phishing simulation campaigns to assess an organization's susceptibility to email-based social engineering attacks. The tester designs realistic phishing scenarios, builds credential harvesting infrastructure, sends targeted phishing emails, and tracks open rates, click-through rates, and credential submission rates to measure human security awareness. Activates for requests involving phishing simulation, social engineering assessment, email security testing, or security awareness measurement.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 2 785 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Executes authorized phishing simulation campaigns to assess an organization's susceptibility to email-based social engineering attacks.

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

AnalyzerAI and agentsSecuritytype 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%
93
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 · 7

    ✓ No critical or high findings

    Medium and low: 7
    • low Risky intent intent-offensive-security references/api-reference.md:32
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Creates a credential harvesting page. API: `POST /api/pages/`.
    • low Risky intent intent-offensive-security references/api-reference.md:38
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Launches a phishing campaign. API: `POST /api/campaigns/`.
    • low Risky intent intent-offensive-security SKILL.md:5
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      phishing scenarios, builds credential harvesting infrastructure, sends targeted
    • low Risky intent intent-offensive-security SKILL.md:73
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Conducting the social engineering component of a red team exercise to gain initial access
    • low Risky intent intent-offensive-security SKILL.md:114
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Build the credential harvesting landing page that mirrors the target's login portal
    • low Risky intent intent-offensive-security SKILL.md:121
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Launch the phishing campaign:
    • low Risky intent intent-offensive-security SKILL.md:159
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | **Credential Harvesting** | Collecting usernames and passwords through fake login pages that mimic legitimate services |

    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 76/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 17 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. 64 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2785 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 518: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 64 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.