SKILLEMALL.ai

BC performing-red-team-phishing-with-gophish

Automates GoPhish phishing simulation campaigns using the Python gophish library, creating email templates with tracking pixels, configuring SMTP sending profiles, building target groups from CSV, launching campaigns, and analyzing results such as open rates, click rates, and credential submission statistics. Use when running an authorized phishing simulation or security awareness assessment via GoPhish.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 432 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Automates GoPhish phishing simulation campaigns using the Python gophish library, creating email templates with tracking pixels, configuring SMTP sending…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

GeneratorAI and agentsSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
98
Quality 40%
79
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security SKILL.md:63
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - When conducting security assessments that involve performing red team phishing with gophish
    • low Risky intent intent-offensive-security SKILL.md:88
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # For authorized penetration testing and lab environments only

    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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 432 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 407: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (2 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.