SKILLEMALL.ai
GitHub ↗ RU Sign in

BC performing-threat-modeling-with-owasp-threat-dragon

Uses OWASP Threat Dragon (web or desktop) to build data flow diagrams, identify threats with STRIDE, LINDDUN, CIA, DIE, or PLOT4ai methodologies via its auto-generation rule engine, and produce PDF threat model reports. Use during secure design review of an application architecture to build a formal threat model and document mitigations for GRC compliance.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 8 files · 2 scripts body ≈ 1 925 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Uses OWASP Threat Dragon (web or desktop) to build data flow diagrams, identify threats with STRIDE, LINDDUN, CIA, DIE, or PLOT4ai methodologies via its…

As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

GeneratorGitHubPeople and hiringDesignData and analyticstype 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%
100
Quality 40%
75
Run on models
none yet
Process rating
C
62/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 8. 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_ai_rmf"
    • note frontmatter-key unknown frontmatter key "atlas_techniques"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 62/100

    • 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. 4 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 42 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1925 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
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 358: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 42 items
    • +4Has examples (2 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.