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BC fda-consultant-specialist

Use for FDA medical device questions: 510(k) vs De Novo vs PMA, predicates and substantial equivalence, QMSR (21 CFR 820), HIPAA or device cybersecurity. Runs fda_submission_tracker.py, qsr_compliance_checker.py and hipaa_risk_assessment.py.

alirezarezvani/claude-skills Agent Skills author: alirezarezvani MIT 9 files · 3 scripts body ≈ 2 820 tokens Open the sourcegithub.com↗ analyzed 8 d ago

Use for FDA medical device questions: 510(k) vs De Novo vs PMA, predicates and substantial equivalence, QMSR (21 CFR 820), HIPAA or device cybersecurity.

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSecurityWriting and documentstype 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
93/100
safety, quality, tests
Safety 60%
94
Quality 40%
91
Run on models
none yet
Process rating
C
54/100
Has gaps
Inputs and preconditions w 11
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 · 6

    ✓ No critical or high findings

    Medium and low: 6
    • low Risky intent intent-offensive-security references/device_cybersecurity_guidance.md:72
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      │   ├── Penetration testing
    • low Risky intent intent-offensive-security references/device_cybersecurity_guidance.md:104
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Penetration testing | Required | Recommended |
    • low Risky intent intent-offensive-security references/device_cybersecurity_guidance.md:504
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Bug bounty program: [if applicable]
    • low Risky intent intent-offensive-security references/device_cybersecurity_guidance.md:604
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - [ ] Penetration test report
    • low Risky intent intent-offensive-security references/device_cybersecurity_guidance.md:674
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Insufficient testing | Conduct penetration testing |
    • low Risky intent intent-offensive-security SKILL.md:242
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Security Testing | Penetration testing, vulnerability scanning |

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 120, 122, 124, 134, 162, 283): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 54/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 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. 2 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 85Steps. 33 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2820 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 241: enough signal without eating the budget
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 33 items
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.