SKILLEMALL.ai

BA referral-letter-generator

Generate medical referral letters with patient summary, reason for referral.

ClawHub Agent Skills author: AIpoch v1.0.0 MIT-0 13 files body ≈ 2 373 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process A 84/100 · Runs to the end — no weak spots found

GeneratorInfrastructureWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
90
Quality 40%
83
Run on models
none yet
Process rating
A
84/100
Runs to the end
When it triggers w 12
50
Tools and files w 18
60
Inputs and preconditions w 11
70
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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
    • medium Dangerous commands cmd-eval-dynamic scripts/main.py:355
      Dynamic code execution from decoded/untrusted input
      os.system(f"{sys.executable} -m pip install reportlab -q")
    • medium Dangerous commands cmd-eval-dynamic scripts/main.py:473
      Dynamic code execution from decoded/untrusted input
      os.system(f"{sys.executable} -m pip install python-docx -q")

    Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "skill-author"

    Process rating: all ten parameters 84/100

    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 92 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Failures and branches. 4 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2373 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 26 top-level sections: this looks like several domains in one skill

    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)
    • +3Description length 76: 120–800 characters recommended
    • -45 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 92 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    The skill is mostly a medical referral letter generator, but it needs review because it can automatically install software at runtime and makes weakly supported privacy/security claims while handling sensitive patient data.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026