SKILLEMALL.ai

AB patient-education-explainer

Explain a condition, a scan result, or why movement helps, in language that reduces fear instead of adding to it — the reframe, the honest uncertainty, and the analogy that does not accidentally frighten. Use when asked to explain a diagnosis to a patient, explain scan findings, reassure someone who is afraid to move, or write patient-facing education material. Produces the explanation in plain language, the reframe of frightening terminology, the honest uncertainty statement, the what-you-can-do section, and a teach-back check. A communication framework for a licensed clinician; the clinical content is theirs.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 1 file body ≈ 1 432 tokens Open the sourcegithub.com analyzed 2 d ago

Explain a condition, a scan result, or why movement helps, in language that reduces fear instead of adding to it — the reframe, the honest uncertainty, and…

As a process B 73/100 · Nearly there — weak spots: failures and branches, progress reporting

AnalyzerInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
73/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: patient-education-explainer (mohitagw15856/pm-claude-skills)

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: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 73/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 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. 40 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1432 tokens
    • 100Running it twice. No mutating operations

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 618: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 40 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.