Ankle sprain

Skill CyrilLeMat/temper-skills/examples/ankle_sprain/output/ankle-sprain

Test suites + deterministic Python for your agent skills' decision logic. Adversarial persona reviewers write the tests; the generated code must keep passing them — zero LLM calls at inference. Try: uvx temper-skills audit <skill.md>

Install

npx -y skills add CyrilLeMat/temper-skills --skill ankle-sprain

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Frozen, deterministic decision (no LLM): maps pain_malleolar_zone, bone_tenderness_lateral_malleolus, bone_tenderness_medial_malleolus, can_bear_weight, visible_deformity, sprain_grade, hours_since_injury, age_years, patient_profile to one of urgent_care, seek_imaging, see_clinician, police_acute, police_subacute. Use when this decision must be made consistently and auditably — extract the features, call assess_ankle(), and relay its verdict without overriding it.

SKILL.md

2.5 KB, as published. Nobody here has run it

assess_ankle — skill (tempered by temper-skills)

You are an assistant.

The decision is frozen. Do not re-derive it from prose or your own judgment — the routing logic now lives in a deterministic decision tree (assess_ankle.assess_ankle, zero LLM calls, reviewed and version-controlled). Your job is the part the tree cannot do: turn the request into structured features, call the tree, and phrase its verdict.

How to answer

Extract these structured features from the request:
- pain_malleolar_zone
- bone_tenderness_lateral_malleolus
- bone_tenderness_medial_malleolus
- can_bear_weight
- visible_deformity
- sprain_grade
- hours_since_injury
- age_years
- patient_profile

Call the decision tree and treat its result as authoritative (bundled at scripts/assess_ankle.py):

from scripts.assess_ankle import assess_ankle
verdict = assess_ankle({"pain_malleolar_zone": pain_malleolar_zone, "bone_tenderness_lateral_malleolus": bone_tenderness_lateral_malleolus, "bone_tenderness_medial_malleolus": bone_tenderness_medial_malleolus, "can_bear_weight": can_bear_weight, "visible_deformity": visible_deformity, "sprain_grade": sprain_grade, "hours_since_injury": hours_since_injury, "age_years": age_years, "patient_profile": patient_profile})

Relay verdict to the user. Do not override it. If a feature can't be extracted, pass it as None — the tree is built to fall through safely.

Gray zones to surface

The tree flags these as underdetermined — mention the caveat when the answer touches them:

(n2) Ottawa Rules are validated for ages ~18–55; outside that, escalate (handled below)
(n6) ice for symptom relief is fine briefly; the harm is prolonged/aggressive icing and immobilization