How do I install the Ankle Sprain skill?

Run npx skillmds@latest add cyrillemat/ankle-sprain in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ankle Sprain safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ankle Sprain?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ankle Sprain free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ankle Sprain?

CyrilLeMat (@cyrillemat) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ankle Sprain by cyrillemat — Agent Skill

Name: Ankle Sprain
Author: CyrilLeMat

Ankle Sprain

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.

CyrilLeMat 7b32112 6 files · 17.9 KB Updated Sep 17, 2026

File contents

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

Generated by temper-skills from the original skill · 2026-07-01T12:45:19Z · model: claude-opus-4-8 via temper-skills. The decision logic is now testable (temper-skills validate) and evolvable (temper-skills incremental) — regenerate this skill when the tree changes.

---name: ankle-spraindescription: 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.---# 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 answer1. 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`2. Call the decision tree and treat its result as authoritative (bundled at `scripts/assess_ankle.py`): ```python 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}) ```3. 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 surfaceThe 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---Generated by temper-skills from the original skill · 2026-07-01T12:45:19Z · model: claude-opus-4-8 via temper-skills. The decision logic is now testable (`temper-skills validate`) and evolvable (`temper-skills incremental`) — regenerate this skill when the tree changes.

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