ankle-sprainlisted

# 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 1. 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 t