# WHO-EML-query

> Query the WHO Model List of Essential Medicines (23rd list, 2023). Use whenever the user asks about essential medicines, WHO-recommended drugs, dosage forms, therapeutic sections, or AWaRe antibiotic classification.

- Skill: `gabrielmoreira/who-eml-query` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/who-eml-query`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/who-eml-query/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/who-eml-query

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# WHO Essential Medicines List Query Skill

Search ~500 WHO essential medicines by name, section, or section number. Case-insensitive substring match.

| Input Example | Matches On |
|---|---|
| `amoxicillin` | medicine name |
| `antimalarial` | section hierarchy (full path) |
| `diabetes` | section hierarchy |
| `6.2` | section_num prefix |

## API

| Function | Input | Returns |
|---|---|---|
| `load_eml(cache_path)` | JSON path | list[dict] |
| `build_cache(pdf_path, cache_path)` | PDF + cache paths | list[dict] (parses PDF → JSON) |
| `search(data, entity)` | single string | list[dict] |
| `search_batch(data, entities)` | list of strings | dict[str, list[dict]] |
| `summarize(hits, entity)` | list[dict] + label | compact text |
| `to_json(hits)` | list[dict] | list[dict] |

## Record Schema

| Field | Example |
|---|---|
| `medicine` | `amoxicillin` |
| `section_num` | `6.2.1` |
| `section_name` | `Anti-infective medicines > Antibacterials > Access group antibiotics` |
| `dosage_forms` | `["Capsule: 250 mg; 500 mg", "Oral liquid: 125 mg/5 mL"]` |

## Usage

See `if __name__ == "__main__"` block in `59_WHO_EML.py` for runnable examples.

## Data

- **Source**: WHO Model List of Essential Medicines – 23rd List (2023)
- **PDF**: `WHO EML 23rd List (2023).pdf` in `DATA_DIR`
- **Cache**: `who_eml_23.json` (auto-built on first run from PDF)
- **License**: CC BY-NC-SA 3.0 IGO
- **Dependency**: `pip install pypdf`