Skill: Add an E2E Eval Test Case

Steps

1. Identify the interaction pair — substance A + substance B, with ANSM level:

   • Contre-indication (CI) or Association déconseillée (AD) → expect_warn: true
   • Précaution d'emploi (PE) or À prendre en compte (APEC) → expect_warn: false

2. Write a realistic clinical scenario (in French, pharmacist/physician perspective):

   • Use DCI names (not brand names) in the prompt
   • Ground it in a plausible indication (e.g. patient greffé sous ciclosporine)
   • Keep it concise — 2-3 sentences

3. Add the case to tests/e2e/prompts.yaml:

   # <ANSM level>: <mechanism> — DB pair: <SUBSTANCE_A> + <SUBSTANCE_B> (<CI|AD|PE|APEC>)
   - id: <substance_a>_<substance_b>
     prompt: >
       <Clinical scenario in French.>
     expect_warn: true   # or false
     expect_in:
       - "⚠️"            # only if expect_warn: true
       - "<substance name as it appears in French DCI>"
     expect_not:
       - "<unrelated substance that should not appear>"
   

4. Choose expect_not carefully — must be a substance meaningfully different, not a trivial variation. Used as false-positive guard.

5. Run the eval to validate the new case passes:

   uv run dotenv -f .env run -- python scripts/run_eval.py
   

Coverage targets

Maintain balance across:

• expect_warn: true cases (CI + AD) — currently: ibuprofene_methotrexate, ciclosporine_simvastatine
• expect_warn: false cases (PE + true negatives) — currently: amiodarone_simvastatine, paracetamol_amoxicilline, generiques_doliprane
• Tool path diversity: interaction lookup, generic lookup, simple drug search

Rules

• id format: <substance_a>_<substance_b> in lowercase, underscores, no accents
• Comment above each case must include the ANSM level and DB pair (for traceability)
• expect_in terms must match what the agent actually outputs (test with a manual run first)
• Never add a case you haven't verified exists in the ANSM thésaurus