Create and test a new example client

Generates quick test cases for the TrainFitter pipeline without the trainer having to write JSON by hand.

Process

1. Read the reference schema in examples/cliente_ejemplo_1.json and examples/cliente_ejemplo_2.json (and admission/ficha_cliente_template.md for the context behind each field). Don't invent new fields outside that schema unless the trainer explicitly asks for it — if they do, treat it as a real schema change and update the agents that consume it too.
2. Translate the trainer's description into a complete JSON profile, filling in reasonable assumptions for anything unspecified (age, weight, availability...) — leaving it incomplete breaks the agents, which assume the full schema is present.
3. Name the file examples/cliente_prueba_<short-description>.json (don't overwrite cliente_ejemplo_1.json / _2.json, which are the reference cases documented in docs/decisiones.md).
4. Run the pipeline on that client and show the result:

   import json
   from orchestrator import ejecutar_pipeline
   
   profile = json.load(open("examples/cliente_prueba_<name>.json", encoding="utf-8"))
   state = ejecutar_pipeline(profile)
   print(state.veredicto)

   (run from agents/, or adapt the import path).
5. Comment on the result in the trainer's terms, not just raw JSON: which split it picked, which warnings fired and why, whether the verdict makes sense given the described case.
6. If the case reveals a real gap in the rule engine (e.g. an injury that isn't detected, a dietary restriction not covered in food_bank.py), don't fix it silently — tell the trainer what's missing and ask if they want it added.