configs/net/template.yaml

# NET teaching program template — TRACE v1

template_id: task1_net_v1
task_type: teaching_program
method_id: net

system_prompt: |
  You are an expert ABA (Applied Behavior Analysis) clinical assistant. You help
  Board Certified Behavior Analysts and staff design teaching programs for
  individuals with autism. Your responses are clinically accurate, individualized
  to the learner profile, follow BACB ethical guidelines, and reference no real
  client data. Select the appropriate teaching method (DTT, NET, Task Analysis,
  FCT, BST, PRT) based on the skill target and learner profile.

user_variants:
  - |
    Generate a teaching program for the following target.

    Skill Target: {skill_target}
    Curriculum Reference: {curriculum_ref}
    Learner Profile: {learner_profile_name}
    Current Mastery: {mastery_state_name}
    Teaching Method: NET (Natural Environment Teaching)

    Provide the full program structure appropriate to the selected method.

  - |
    I need a NET program for this skill.

    Target: {skill_target}
    Curriculum: {curriculum_ref}
    Learner: {learner_profile_name}
    Mastery Status: {mastery_state_name}

    Please include the MO arrangement, natural opportunity, prompt strategy, natural reinforcer, generalization tactics, and mastery criteria.

  - |
    Design a Natural Environment Teaching program for the following.

    Skill: {skill_target}
    Curriculum: {curriculum_ref}
    Learner Level: {learner_profile_name}
    Baseline: {mastery_state_name}

    Include the full naturalistic teaching structure.

assistant_template: |
  ## Program Overview
  - Target skill: {skill_target}
  - Curriculum: {curriculum_ref}
  - Learner: {learner_profile_name}
  - Current mastery: {mastery_state_name}

  This NET program teaches the target skill through naturalistic embedding — capturing motivated moments within the learner's routine rather than using contrived massed trials.

  ## Motivating Operation (MO) Arrangement
  {mo_arrangement_text}
  Primary natural context: {primary_natural_context}.

  ## Natural Opportunity
  {natural_opportunity_text}

  ## Prompt Strategy
  Strategy: {prompt_strategy_name}
  {prompt_strategy_description}
  Current prompt level: At the {mastery_state_short} stage, {current_prompt_guidance}

  ## Natural Reinforcer
  {natural_reinforcer_text}
  Avoid contrived consequences — the functional outcome IS the reinforcer.

  ## Generalization Tactics
  - Program multiple exemplars across {n_exemplars} different stimuli or peers.
  - Rotate between {n_settings} different natural settings.
  - Rotate across {n_therapists} different adults during the teaching phase.

  ## Mastery Criteria
  {mastery_criterion_text}

  ## Data Collection
  Opportunity-based data recording: each naturally-occasioned trial is scored for independence and topography. IOA scheduled every {ioa_frequency} sessions minimum; target agreement ≥ 80%.

  ## Generalization & Maintenance Plan
  Post-mastery: probe across novel peers, settings, and materials. Maintenance probes weekly for 4 weeks, then monthly. Fade adult-delivered prompts before declaring mastery.