---
# Model Selection Policy
# Used by model-selection/SKILL.md as the routing and ranking source of truth.
# Edit this file to adjust priorities without touching the skill logic.

default_output_count: 3
always_include_budget_option: true
always_include_frontier_option: true

# Scoring weights (must sum to 1.0)
weights:
  task_fit: 0.40          # primary_tasks match
  modality_fit: 0.20      # input/output/conditioning modality match
  compute_fit: 0.15       # compute_tier within stated budget
  openness_fit: 0.10      # open_weights + commercial_use alignment
  maturity_score: 0.10    # established > recent > experimental
  deployment_practicality: 0.05  # single_gpu_feasible, no gating, etc.

# Penalties applied on top of the weighted score
penalties:
  gated: 0.20                    # gated=true and user wants frictionless
  over_budget_compute: 0.25      # compute_tier exceeds stated budget
  unknown_commercial_status: 0.15  # commercial_use=unknown when deployment is commercial
  legacy_model: 0.10             # status=legacy or exclusion_tags contains prefer_*_over_this
  non_commercial_commercial_deploy: 0.30  # commercial_use=non_commercial_only|research_only when commercial

# Default assumed compute budget when none stated
default_compute_budget: single_gpu_midmem

# Task → ordered candidate list (first is preferred; apply compute filter after)
routing:
  embedding:
    - esmc_300m          # strongest small embedder (non-commercial)
    - esm2_t33_650m      # best commercial default
    - saprot_650m        # structure-aware, if structure available
    - ankh3_large        # multi-task pretrain, strong generalization
    - prot_t5_xl_enc     # T5 ecosystem; slower but competitive
    - esm2_t30_150m      # budget fallback

  variant_effect_prediction:
    - esm1v_650m         # purpose-built masked-marginal scorer
    - tranception        # autoregressive + retrieval; best on fitness landscapes
    - saprot_650m        # structure-aware variant effect if structure available
    - esm2_t33_650m      # general fallback with PLL scoring
    - esm2_t6_8m       # budget option with PLL scoring

  fitness_prediction:
    - tranception
    - esm1v_650m
    - esm2_t33_650m

  sequence_generation:
    - protgpt2           # established decoder-only baseline
    - dplm_650m          # diffusion; better diversity
    - esm3_sm_open       # generative multimodal (non-commercial)

  controlled_generation:
    - esm3_sm_open       # best open for structure/function conditioned
    - dplm2_150m         # joint seq+structure, lighter
    - nv_la_proteina     # frontier all-atom (gated, research-only)

  folding:
    - esmfold_v1         # fast MSA-free, open weights
    - boltz_2            # better for complexes / affinity
    - dplm2_150m         # lighter; co-designs sequence + structure

  inverse_folding:
    - esm_if1            # purpose-built, small, commercial
    - dplm_650m          # diffusion-based; good diversity
    - dplm2_150m         # joint seq+structure design

  motif_scaffolding:
    - dplm_650m
    - dplm2_150m
    - nv_la_proteina     # frontier all-atom scaffolding

  binder_design:
    - boltzgen_1         # purpose-built binder generator
    - nv_la_proteina     # all-atom frontier design
    - boltz_2            # complex prediction + affinity

  complex_prediction:
    - boltz_2
    - boltzgen_1

  binding_affinity_prediction:
    - boltz_2

  nucleotide_representation:
    - nucleotide_transformer_v2_500m

  biomedical_nlp:
    - biomedbert_base    # MIT, PubMed abstracts
    - biobert_base       # Apache 2.0, PubMed + PMC

# Hard filter rules (applied before scoring)
# Models failing any applicable rule are excluded from candidates.
hard_filters:
  - rule: task_mismatch
    description: Exclude if task not in model primary_tasks or secondary_tasks
  - rule: compute_over_budget
    description: Exclude if compute_tier > stated budget (unless no candidate passes)
  - rule: gated_frictionless
    description: Exclude gated models when user explicitly requests frictionless access
  - rule: commercial_restriction
    description: Exclude non_commercial_only/research_only models when commercial deployment is implied
  - rule: wrong_category
    description: Exclude nucleotide_language_model and biomedical_text_model for protein tasks (and vice versa)