# Mode router config — task_class → mode mapping
#
# `preferred_mode` is the first choice for a task class; `fallback_modes`
# get tried in order if the preferred one isn't available (LLM Team can
# return Unknown mode for some, OR the matrix has stronger signal for a
# fallback). `default_model` seeds the mode runner's model field if the
# caller doesn't override.
#
# Modes are dispatched against LLM Team UI (localhost:5000/api/run) for
# now; future Rust-native runners will short-circuit before the proxy.
# See crates/gateway/src/v1/mode.rs for the dispatch path.

[[task_class]]
name = "scrum_review"
# 2026-04-26 pass5 variance test (5 reps × 4 conditions, grok-4.1-fast,
# pathway_memory.rs): composed corpus LOST 5/5 vs isolation (Δ −1.8
# grounded findings, p=0.031). See docs/MODE_RUNNER_TUNING_PLAN.md.
# Default is now isolation — bug fingerprints + adversarial framing +
# file content carries strong models without matrix noise. The
# `codereview_lakehouse` matrix path remains available via force_mode
# (auto-downgrades to isolation on strong models — see the
# is_strong_model gate in crates/gateway/src/v1/mode.rs).
preferred_mode = "codereview_isolation"
fallback_modes = ["codereview_lakehouse", "codereview", "consensus", "ladder"]
default_model = "qwen3-coder:480b"
# Corpora kept defined so experimental modes (codereview_matrix_only,
# pass2/pass5 sweeps) and weak-model rescue rungs can still pull them.
# scrum_findings_v1 is built but EXCLUDED — bake-off showed 24% OOB
# line citations from cross-file drift, only safe with same-file gating.
matrix_corpus = ["lakehouse_arch_v1", "lakehouse_symbols_v1"]

[[task_class]]
name = "contract_analysis"
preferred_mode = "deep_analysis"
fallback_modes = ["research", "extract"]
default_model = "kimi-k2:1t"
matrix_corpus = "chicago_permits_v1"

[[task_class]]
name = "staffing_inference"
# Staffing-domain native enrichment runner — Pass 4 (2026-04-26).
# Same composer architecture as codereview_lakehouse but with staffing
# framing + workers corpus. Validates that the modes-as-prompt-molders
# pattern generalizes beyond code review.
preferred_mode = "staffing_inference_lakehouse"
fallback_modes = ["ladder", "consensus", "pipeline"]
default_model = "openai/gpt-oss-120b:free"
matrix_corpus = "workers_500k_v8"

[[task_class]]
name = "fact_extract"
preferred_mode = "extract"
fallback_modes = ["distill"]
default_model = "qwen2.5"
matrix_corpus = "kb_team_runs_v1"

[[task_class]]
name = "doc_drift_check"
preferred_mode = "drift"
fallback_modes = ["validator"]
default_model = "gpt-oss:120b"
matrix_corpus = "distilled_factual_v20260423095819"

# Fallback when task_class isn't in the table — useful for ad-hoc calls
# during development that don't yet have a mapped mode.
[default]
preferred_mode = "pipeline"
fallback_modes = ["consensus", "ladder"]
default_model = "qwen3.5:latest"