feat(api): POST /agent/run endpoint (orchestrator + RAG, stub-injectable)

fix(agents/tools): parameterize processed_dir + translate HTTPException → ValueError

feat(agents): orchestrator loop (function-calling + tool trace + max-steps gate)

feat(agents): Tool dataclass + registry + 4 tool wrappers (3 pipelines + RAG)

feat(rag): RAGRetriever (load + search → chunks with scores)

feat(rag): ingest CLI (markdown/PDF → chunks → FAISS) + sample KB fixtures

fix(rag/store): copy vectors before in-place normalize_L2 (no caller mutation)

feat(rag): FAISS inner-product store with chunk metadata + roundtrip

feat(rag): fastembed wrapper (Embedder, bge-small-en-v1.5, 384-dim)

feat(rag): paragraph-aware chunker (chunk_text)

fix(test): attach caplog handler to non-propagating explainer logger

test(llm): tighten live integration test (slow marker, log-gated skip, on-topic assertion)

test(llm): add network-gated end-to-end OpenRouter integration test

test(llm): pin 401 short-circuit + 400 try-next-model behavior (red)

test(deploy): assert Dockerfile and Dockerfile.hf stay byte-identical

test(deploy): scan ENV lines only in MLflow kill-switch guard (avoid comment false-positive)

test(deploy): assert Dockerfile seeds EEG fixture, runs all pipelines, drops MLflow kill-switch (RED)

fix(frontend): EEG default path points at fixture (HF container has no data/raw/eeg.fif)

feat(deploy): Hugging Face Spaces Dockerfile + supervisord launcher

feat(api): GET /experiments/runs + POST /experiments/diff (Track 5)

feat(api): POST /explain/eeg + /explain/mri — full-stack Track-1 coverage

feat(llm): modality dispatch — explain(payload, modality) for BBB/EEG/MRI

feat(api): POST /explain/bbb — natural-language rationale endpoint

feat(llm): explainer with deterministic template + OpenRouter fallback

feat(api+frontend): MLflow provenance badge in decision card

feat(api): drift z-score in /predict/bbb response

feat(models): train-time confidence stats stashed on _neurobridge_train_stats

feat(api): POST /pipeline/mri/diagnostics — pre/post ComBat KPIs + long-format rows

feat(pipelines): compute_harmonization_diagnostics — long-format pre/post ComBat for viz

feat(api): expose calibration bin in /predict/bbb response

feat(models): calibration metadata — precision-at-threshold bins on _neurobridge_calibration

feat(api): POST /predict/bbb with prediction, uncertainty, SHAP top-k

feat(models): SHAP top-k explainer for BBB predictions

feat(models): BBB classifier with predict_with_proba uncertainty

feat(frontend): Streamlit dashboard with 3 modality tabs

feat(api): POST /pipeline/{bbb,eeg,mri} dispatch routes

feat(api): scaffold FastAPI app + /health + shared Pydantic schemas

feat(mri): log run params, metrics, and parquet artifact to MLflow

feat(eeg): log run params, metrics, and parquet artifact to MLflow

feat(bbb): log run params, metrics, and parquet artifact to MLflow

feat(core): add MLflow tracking helper with disable env-flag

test: cross-pipeline smoke run for all three modalities

feat(core): extract write_parquet() helper for §6 storage contract

feat(core): extract pin_threads() helper for determinism

fix(mri): handle all-constant features; tighten variance threshold; reorder log

feat(mri): add run_pipeline orchestrator + CLI (NIfTI dir → ComBat Parquet)

fix(mri): pin ComBat transitive deps; clarify np.round(14) intent; guard inputs

feat(mri): add harmonize_combat wrapper around neuroHarmonize.harmonizationLearn

refactor(mri): bind ROI_STATS to callables; guard volume/mask shape mismatch

feat(mri): add extract_features_from_volume (8 ROI octants × 6 stats)