diff --git a/PRPs/INITIAL/INITIAL-MLZOO-A-foundation-feature-frames.md b/PRPs/INITIAL/INITIAL-MLZOO-A-foundation-feature-frames.md
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+# INITIAL-MLZOO-A-foundation-feature-frames.md - Feature-Aware Forecasting Foundation
+
+## FEATURE:
+
+Create the foundation for feature-aware forecasting in ForecastLabAI.
+
+This is the first MLZOO PRP input and should become PRP-29. It must not implement LightGBM, XGBoost, Prophet-like models, frontend UI, explainability UI, hyperparameter search, or portfolio/global orchestration. Its job is to make the existing forecasting layer capable of supporting future advanced ML models without breaking current baseline forecasters.
+
+Goals:
+
+- Define a feature-aware forecasting contract that supports `fit(y, X=None)` and `predict(horizon, X=None)`.
+- Preserve existing target-only baseline models: `naive`, `seasonal_naive`, and `moving_average`.
+- Define historical training feature-frame requirements.
+- Define future prediction feature-frame requirements.
+- Add or document leakage-safe feature-frame generation rules.
+- Add load-bearing leakage tests that prove future rows do not use future target values.
+- Make future advanced models possible without adding their dependencies yet.
+
+Expected user value:
+
+- ForecastLabAI gains a safe foundation for serious ML forecasting.
+- Future LightGBM/XGBoost/Prophet-like work can build on a tested frame contract.
+- Scenario simulation and explainability can later depend on a consistent feature-frame interface.
+
+Recommended user story:
+
+As a forecasting engineer,
+I want a leakage-safe feature-frame contract for training and prediction,
+So that advanced ML models can be added without breaking baseline models or leaking future data.
+
+Out of scope:
+
+- LightGBM implementation.
+- XGBoost implementation.
+- Prophet-like implementation.
+- New database migrations unless absolutely required.
+- Frontend pages.
+- Agent tools.
+- Hyperparameter search.
+
+## EXAMPLES:
+
+Read these before PRP creation:
+
+- `docs/optional-features/05-advanced-ml-model-zoo.md`
+  - Full feature vision and risks.
+
+- `PRPs/INITIAL/INITIAL-5.md`
+  - Existing forecasting model brief.
+
+- `docs/PHASE/4-FORECASTING.md`
+  - Current forecasting layer documentation.
+
+- `app/features/forecasting/models.py`
+  - Existing `BaseForecaster` and baseline model implementations.
+
+- `app/features/forecasting/schemas.py`
+  - Existing model config schemas and discriminated union pattern.
+
+- `app/features/forecasting/service.py`
+  - Existing train/predict orchestration.
+
+- `app/features/forecasting/persistence.py`
+  - Existing `ModelBundle` persistence.
+
+- `app/features/featuresets/service.py`
+  - Existing time-safe feature computation.
+
+- `app/features/featuresets/schemas.py`
+  - Feature configuration schemas.
+
+- `app/features/featuresets/tests/test_leakage.py`
+  - Existing leakage tests to mirror and extend.
+
+- `app/features/backtesting/service.py`
+  - Current backtesting integration points.
+
+Potential example artifacts:
+
+- `examples/models/feature_frame_contract.md`
+  - Describes historical and future frame shape, required columns, safe/unsafe feature classes.
+
+## DOCUMENTATION:
+
+- scikit-learn estimator interface conventions: https://scikit-learn.org/stable/developers/develop.html
+- scikit-learn Pipeline composition: https://scikit-learn.org/stable/modules/compose.html
+- scikit-learn TimeSeriesSplit: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
+- Pandas time series documentation: https://pandas.pydata.org/docs/user_guide/timeseries.html
+- Pydantic documentation: https://docs.pydantic.dev/latest/
+- Joblib persistence documentation: https://joblib.readthedocs.io/en/stable/persistence.html
+
+## OTHER CONSIDERATIONS:
+
+This PRP is primarily about contracts and leakage safety.
+
+Required decisions:
+
+- How to represent feature-aware models without forcing every baseline model to require `X`.
+- Whether to introduce a `FeatureAwareForecaster` protocol/base class or extend the existing base interface only.
+- Where historical training frames are built.
+- Where future prediction frames are built.
+- Which feature classes are safe for future frames:
+  - Safe: calendar features known in advance.
+  - Conditionally safe: lag/rolling features generated from historical tail and prior predictions.
+  - Unsafe unless explicitly supplied: future price, promotion, inventory, markdown, exogenous signals.
+- How to reject missing future features instead of silently filling misleading defaults.
+
+Validation expectations:
+
+- Existing baseline forecasting tests still pass.
+- New feature-frame contract tests exist.
+- New leakage tests prove future target values are not used.
+- Backtesting remains time-safe.
+- `uv run pytest -q -m "not integration"` should pass.
+- `uv run ruff check app tests` should pass for touched Python code.
+
+Important gotchas:
+
+- Do not break current target-only baseline forecasters.
+- Do not add LightGBM or other heavy ML dependencies in this PRP.
+- Do not silently convert unknown future exogenous values into zeros.
+- Do not let training frames include rows after the cutoff date.
+- Do not let future prediction frames read true future targets.
+
diff --git a/PRPs/INITIAL/INITIAL-MLZOO-B-lightgbm-first-model.md b/PRPs/INITIAL/INITIAL-MLZOO-B-lightgbm-first-model.md
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--- /dev/null
+++ b/PRPs/INITIAL/INITIAL-MLZOO-B-lightgbm-first-model.md
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+# INITIAL-MLZOO-B-lightgbm-first-model.md - LightGBM First Advanced Model
+
+## FEATURE:
+
+Add the first advanced feature-aware model to ForecastLabAI after the MLZOO foundation is merged.
+
+Preferred model: LightGBM.
+
+Fallback model: sklearn `HistGradientBoostingRegressor` or another sklearn-native gradient boosting model if LightGBM creates unacceptable dependency or CI risk.
+
+This PRP must depend on `INITIAL-MLZOO-A-foundation-feature-frames.md` being implemented first.
+
+Goals:
+
+- Add one advanced model config schema.
+- Add one feature-aware model implementation.
+- Support deterministic training.
+- Integrate with forecasting train/predict.
+- Integrate with backtesting.
+- Persist model metadata needed for reproducibility.
+- Preserve all existing baseline model behavior.
+
+Out of scope:
+
+- XGBoost.
+- Prophet-like models.
+- Hyperparameter search.
+- Portfolio/global models.
+- Frontend model administration.
+- Explainability UI.
+
+## EXAMPLES:
+
+Read these before PRP creation:
+
+- `PRPs/INITIAL/INITIAL-MLZOO-A-foundation-feature-frames.md`
+  - Required prerequisite.
+
+- `docs/optional-features/05-advanced-ml-model-zoo.md`
+  - Full advanced model vision.
+
+- `app/features/forecasting/models.py`
+  - Model factory and baseline model patterns.
+
+- `app/features/forecasting/schemas.py`
+  - Model config schema patterns.
+
+- `app/features/forecasting/service.py`
+  - Training/prediction service integration.
+
+- `app/features/forecasting/persistence.py`
+  - Model bundle save/load behavior.
+
+- `app/features/backtesting/service.py`
+  - Backtesting orchestration.
+
+- `app/features/registry/service.py`
+  - Registry run metadata patterns.
+
+Potential example artifacts:
+
+- `examples/models/advanced_lightgbm.py`
+  - Minimal training/prediction example.
+
+## DOCUMENTATION:
+
+- LightGBM documentation: https://lightgbm.readthedocs.io/
+- LightGBM Python API: https://lightgbm.readthedocs.io/en/stable/Python-API.html
+- LightGBM parameters: https://lightgbm.readthedocs.io/en/stable/Parameters.html
+- scikit-learn HistGradientBoostingRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html
+- scikit-learn model persistence: https://scikit-learn.org/stable/model_persistence.html
+- Joblib persistence documentation: https://joblib.readthedocs.io/en/stable/persistence.html
+- Pydantic documentation: https://docs.pydantic.dev/latest/
+
+## OTHER CONSIDERATIONS:
+
+Dependency strategy is the main open risk.
+
+Required decisions:
+
+- Whether to add LightGBM as a hard dependency, optional dependency group, or defer to sklearn fallback.
+- Exact advanced model config fields.
+- How model dependency versions are captured in registry/runtime metadata.
+- How prediction rejects missing future feature frames.
+
+Recommended defaults:
+
+- Use fixed `random_state` from settings.
+- Start with single store/product training.
+- Keep the first config conservative.
+- Avoid hyperparameter search.
+- Persist feature column order.
+
+Validation expectations:
+
+- Config schema tests.
+- Deterministic training tests.
+- Save/load persistence tests.
+- Backtesting integration test comparing baseline and advanced model path.
+- Tests proving baselines still work unchanged.
+
diff --git a/PRPs/INITIAL/INITIAL-MLZOO-C-xgboost-prophet-extensions.md b/PRPs/INITIAL/INITIAL-MLZOO-C-xgboost-prophet-extensions.md
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+++ b/PRPs/INITIAL/INITIAL-MLZOO-C-xgboost-prophet-extensions.md
@@ -0,0 +1,59 @@
+# INITIAL-MLZOO-C-xgboost-prophet-extensions.md - XGBoost and Prophet-like Extensions
+
+## FEATURE:
+
+Extend the Advanced ML Model Zoo after the feature-frame foundation and first advanced model path are stable.
+
+This INITIAL is for later work, not PRP-29.
+
+Goals:
+
+- Add XGBoost as a second tree-based feature-aware model.
+- Add a Prophet-like additive model path or choose the real Prophet dependency if justified.
+- Support holiday/regressor-style features where appropriate.
+- Add model-family-specific validation and metadata.
+
+Out of scope:
+
+- Foundation feature-frame work.
+- First advanced model architecture.
+- Frontend/explainability polish unless explicitly needed.
+- Hyperparameter search unless separately scoped.
+
+## EXAMPLES:
+
+Read these before PRP creation:
+
+- `PRPs/INITIAL/INITIAL-MLZOO-A-foundation-feature-frames.md`
+  - Foundation dependency.
+
+- `PRPs/INITIAL/INITIAL-MLZOO-B-lightgbm-first-model.md`
+  - First advanced model pattern to follow.
+
+- `app/features/forecasting/models.py`
+  - Model factory and advanced model pattern.
+
+- `app/features/forecasting/schemas.py`
+  - Config schema pattern.
+
+- `app/features/featuresets/service.py`
+  - Regressor and calendar feature source.
+
+## DOCUMENTATION:
+
+- XGBoost documentation: https://xgboost.readthedocs.io/en/stable/
+- XGBoost Python package documentation: https://xgboost.readthedocs.io/en/stable/python/
+- XGBoost parameters: https://xgboost.readthedocs.io/en/stable/parameter.html
+- Prophet documentation: https://facebook.github.io/prophet/docs/quick_start.html
+- Prophet seasonality, holidays, and regressors: https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html
+- scikit-learn model persistence: https://scikit-learn.org/stable/model_persistence.html
+- Pandas time series documentation: https://pandas.pydata.org/docs/user_guide/timeseries.html
+
+## OTHER CONSIDERATIONS:
+
+- XGBoost should mirror the first advanced model path where possible.
+- Prophet-like work should be carefully evaluated because dependency weight and API shape differ from sklearn-style regressors.
+- Real Prophet support should be chosen only if install/runtime constraints are acceptable.
+- A lightweight additive sklearn model may be safer than the real Prophet dependency.
+- Holiday/regressor support must use known-in-advance or explicitly supplied future values.
+
diff --git a/PRPs/INITIAL/INITIAL-MLZOO-D-frontend-registry-explainability.md b/PRPs/INITIAL/INITIAL-MLZOO-D-frontend-registry-explainability.md
new file mode 100644
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--- /dev/null
+++ b/PRPs/INITIAL/INITIAL-MLZOO-D-frontend-registry-explainability.md
@@ -0,0 +1,69 @@
+# INITIAL-MLZOO-D-frontend-registry-explainability.md - Frontend, Registry, and Explainability Polish
+
+## FEATURE:
+
+Expose Advanced ML Model Zoo capabilities in the product after backend model contracts and at least one advanced model are stable.
+
+This INITIAL is for later work, not PRP-29.
+
+Goals:
+
+- Add model selection UI where useful.
+- Surface advanced model metadata in run detail and comparison pages.
+- Show feature config, feature columns, dependency versions, and model family metadata.
+- Add basic feature importance or explanation hooks where available.
+- Update docs/admin surfaces so operators understand advanced model constraints.
+
+Out of scope:
+
+- Core feature-frame foundation.
+- First advanced model backend implementation.
+- XGBoost/Prophet backend implementation.
+- Full SHAP explainability unless separately scoped.
+
+## EXAMPLES:
+
+Read these before PRP creation:
+
+- `PRPs/INITIAL/INITIAL-MLZOO-A-foundation-feature-frames.md`
+  - Foundation dependency.
+
+- `PRPs/INITIAL/INITIAL-MLZOO-B-lightgbm-first-model.md`
+  - First advanced model dependency.
+
+- `frontend/src/pages/explorer/runs.tsx`
+  - Existing run table.
+
+- `frontend/src/pages/explorer/run-detail.tsx`
+  - Existing run detail surface.
+
+- `frontend/src/pages/explorer/run-compare.tsx`
+  - Existing comparison surface.
+
+- `frontend/src/pages/visualize/forecast.tsx`
+  - Forecast visualization page.
+
+- `frontend/src/pages/visualize/backtest.tsx`
+  - Backtest visualization page.
+
+- `app/features/registry/schemas.py`
+  - Backend response contracts for run metadata.
+
+## DOCUMENTATION:
+
+- React Router documentation: https://reactrouter.com/home
+- TanStack Query documentation: https://tanstack.com/query/latest/docs/framework/react/overview
+- TanStack Table documentation: https://tanstack.com/table/latest/docs/overview
+- shadcn/ui documentation: https://ui.shadcn.com/docs
+- Recharts documentation: https://recharts.org/en-US/
+- SHAP documentation: https://shap.readthedocs.io/en/stable/
+- scikit-learn permutation importance: https://scikit-learn.org/stable/modules/permutation_importance.html
+
+## OTHER CONSIDERATIONS:
+
+- Do not create frontend controls before backend contracts are stable.
+- Avoid adding a large admin panel if run detail and comparison pages are enough.
+- Keep advanced model metadata readable and compact.
+- Feature importance must be clearly labeled as model-derived, not causal truth.
+- Browser QA is required for all frontend additions.
+
diff --git a/PRPs/INITIAL/INITIAL-MLZOO-index.md b/PRPs/INITIAL/INITIAL-MLZOO-index.md
new file mode 100644
index 00000000..2a07a99b
--- /dev/null
+++ b/PRPs/INITIAL/INITIAL-MLZOO-index.md
@@ -0,0 +1,76 @@
+# INITIAL-MLZOO-index.md - Advanced ML Model Zoo Roadmap
+
+## FEATURE:
+
+Split the Advanced ML Model Zoo into multiple INITIAL briefs so each future PRP can remain small, reviewable, and implementation-safe.
+
+This index is the roadmap for the MLZOO sequence. Do not create one PRP that implements the full model zoo. The correct flow is:
+
+1. Use this index to understand the full architecture.
+2. Use `INITIAL-MLZOO-A-foundation-feature-frames.md` as the first PRP input.
+3. Implement and merge the foundation before creating PRPs for later parts.
+4. Promote B, C, and D into PRPs only after their prerequisites are stable.
+
+Recommended PRP sequence:
+
+| Order | INITIAL | Intended PRP | Purpose |
+| --- | --- | --- | --- |
+| 1 | `INITIAL-MLZOO-A-foundation-feature-frames.md` | PRP-29 | Feature-aware forecasting foundation and leakage-safe frame contracts |
+| 2 | `INITIAL-MLZOO-B-lightgbm-first-model.md` | Future PRP | First advanced model path with LightGBM or sklearn fallback |
+| 3 | `INITIAL-MLZOO-C-xgboost-prophet-extensions.md` | Future PRP | XGBoost and Prophet-like extensions |
+| 4 | `INITIAL-MLZOO-D-frontend-registry-explainability.md` | Future PRP | UI, registry surfacing, and explanation polish |
+
+Dependency graph:
+
+```text
+A. Foundation feature frames
+  -> B. LightGBM first model
+      -> C. XGBoost / Prophet-like extensions
+      -> D. Frontend / registry / explainability
+```
+
+The full vision is documented in `docs/optional-features/05-advanced-ml-model-zoo.md`.
+
+## EXAMPLES:
+
+Read these before creating any MLZOO PRP:
+
+- `docs/optional-features/05-advanced-ml-model-zoo.md`
+  - Full optional-feature concept and documentation links.
+
+- `PRPs/INITIAL/INITIAL-5.md`
+  - Earlier forecasting model brief, including baseline model zoo and global ML hooks.
+
+- `docs/PHASE/4-FORECASTING.md`
+  - Completed forecasting phase, model interface, configs, persistence, service, and API behavior.
+
+- `app/features/forecasting/models.py`
+  - Current baseline model interface.
+
+- `app/features/featuresets/service.py`
+  - Existing time-safe feature engineering.
+
+- `app/features/featuresets/tests/test_leakage.py`
+  - Existing leakage-safety testing pattern.
+
+## DOCUMENTATION:
+
+- LightGBM documentation: https://lightgbm.readthedocs.io/
+- XGBoost documentation: https://xgboost.readthedocs.io/en/stable/
+- Prophet documentation: https://facebook.github.io/prophet/docs/quick_start.html
+- scikit-learn model persistence: https://scikit-learn.org/stable/model_persistence.html
+- scikit-learn TimeSeriesSplit: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
+- scikit-learn Pipeline composition: https://scikit-learn.org/stable/modules/compose.html
+- Pandas time series documentation: https://pandas.pydata.org/docs/user_guide/timeseries.html
+- Joblib persistence documentation: https://joblib.readthedocs.io/en/stable/persistence.html
+- Pydantic documentation: https://docs.pydantic.dev/latest/
+- FastAPI documentation: https://fastapi.tiangolo.com/
+
+## OTHER CONSIDERATIONS:
+
+- The first PRP should be generated from `INITIAL-MLZOO-A-foundation-feature-frames.md`.
+- Do not implement LightGBM before the feature-frame contracts and leakage tests are stable.
+- Do not implement XGBoost or Prophet-like models before the first advanced model path proves the architecture.
+- Do not add frontend/explainability scope before backend metadata and persistence contracts are stable.
+- Keep each PRP to one branch and one reviewable unit.
+
diff --git a/docs/optional-features/10-baseforecaster-feature-contract.md b/docs/optional-features/10-baseforecaster-feature-contract.md
new file mode 100644
index 00000000..4d6f89a1
--- /dev/null
+++ b/docs/optional-features/10-baseforecaster-feature-contract.md
@@ -0,0 +1,116 @@
+# BaseForecaster Feature Contract
+
+## Summary
+
+Formalize the existing `BaseForecaster` interface as the canonical model contract for both target-only baseline models and feature-aware ML models. Add a `requires_features` class attribute or property so services can branch on model capability without `isinstance` checks or a new `FeatureAwareForecaster` subclass.
+
+This is a small but important foundation item for the Advanced ML Model Zoo.
+
+## Why It Fits ForecastLabAI
+
+ForecastLabAI already has a forecasting model interface where models expose:
+
+- `fit(y, X=None)`
+- `predict(horizon, X=None)`
+
+Baseline models can ignore `X`; regression and future advanced models need `X`. Introducing a second base class too early would create inheritance churn without solving the harder platform problems: feature-frame contracts, future feature availability, leakage safety, and train/serve skew.
+
+## User Value
+
+- Keeps current baseline behavior stable.
+- Makes feature-aware model support explicit.
+- Prepares LightGBM/XGBoost/Prophet-like work without API churn.
+- Avoids brittle `isinstance` checks in services.
+- Reduces persistence risk for existing joblib model bundles.
+
+## Proposed Design
+
+Keep `BaseForecaster` as the single canonical model interface.
+
+Add a class-level capability flag:
+
+```python
+requires_features: ClassVar[bool] = False
+```
+
+Baseline models:
+
+```python
+class NaiveForecaster(BaseForecaster):
+    requires_features = False
+```
+
+Feature-aware models:
+
+```python
+class RegressionForecaster(BaseForecaster):
+    requires_features = True
+```
+
+Service code branches on the model contract:
+
+```python
+if model.requires_features:
+    # require and validate X / X_future
+else:
+    # y-only baseline path
+```
+
+## Backend Design
+
+Likely files:
+
+- `app/features/forecasting/models.py`
+- `app/features/forecasting/service.py`
+- `app/features/forecasting/tests/test_models.py`
+- `examples/models/model_interface.md`
+- `docs/PHASE/4-FORECASTING.md`
+
+The change should document that:
+
+- `fit(y, X=None)` is the universal train contract.
+- `predict(horizon, X=None)` is the universal predict contract.
+- `requires_features = False` models may ignore `X`.
+- `requires_features = True` models must receive valid feature frames.
+- A `FeatureAwareForecaster` subclass should be revisited only after multiple advanced model families need shared behavior beyond the flag.
+
+## MVP Scope
+
+- Add `requires_features` to the model interface.
+- Set it explicitly on existing baseline and regression models.
+- Update service branching where it currently relies on model type checks.
+- Add tests proving baseline models ignore `X` and regression requires it.
+- Update model interface documentation.
+
+## Full Version
+
+- Add richer capability flags if needed:
+  - `supports_prediction_intervals`
+  - `supports_feature_importance`
+  - `supports_exogenous_future`
+  - `supports_recursive_prediction`
+- Introduce a `FeatureAwareForecaster` subclass only when shared advanced-model behavior justifies the abstraction.
+
+## Risks
+
+- Adding a flag without tests can become another implicit contract.
+- Service code must not silently pass `None` into feature-aware models.
+- Documentation must be precise so future LightGBM work does not reinterpret the contract.
+
+## Validation Plan
+
+- Unit tests for each existing model's `requires_features` value.
+- Unit tests proving baseline models still fit/predict with `X=None`.
+- Unit tests proving feature-aware models reject missing required features.
+- Regression tests for existing forecasting service behavior.
+- `uv run pytest -q -m "not integration"`
+- `uv run ruff check app tests`
+
+## Documentation
+
+- scikit-learn estimator development guide: https://scikit-learn.org/stable/developers/develop.html
+- scikit-learn Pipeline composition: https://scikit-learn.org/stable/modules/compose.html
+- scikit-learn model persistence: https://scikit-learn.org/stable/model_persistence.html
+- Joblib persistence documentation: https://joblib.readthedocs.io/en/stable/persistence.html
+- Pydantic documentation: https://docs.pydantic.dev/latest/
+
diff --git a/docs/optional-features/11-feature-aware-predict-serving.md b/docs/optional-features/11-feature-aware-predict-serving.md
new file mode 100644
index 00000000..1c745bd0
--- /dev/null
+++ b/docs/optional-features/11-feature-aware-predict-serving.md
@@ -0,0 +1,103 @@
+# Feature-Aware Forecasting Predict Serving
+
+## Summary
+
+Extend `POST /forecasting/predict` so feature-aware models can produce forecasts outside `/scenarios/simulate` when a leakage-safe future feature frame can be constructed or supplied.
+
+Today feature-aware regression models are rejected by `/forecasting/predict` because the endpoint cannot supply future `X`. That is correct for the current foundation, but it becomes a missing serving capability once MLZOO-B introduces the first advanced model.
+
+## Why It Fits ForecastLabAI
+
+ForecastLabAI is evolving from y-only baseline forecasting toward ML forecasting with `y + X`. Scenario simulation already provides a context where future assumptions can produce `X_future`. The standard forecasting endpoint needs a safe, explicit serving path for feature-aware models too, but only after the shared feature-frame contract is stable.
+
+## User Value
+
+- Advanced models become usable through the normal forecast API.
+- Forecast visualization can load predictions from feature-aware jobs.
+- LightGBM and future XGBoost models can serve without requiring the scenario UI.
+- The product can distinguish baseline forecasts, scenario forecasts, and assumptions-free ML forecasts.
+
+## Proposed Design
+
+Add feature-aware predict support in a later PRP, not in the foundation-only MLZOO-A work.
+
+Supported future-frame modes:
+
+1. **Calendar-only / history-tail mode**
+   - Use known future calendar features.
+   - Use historical tail for lag and rolling seeds.
+   - Generate recursive target-derived features only from history and prior predictions.
+   - Reject unsafe feature columns that require explicit future assumptions.
+
+2. **Supplied future-frame mode**
+   - Client or service supplies validated `X_future`.
+   - API verifies required columns, order, dtypes, horizon length, and no target leakage.
+
+3. **Scenario-backed mode**
+   - Reuse saved scenario assumptions to construct `X_future`.
+   - Clearly mark the result as scenario-conditioned.
+
+## Backend Design
+
+Likely files:
+
+- `app/features/forecasting/routes.py`
+- `app/features/forecasting/service.py`
+- `app/features/forecasting/schemas.py`
+- `app/shared/feature_frames/`
+- `app/features/jobs/service.py`
+- `frontend/src/pages/visualize/forecast.tsx`
+
+Possible request additions:
+
+- `feature_mode`: `baseline`, `history_calendar`, `supplied_frame`, `scenario`
+- `future_frame`: optional structured future features
+- `scenario_id`: optional saved scenario reference
+- `history_tail_days`: optional bounded history window
+
+The endpoint must reject feature-aware predictions when required future features are unavailable.
+
+## MVP Scope
+
+- Keep current rejection behavior until the first advanced model lands.
+- Add a dedicated PRP later for `history_calendar` mode.
+- Support only safe known-ahead features and recursive target-derived features.
+- Return metadata that states how `X_future` was built.
+
+## Full Version
+
+- Supplied future-frame mode.
+- Scenario-backed mode.
+- Prediction interval support where available.
+- Feature availability diagnostics.
+- UI warnings when forecasts are assumptions-free vs scenario-conditioned.
+
+## Risks
+
+- Assumptions-free future frames can be misleading if users expect promotions, inventory, or exogenous events to be included.
+- Recursive lag generation can leak future targets if implemented incorrectly.
+- Train/serve skew can silently degrade advanced model quality.
+- API shape can become too broad if scenario, supplied-frame, and history-calendar modes are mixed without clear validation.
+
+## Validation Plan
+
+- Unit tests for future-frame validation.
+- Leakage tests proving `X_future` never reads true future targets.
+- API tests:
+  - baseline model predict still works
+  - feature-aware predict rejects missing future features
+  - feature-aware predict accepts valid history-calendar frame
+  - unsafe future feature requirements produce clear errors
+- Job result metadata tests.
+- Browser QA for forecast visualization using a feature-aware prediction job.
+
+## Documentation
+
+- FastAPI documentation: https://fastapi.tiangolo.com/
+- Pydantic documentation: https://docs.pydantic.dev/latest/
+- scikit-learn TimeSeriesSplit: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
+- Pandas time series documentation: https://pandas.pydata.org/docs/user_guide/timeseries.html
+- LightGBM Python API: https://lightgbm.readthedocs.io/en/stable/Python-API.html
+- XGBoost Python package documentation: https://xgboost.readthedocs.io/en/stable/python/
+- Recharts documentation: https://recharts.org/en-US/
+
diff --git a/docs/optional-features/README.md b/docs/optional-features/README.md
index 6a8a309e..33c7f6b9 100644
--- a/docs/optional-features/README.md
+++ b/docs/optional-features/README.md
@@ -15,6 +15,8 @@ This folder contains implementation-oriented product and architecture notes for
 | Agent Experiment Workbench | [07-agent-experiment-workbench.md](07-agent-experiment-workbench.md) | Strategic | High |
 | Demand Anomaly and Data Quality Monitor | [08-demand-anomaly-data-quality-monitor.md](08-demand-anomaly-data-quality-monitor.md) | Medium-term | Medium |
 | Model Champion/Challenger Governance | [09-model-champion-challenger-governance.md](09-model-champion-challenger-governance.md) | Medium-term | High |
+| BaseForecaster Feature Contract | [10-baseforecaster-feature-contract.md](10-baseforecaster-feature-contract.md) | MLZOO foundation | Low |
+| Feature-Aware Forecasting Predict Serving | [11-feature-aware-predict-serving.md](11-feature-aware-predict-serving.md) | MLZOO follow-up | Medium |
 
 ## Promotion Criteria
 
diff --git a/uv.lock b/uv.lock
index 2abaae1e..6fb0e783 100644
--- a/uv.lock
+++ b/uv.lock
@@ -821,7 +821,7 @@ wheels = [
 
 [[package]]
 name = "forecastlabai"
-version = "0.2.8"
+version = "0.2.15"
 source = { editable = "." }
 dependencies = [
     { name = "alembic" },