---

name: feast description: "Use for Feast feature store tasks: feature repositories, definitions, CLI, retrieval, materialization, serving, RAG/vector search, integrations, and Feast contributor workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Feast

Use this skill when the user asks to work with Feast as a feature store or to contribute to the Feast codebase. Feast manages feature definitions, registry state, offline training retrieval, online serving, materialization, feature servers, vector/RAG retrieval, and optional data infrastructure integrations.

Start Here

1. If the user is operating a Feast project, identify the feature repository root and feature_store.yaml first.
2. If the user is defining objects, inspect or create Python definitions before running feast apply.
3. If the user is retrieving features, confirm the definitions were applied and the online store was materialized or pushed.
4. If the user is using remote services, auth, cloud stores, vector DBs, or clusters, confirm the required Feast extra, service, credentials, and safe validation path before running commands.
5. If the user is changing Feast source code, use targeted tests and lint/type checks before broad suites.

Install And Smoke Check

Public install patterns:

pip install feast
pip install "feast[redis]"        # example optional backend extra
pip install "feast[snowflake]"    # example offline store extra

Minimal import and CLI checks:

python - <<'PY'
import feast
from feast import FeatureStore, Entity, FeatureView, Field
print("Feast import OK", getattr(feast, "__version__", "unknown"))
PY
feast --help
feast version

Run scripts/check_feast_environment.py --help when you need a bundled diagnostic for installed Feast, CLI availability, and optional extras.

Route By Task

User request	Use this sub-skill
Create a feature repo, inspect feature_store.yaml, choose CLI commands, run init, apply, plan, list objects, or handle registry paths	sub-skills/feature-repos-and-cli/SKILL.md
Define Entity, Field, data sources, FeatureView, OnDemandFeatureView, stream/batch feature views, feature services, labels, or permission metadata	sub-skills/feature-definitions/SKILL.md
Retrieve historical or online features, materialize, push rows, build training datasets, diagnose null/stale online values, or handle saved datasets	sub-skills/retrieval-and-materialization/SKILL.md
Run or debug feature server, offline server, registry server, transformation server, MCP, TLS, auth/RBAC, remote stores, or production serving topology	sub-skills/servers-and-remote/SKILL.md
Build RAG or vector-search workflows with vector fields, vector online stores, document embeddings, chunking, or retrieve_online_documents	sub-skills/rag-and-vector-search/SKILL.md
Select optional extras, configure stores/providers/compute engines, use dbt/MLflow/OpenLineage/DQM, or design custom store/provider extensions	sub-skills/integrations-and-extensibility/SKILL.md
Modify Feast source, choose focused tests, run Ruff/MyPy/Pytest, update docs/protos, work on Go/Java/operator code, or prepare a PR	sub-skills/repo-development/SKILL.md

Common Decision Points

• Local quickstart: prefer local provider, file offline store, SQLite online store, and the feature-repos-and-cli plus retrieval-and-materialization routes.
• Definition errors: stay in feature-definitions until constructors, schemas, sources, and feature services are valid; then route to feature-repos-and-cli for apply.
• Online nulls: check materialization/push windows, entity join keys, feature refs, and registry state in retrieval-and-materialization before blaming serving.
• Remote/service errors: use servers-and-remote for endpoint/auth/TLS behavior and integrations-and-extensibility for missing extras or backend selectors.
• Vector/RAG: define vector Field metadata in feature-definitions, then use rag-and-vector-search for vector store config and document retrieval.
• Contributing: use repo-development; do not run broad integration suites or service-backed examples unless prerequisites and safety are clear.

Bundled References And Scripts

• Read references/repo-provenance.md before deciding whether this skill matches a current Feast checkout or should be refreshed.
• Read references/troubleshooting.md for cross-cutting install/import, CLI discovery, optional dependency, and routing failures.
• Run scripts/check_feast_environment.py for a safe import/CLI/extra diagnostic that does not contact external services.

Safety Rules

• Do not run feast teardown, cloud-backed materialization, Kubernetes/operator examples, release scripts, or service-backed integration tests without explicit confirmation and prerequisites.
• Do not assume optional extras are installed. Check imports and route backend selection to integrations-and-extensibility.
• Keep generated project examples local and tiny unless the user explicitly asks for production/cloud/service deployment.
• For source changes, start with the smallest relevant lint/type/test commands, then broaden only if needed.