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Feast — open-source feature store. Online and offline serving, point-in-time joins, feature validation, and streaming ingestion. Standardizes feature management across training and production.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/mkurman/zorai --skill feastコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
SOC 職業分類に基づく
SKILL.md を表示中
| name | feast |
| description | Feast — open-source feature store. Online and offline serving, point-in-time joins, feature validation, and streaming ingestion. Standardizes feature management across training and production. |
| tags | ["feast","feature-store","mlops","feature-engineering","online-serving","infrastructure","zorai"] |
Feast is an open-source feature store for production ML, providing offline (batch training data via SQL queries) and online (low-latency serving via Redis, Firestore, or DynamoDB) feature retrieval with point-in-time correctness. Features are versioned, validated, and governed through a registry.
uv pip install feast
from feast import Entity, FeatureView, FileSource, ValueType
from datetime import timedelta
driver = Entity(name="driver_id", value_type=ValueType.INT64, description="Driver identifier")
source = FileSource(path="data/driver_stats.parquet", timestamp_field="event_timestamp")
feature_view = FeatureView(
name="driver_hourly_stats",
entities=[driver],
ttl=timedelta(hours=2),
source=source,
)
feast apply # register in registry
feast serve # online serving at localhost:6566