技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
feature-definitions【数据与研究】Model Feast entities, fields, data sources, feature views, on-demand/batch/stream transforms, feature services, labels, permissions basics, validation, and versioning without applying a repo.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill when the user asks how to define, migrate, validate, or troubleshoot Feast Python definition objects: Entity, Field, FileSource/request/push sources, FeatureView, OnDemandFeatureView, BatchFeatureView, StreamFeatureView, FeatureService, label-oriented definitions, and permission metadata basics.
ttl, online/offline, vector metadata, source wiring, ODFV decorators, feature services, and validation errors: stay here.feast apply, feast plan, registry commands, and CLI lifecycle: use ../feature-repos-and-cli/SKILL.md.push, saved datasets, and point-in-time joins: use ../retrieval-and-materialization/SKILL.md.Fields: use ../rag-and-vector-search/SKILL.md.../servers-and-remote/SKILL.md.references/api-reference.md.references/data-modeling.md.references/transformations.md.references/troubleshooting.md.Run the bundled validator against a Python definitions file before apply/materialization work:
python skills/disco/feast/sub-skills/feature-definitions/scripts/validate_feature_definitions.py path/to/features.py
Expected output lists discovered Feast object types/names and reports local validation warnings/errors. The script imports the installed feast package if available, never calls FeatureStore.apply, plan, materialize, push, retrieval APIs, or any server command, and does not require a Feast source checkout.