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feast-user-guide

Guide for working with Feast (Feature Store) — defining features, configuring feature_store.yaml, retrieving features online/offline, using the CLI, and building RAG retrieval pipelines. Use when the user asks about creating entities, feature views, on-demand feature views, stream feature views, feature services, data sources, feature_store.yaml configuration, feast apply/materialize commands, online or historical feature retrieval, or vector-based document retrieval with Feast.

73

Quality

90%

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SKILL.md
Quality
Evals
Security

Quality

Content

80%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is an actionable, well-structured overview with executable examples and clean progressive disclosure into real reference files. Its main weakness is the absence of explicit validation/feedback checkpoints for the batch and destructive operations it documents.

Suggestions

Add explicit validation checkpoints after batch/destructive commands (e.g., run 'feast entities list' / 'feast plan' to verify 'feast apply' succeeded before proceeding).

Trim introductory prose that restates concepts Claude already knows (e.g., the Entity and Data Sources one-liners) in favor of leading directly with the executable code.

Show how to confirm materialization succeeded (e.g., querying the online store) to introduce a feedback loop into the materialization workflow.

DimensionReasoningScore

Conciseness

Largely lean with section headers and executable code, but a few prose intros ('An entity is a collection of semantically related features', 'Data sources describe where raw feature data lives') restate concepts Claude already knows.

4 / 5

Actionability

Copy-paste ready, executable code blocks for Entity, FeatureView, OnDemandFeatureView, retrieval, and materialization, plus a concrete CLI command table — coverage is specific and complete.

5 / 5

Workflow Clarity

The Quick Start gives a rough init→apply sequence, but batch/registration operations (feast apply, materialize, teardown) lack explicit validation checkpoints or feedback loops, capping workflow clarity per the rubric.

3 / 5

Progressive Disclosure

A clear overview body with well-signaled, one-level-deep references (feature-definitions.md, configuration.md, retrieval-and-rag.md) that are real files, splitting detail appropriately.

5 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, comprehensive, and explicitly structured with both a capabilities list and a natural-language 'Use when' trigger clause. It uses third person and avoids fluff or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'defining features, configuring feature_store.yaml, retrieving features online/offline, using the CLI, and building RAG retrieval pipelines' — covering the skill comprehensively.

5 / 5

Completeness

Explicitly answers both 'what' (the enumerated actions) and 'when' via a concrete 'Use when the user asks about...' clause listing specific triggers.

5 / 5

Trigger Term Quality

Comprehensive natural terms including synonyms and command names: feature_store.yaml, entities, feature views, on-demand feature views, feast apply/materialize, online and historical feature retrieval, vector-based document retrieval.

5 / 5

Distinctiveness Conflict Risk

A clearly defined Feast (Feature Store) niche with distinct, domain-specific triggers that are unlikely to fire for unrelated skills.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
feast-dev/feast
Reviewed

Table of Contents

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