Conciseness

The body is efficient: one-line concept intros followed by executable code, with references pushed to separate files. It is not 5 because the 'Common Imports' section largely duplicates imports already shown in earlier examples, and the 'feature_store.yaml Minimal Config' section overlaps references/configuration.md, so a few tokens do not earn their place.

Actionability

Most guidance is concrete, executable code (Entity, FeatureView, on-demand view, retrieval, CLI commands, yaml config). It is not 5 because of minor completeness gaps: the vector-search example references undefined 'passage_entity' and 'passages_source', and the RequestSource snippet uses 'Field' and 'Float64' without showing their imports in that block.

Workflow Clarity

The Quick Start gives a clear init → apply sequence and section ordering mirrors the real workflow (define → apply → materialize → retrieve), with 'feast plan — Preview changes without applying' offering a validation path. It is not 5 because no explicit validation checkpoints or error-recovery steps are woven into the apply/materialization workflows; it is not 3 because the primary sequence is unambiguous and a preview command is documented.

Progressive Disclosure

The body is a concise overview with three one-level-deep reference files (references/feature-definitions.md, references/configuration.md, references/retrieval-and-rag.md), all verified to exist, clearly signaled in a 'Detailed References' section that describes each file's scope. This matches the anchor-5 example structure.

Specificity

The description lists multiple concrete actions — 'defining features, configuring feature_store.yaml, retrieving features online/offline, using the CLI, and building RAG retrieval pipelines' — giving comprehensive coverage of the skill's capabilities. It is not score 4 because coverage has no notable gaps; the when-clause additionally enumerates every object type (entities, feature views, on-demand/stream feature views, feature services, data sources).

Completeness

Both questions are explicitly answered: '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' (what) and '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' (when, with concrete trigger phrases). This matches the anchor-5 example structure exactly.

Trigger Term Quality

Good keyword coverage with natural phrases users would say: 'feature views', 'feature_store.yaml configuration', 'feast apply/materialize commands', 'online or historical feature retrieval', 'vector-based document retrieval'. It falls short of 5 because common synonyms like 'feature engineering', 'training data', or 'point-in-time joins' are absent, though core Feast vocabulary is well represented.

Distinctiveness Conflict Risk

It occupies a clear niche (Feast feature store) with distinct triggers — specific CLI commands, config file names, and Feast-only object types — making conflict with other skills minimal. Only mild adjacency exists with generic RAG skills via 'building RAG retrieval pipelines', not enough to reduce below 5.