Conciseness

The body is efficient: the Flags section documents defaults without explaining known concepts, and no background padding is present. Minor redundancy — the full three-line bazel target is repeated verbatim six times and the bracketed flags in the usage block duplicate the Flags section — keeps it at anchor 4 rather than anchor 5.

Actionability

Fully executable, copy-paste-ready bazel commands are given with a concrete model ID (google/gemma-3-270m-it), and five examples cover the common cases (single-turn, multi-turn, prompt file, externalized embedder, NPU backend). This matches anchor 5.

Workflow Clarity

The single action (invoke the equivalence_test target with documented flags) is unambiguous, but the body never states what the test outputs or what equivalence success/failure looks like — a minor validation/interpretation gap matching anchor 4. It is not anchor 5 because no explicit checkpoint or result-interpretation guidance exists, and not anchor 3 because the invocation itself is fully specified.

Progressive Disclosure

No bundle files exist or are referenced, and none are needed: the body is a well-organized single file with clear sections (Usage, Running the Test, Flags, Examples) and easy navigation. Per the simple-skill guidance, well-organized single-file structure with no need for external references matches the top anchor.

Specificity

"Validates equivalence between LiteRT models (litert_lm) and PyTorch models (transformers)" and "Supports multi-turn conversations and custom prompts" list several concrete, named capabilities. Minor gaps in coverage keep it at anchor 4 rather than the comprehensive action list of anchor 5.

Completeness

Both parts are present: a clear what ("Validates equivalence between LiteRT models and PyTorch models") and an explicit when ("Use when you need to verify that an exported LiteRT model produces the same outputs as the original Hugging Face model"). The when-clause covers only a single trigger scenario rather than the multiple concrete trigger phrases of anchor 5, so it sits at anchor 4.

Trigger Term Quality

Terms like "LiteRT", "PyTorch", "Hugging Face", "exported", and "equivalence" are natural phrases for this domain and would match real user requests. A few natural synonyms (e.g., "compare outputs", "model parity", "export validation") are missing, matching anchor 4 rather than the full synonym coverage of anchor 5.

Distinctiveness Conflict Risk

"Validates equivalence between LiteRT models (litert_lm) and PyTorch models (transformers)" carves out a clear niche with distinct, technology-specific triggers; it is unlikely to fire for any unrelated skill. This matches anchor 5 (clear niche, minimal conflict risk).