Conciseness

The body is dense with internal-API specifics Claude cannot know (exact call signatures like "requires 4 positional arguments (note the 4th ones tensor)", flag couplings, failure modes) and explains no generally known concepts. Every section earns its tokens through non-obvious detail delivered in tight bullets, tables, and commands, matching the "lean and efficient... every token earns its place" anchor.

Actionability

Commands are copy-paste ready with real paths and flags (bazel run inspector, litert convert with --extension-modules, --target=npu:qualcomm), the cheat-sheet gives exact import paths and call signatures, and code snippets like the _find_modeling_module loop and attn_output reshape are executable. This matches the fully-executable, common-cases-covered anchor.

Workflow Clarity

The 4-step workflow is clearly sequenced with strong explicit validation in Step 3 (three named testing.assert_* checks plus dry_run, with timing budgets like "<2s on CPU") and error-recovery detail in the gotchas. However, Step 4 is titled "Full Export & litert_lm Verification" yet its bullets only show export invocation commands and never state how to verify the exported artifact — a minor validation gap that fits anchor 4 rather than the explicit-checkpoint-everywhere anchor 5.

Progressive Disclosure

The body has good internal structure (numbered sections, headers, a signature table), but it is a single ~230-line monolith with no bundle files at all: the composite-ops cheat-sheet and the six architecture gotchas are exactly the kind of deep reference content that belongs in separate one-level-deep files. This matches anchor 3 ("content that should be separate is inline"), not anchor 4's "bulk in separate file".

Specificity

The description lists multiple concrete actions — "Onboards and exports new or custom Hugging Face... model architectures", "inspecting a checkpoint with model_ext/inspector.py", "patching RMSNorm/SwiGLU/ShortConv/Attention/RoPE", "configuring fused weights or LlmMetadata", "verifying extensions with model_ext/testing.py" — with exact module and flag names. Coverage is comprehensive within the domain, matching the anchor for multiple specific concrete actions; anchor 4's "minor gaps in coverage" does not apply.

Completeness

It explicitly answers "what" ("Onboards and exports... using pluggable ModelExtension classes") and "when" with a concrete "Use when..." clause enumerating trigger scenarios (supporting a new model_type, inspecting a checkpoint, patching modules, configuring fused weights, verifying extensions), plus an explicit negative scope ("Don't use for generic non-generative PyTorch-to-TFLite conversion"). This clearly matches the anchor for explicit what AND when with concrete trigger phrases.

Trigger Term Quality

Good natural-term coverage including synonyms ("Hugging Face (or Diffusers/PyTorch)", "export_hf or litert_lm", "checkpoint", "model_type"), but no file extensions (e.g. .safetensors) appear as positive triggers and "convert"/"TFLite" surface only in the exclusion clause. This sits between anchor 4 (a few natural terms missing) and anchor 5 (comprehensive including file extensions), and the missing extensions keep it at 4.

Distinctiveness Conflict Risk

The skill occupies a clear niche (litert_torch.generative.export_hf ModelExtension framework) with highly specific triggers, and the "Don't use for..." clause explicitly fences off adjacent domains like generic PyTorch-to-TFLite conversion. Conflict risk with other skills is minimal, matching anchor 5.