Conciseness

The body avoids padding with concepts Claude already knows — nearly every line is package-specific fact — but it is overstuffed for a main SKILL.md: per-provider sampling minutiae, Vertex caching rules, and session-adapter behavior are compressed into run-on sentences (e.g., the ~180-word sampling paragraph) that could be tightened or moved out. It fits 'mostly efficient but could be tightened' more than anchor 2's padded/generic verbosity, but the density routinely sacrifices clarity for brevity.

Actionability

Guidance is frequently concrete (exact factory names, error strings like 'AxAIServiceTimeoutError (Request timed out after <N>ms)', defaults like '500, 408, 429, 502, 503, 504 and 529'), but the sole code snippet — `let llm = ai("openai", options)?;` — is incomplete (options never defined) and the referenced runnable examples under `examples/` and `src/examples/rust/generation/` do not exist in the bundle. This lands on anchor 3: concrete but incomplete, pseudocode-level rather than executable.

Workflow Clarity

There is an implicit reading order (When To Use → Package Facts → Core Pattern → per-topic rules → Guardrails) but no sequenced workflow and no validation checkpoints for risky operations — nothing tells the agent how to verify a call shape before committing to it beyond 'start from package examples', and those examples are not shipped. Anchor 3 ('sequence present but checkpoints missing or implicit') fits; not 4 because no explicit verify/fix loop exists anywhere.

Progressive Disclosure

The body is organized into clear labeled sections (matching anchor 3's 'some structure' example of inline reference content plus section headers), but everything — provider rules, sampling tables-in-prose, session semantics — is inlined in one 160-line file, and the files it points to (`API.md`, `axir-api.json`, `axir-capabilities.json`, `examples/`) are absent from the bundle, so the references dangle. Not 4 because content that clearly belongs in separate files is inlined and the promised navigation targets don't exist.

Specificity

The description enumerates many concrete capabilities — 'named deployment profiles, generic provider clients, model selection, OpenAI-compatible calls, Responses, Gemini, Anthropic, routers, and balancers' — with only minor gaps. It is not a 5 because these are feature nouns rather than explicit actions (no verbs like 'create', 'route', 'normalize'), and 'Responses' is ambiguous jargon without the OpenAI qualifier.

Completeness

It has an explicit 'Use when writing Rust code with `axllm` for...' trigger clause with concrete contexts, and the 'what' is conveyed through the enumerated capability list. Not a 5 because the 'what' is implicit — the description never states what the skill does (e.g., 'provides API guidance and examples for...') — it is entirely when-shaped; but the when clause is explicit and specific, keeping it above 3.

Trigger Term Quality

Natural trigger terms users would say are well covered: 'Rust', 'axllm', 'OpenAI', 'Gemini', 'Anthropic', plus task terms like 'provider clients', 'model selection', 'routers', 'balancers'. Not a 5 because common variations are missing (e.g., 'failover', 'LLM', 'crate', 'ax package') and 'Responses' alone is not a phrase a user would naturally say.

Distinctiveness Conflict Risk

The pairing of 'Rust code with `axllm`' creates a clear niche with minimal conflict risk against generic skills. Not a 5 because broad terms like 'model selection', 'routers', and 'balancers' plus multi-provider names (Gemini, Anthropic, OpenAI) could overlap with general LLM-client or provider skills in other languages.