Conciseness

Every sentence carries package-specific rules with no padding about concepts Claude already knows (nothing like "PDF files store text and images..."), so it is above 2. But roughly 160 lines of dense run-on prose — e.g., the sampling paragraph "temperature 0, or temperature 0.7 and top-p 1 for `openai-responses`... GPT-5.1-5.4 while reasoning is off, their default; GPT-5.5 and 5.6 with effort `none`" — could be dramatically tightened or moved to reference tables, matching the 3 anchor "mostly efficient but could be tightened" rather than the lean 4-5 anchors.

Actionability

Concrete, specific guidance dominates: one executable snippet (`llm = ai('openai', api_key=os.environ['OPENAI_API_KEY'])`), exact defaults ("`retryableStatusCodes`, by default 500, 408, 429, 502, 503, 504 and 529"), exact error messages ("Request timed out after <N>ms"), and exact signatures (`add_child_agent(namespace, name, child)`). It is not a 5 because the bulk of guidance is prose rules with only a single code example, and the "start with examples under `examples/`" instructions point to paths that are not part of the skill bundle.

Workflow Clarity

The body is organized by topic (profiles, caching, timeouts, retries, routing, sessions), never by sequenced steps, and contains no validation checkpoints — only starting-point guidance like "Start from package examples for exact native syntax before inventing a new call shape". This matches the 3 anchor (sequence/checkpoints implicit), above 2 because sections are coherent and ordered, below 4 because no explicit step sequence or verification loop exists anywhere.

Progressive Disclosure

Thirteen well-labeled sections provide real structure, and external materials are named ("`API.md` and `axir-api.json`", "`axir-capabilities.json`", "`examples/`", a URL), matching the 3 anchor's "some structure... content that should be separate is inline". It is not a 4-5 because essentially all detail (per-provider sampling rules, retry/backoff internals, session semantics, the full API surface list) is inlined in SKILL.md, and the referenced files are not present in the bundle, so the claimed split between overview and reference does not actually exist.

Specificity

Enumerates concrete capabilities — "named deployment profiles, generic provider clients, model selection, OpenAI-compatible calls, Responses, Gemini, Anthropic, routers, and balancers" — giving several specific items with minor coverage gaps. It falls short of the 5 anchor because these are domain nouns rather than the concrete actions ("Extract text and tables from PDF files, fill forms") the top anchor requires, and above 3 because coverage goes well beyond 1-2 actions.

Completeness

The explicit clause "Use when writing Python code with `axllm` for..." clearly answers 'when', and the enumerated purposes convey the 'what' domain. It matches the 4 anchor ("both present; 'when' could be more explicit or specific") rather than 5 because the description never states what the skill itself provides (API docs, examples, manifests), leaving the 'what' implied rather than explicit.

Trigger Term Quality

Natural keywords a user of this package would say are present — "Python", "OpenAI-compatible", "Responses", "Gemini", "Anthropic", "routers", "balancers", plus the package name "axllm". It is not a 5 because common variations and synonyms ("chat completions", "failover", "LLM client", "model catalog") are missing, and not a 3 because provider-name coverage is broad rather than partial.

Distinctiveness Conflict Risk

The package name "axllm" pins the skill to one specific generated package, making it mostly distinct as in the 4 anchor. It is not a 5 because broad provider terms like "Gemini, Anthropic, routers, and balancers" carry minor overlap risk with generic provider-API skills, and not a 3 because the `axllm` gating keeps it well above "could still overlap with similar skills".