Conciseness

The body is lean and assumes competence: terse fact bullets ('Real network support: yes.', 'Runtime profiles: javascript-quickjs') and guardrails that are all non-obvious, domain-specific guidance. Every token earns its place with no padding or explanation of known concepts.

Actionability

The Core Pattern uses genuine Rust syntax with concrete API names ('engine.optimize(request, evaluator)?', 'result["baselineScore"]'), and guardrails point to real package examples. It is not anchor 5 because reflection_client, request, and evaluator are undefined placeholders, so the snippet is not copy-paste ready.

Workflow Clarity

Clear conditionals guide decisions ('Use no-key examples for deterministic local checks', 'Use generated optimizer APIs when the target package does not expose a standalone refine helper') and no destructive or batch operations are involved. It falls short of anchor 5 because there is no explicit ordering of steps, such as when to consult API.md versus examples/ versus the capabilities manifest.

Progressive Disclosure

At roughly 42 lines with well-organized sections and no bundle files provided, the skill appropriately stays an overview and points one level deep to package-side materials (API.md, axir-api.json, examples/). This matches the simple-skill exception for progressive disclosure.

Specificity

The description lists several concrete capabilities ('reward-scored generation, iterative candidate improvement, evaluator feedback') but 'optimizer-backed refinement patterns' is a category rather than an action and coverage has minor gaps (e.g., porting, caching, playbooks). It goes beyond the 1-2 actions of anchor 3 but is not comprehensive enough for anchor 5.

Completeness

Both 'what' and 'when' are explicitly present via the 'Use when writing Rust code with axllm...' clause and the concrete capability list. It does not reach anchor 5 because the when-clause restates the domain rather than adding distinct concrete trigger phrases, making it slightly circular.

Trigger Term Quality

Terms like 'Rust', 'axllm', 'evaluator feedback', 'optimizer', and 'refinement' are the natural vocabulary for this domain, giving good keyword coverage. It falls short of anchor 5 because common variations and synonyms (e.g., 'GEPA', 'playbook', 'few-shot', 'score') are missing and some phrases are compound jargon.

Distinctiveness Conflict Risk

'Rust code with axllm for reward-scored generation' carves out a clear niche with distinct triggers; overlap risk with generic Rust or LLM-refinement skills is minimal.