Conciseness

The ~41-line body is dense and lean: package facts, a two-line core pattern, curated symbol lists, and directive guardrails, with no padding and no explanation of concepts Claude already knows. Every line carries actionable information, matching the 'lean and efficient' anchor.

Actionability

The Core Pattern shows real API call syntax ('axllm::AxGEPA engine(reflection_client, options); auto result = engine.optimize(request, evaluator);') plus exact symbol names and explicit pointers to package examples, but the snippet uses unbound identifiers with no includes, so it is not copy-paste ready and common cases are deferred to `examples/`. Above anchor 3 (real syntax, not pseudocode, with specific direction); below anchor 5 (not fully executable inline).

Workflow Clarity

The core action (construct an optimizer engine and call optimize with an evaluator) is unambiguous and validation is implicitly addressed ('Use `no-key` examples for deterministic local checks'), but the three usage scenarios in 'When To Use' are not sequenced and no explicit validate-then-proceed checkpoint is given. Clear enough to sit above anchor 3; the unsequenced multi-scenario usage keeps it below 5.

Progressive Disclosure

A lean under-50-line overview with well-organized sections; bulk material (API.md, axir-api.json, axir-capabilities.json, examples/) stays external and is clearly signaled one level deep under Package Facts, and nothing inlined belongs in a separate file. No bundle files exist to verify, and none are needed at this size.

Specificity

Lists several concrete capability areas — 'reward-scored generation, iterative candidate improvement, evaluator feedback, and optimizer-backed refinement' — but they are noun phrases rather than verb-led actions and overlap somewhat ('iterative candidate improvement' vs 'optimizer-backed refinement patterns'). This sits above anchor 3 (only 1-2 actions, not comprehensive) and below anchor 5 (comprehensive action-led coverage).

Completeness

The 'when' is explicit and specific ('Use when writing C++ code with `axllm`') and the capability enumeration conveys the 'what', but the 'what' is embedded in the trigger clause as noun-phrase capabilities rather than stated as actions the skill performs. Both are present, so above anchor 3; not anchor 5 because what and when are not independently and fully explicit.

Trigger Term Quality

Includes the primary natural terms a user of this package would say — 'C++', 'axllm', 'evaluator feedback', 'optimizer', 'refinement' — but misses common variations and synonyms such as 'optimize', 'GEPA', or 'improve outputs'. Good coverage, just short of anchor 5's comprehensive synonym set.

Distinctiveness Conflict Risk

Names a specific package (`axllm`), a specific language (C++), and a niche capability set (reward-scored generation, evaluator feedback, optimizer-backed refinement), giving a clear niche with distinct triggers and minimal conflict risk against generic C++ or LLM skills.