Conciseness

The body is ~45 lines with no filler: every line states a fact, a guardrail, or an API name ('Package: `axllm`', 'Runtime profiles: `javascript-quickjs`, `python-pyodide`'). It explains nothing Claude already knows and assumes competence throughout, matching the lean anchor 5 ('every token earns its place').

Actionability

The Core Pattern gives real C++ syntax ('axllm::AxGEPA engine(reflection_client, options); auto result = engine.optimize(request, evaluator);') and the API surface lists concrete symbols, plus 'Start from package examples for exact native syntax' directs to working code. It stops short of anchor 5 because the snippet uses undefined placeholders (reflection_client, options, request, evaluator) with no includes or setup, so it is not copy-paste ready and the common cases aren't covered inline.

Workflow Clarity

As a single-purpose reference skill, the guidance is unambiguous about what to do first ('Start from package examples...') and includes sequencing and decision rules ('Use BootstrapFewShot before GEPA when demonstrations should seed optimization'; choose `no-key` vs `provider-api` examples by credential availability). It misses anchor 5 because there is no explicit checkpoint or validation step in the optimize/evaluate loop, and the actual workflow (wiring an evaluator, running an optimization) is left to the examples.

Progressive Disclosure

Sections are well organized (When To Use, Package Facts, Core Pattern, API Surface, Guardrails) and external materials are clearly enumerated in one place ('Package API docs: `API.md` and `axir-api.json`', 'Runnable examples: `examples/`'). It falls below anchor 5 because those referenced paths (API.md, axir-api.json, axir-capabilities.json, examples/) do not exist in the skill bundle itself, and the API symbol list is inlined rather than deferred — navigation depends on files the agent must find elsewhere in the package.

Specificity

The description names the domain ("writing C++ code with `axllm`") and lists several concrete capability areas ("GEPA, Pareto tradeoffs, reflection clients, metric budgets, optimizer state, and artifacts"), giving clear scope with only minor gaps. It sits above anchor 3 (which expects just 1-2 concrete actions with thin coverage) but below anchor 5 because the items are topic nouns rather than enumerated actions (no verbs like 'run the optimizer' or 'track budgets').

Completeness

The 'when' is explicit ("Use when writing C++ code with `axllm` for GEPA, Pareto tradeoffs...") but the 'what' is only implied — the description never states what the skill provides or does (e.g., 'Guides usage of the generated axllm package...'). Scoring only what is explicitly stated, this matches anchor 3 (one of what/when missing or implicit); it is not anchor 4, which requires both an explicit 'what' and an explicit 'when'.

Trigger Term Quality

Phrases like "C++ code with `axllm`", "GEPA", "Pareto tradeoffs", "reflection clients", and "metric budgets" are exactly the natural vocabulary a user of this package would say. It falls short of anchor 5 because common variations and synonyms (e.g., 'prompt optimization', 'LLM optimizer', 'candidate evaluation') are missing, but coverage is good rather than merely adequate.

Distinctiveness Conflict Risk

The pairing of "C++" with "`axllm`", "GEPA", and "reflection clients" carves out a clear niche with essentially no overlap risk against other skills — only someone working with this exact generated package would match these triggers.