Conciseness

The body avoids generic padding and is dense with library-specific rules Claude would not know, but key guidance is repeated across sections: 'a custom metric overrides the built-in judge path' appears in three sections and the 'prefer a plain typed AxGen evaluator' rule is restated four times. This matches 'mostly efficient but includes some unnecessary explanation or could be tightened'; not 2 because the redundancy is duplicated rules, not explanation of known concepts.

Actionability

Five complete, executable TypeScript patterns (canonical with imports and save/load, minimal, deterministic metric, built-in judge, plain AxGen judge) that are copy-paste ready and cover the common cases, including task record shape and expectedActions. The only blemish is the 'judge-model' string placeholder in one snippet, which is too minor to drop it from the fully-executable anchor.

Workflow Clarity

A clear decision flow (goal -> target -> task design -> metric-vs-judge -> optimize -> save/load -> replay) with an explicit prerequisite ("Use agent.optimize(...) only after the agent is already configured and runnable") and a diagnostic feedback loop ("If one training task keeps collapsing to zero, inspect that task first instead of adding more optimizer rounds"). Not 5 because the sequence is distributed across thematic sections rather than one ordered workflow with explicit validation checkpoints; no destructive/batch operations apply a cap.

Progressive Disclosure

No bundle files exist; all ~365 lines, including self-contained topics like Eval Semantics (MCP replay details) and Delegation Optimization Notes, are inlined in one file with clear headers. Structure and navigation are good and the few external pointers (example links, sibling skills) are clearly signaled, but content that should be separate reference files is inline, matching the score-3 anchor rather than 4.

Specificity

Names the domain ("AxAgent tuning and evaluation code using @ax-llm/ax") and exactly two concrete actions ("generate correct AxAgent tuning and evaluation code"), matching the anchor for 1-2 concrete actions without comprehensive coverage. It stays at 3 because artifact save/load, metric setup, and judge configuration are absent, so 'several specific actions' (score 4) is not met.

Completeness

Explicitly answers both 'what' ("helps an LLM generate correct AxAgent tuning and evaluation code using @ax-llm/ax") and 'when' with a concrete trigger clause ("Use when the user asks about agent.optimize(...), judgeOptions, eval datasets..."). This is a direct match for the score-5 anchor; the 'when' is explicit with concrete trigger phrases, not weaker as the score-4 anchor requires.

Trigger Term Quality

Good keyword coverage with natural library-specific terms users would say: "agent.optimize(...)", "judgeOptions", "eval datasets", "optimization targets", "saved optimizedProgram artifacts". Not 5 because common synonyms like 'GEPA', 'tuning', or 'playbook' are missing, but coverage clearly exceeds the 'some relevant keywords but missing common variations' anchor.

Distinctiveness Conflict Risk

Clear niche with distinct library-specific triggers (agent.optimize(...), judgeOptions, optimizedProgram) and minimal conflict risk; the body even disambiguates against the sibling ax-gepa and ax-playbook skills. It would not trigger for generic agent or optimization requests.