Conciseness

Mostly efficient — the API facts (return shapes, lazy hydration, verify gate semantics) are non-obvious and earn their tokens. However, the isExpensive/teacherOptions gating is explained in at least four places ('Use These Defaults', the Agents teacher bullets, and Troubleshooting), and the verify-gate semantics are stated twice (Agents and the generated-packages paragraph). This repetition goes beyond 'minor instances that could be trimmed', matching 'Mostly efficient but includes some unnecessary explanation or could be tightened'.

Actionability

Fully executable copy-paste TypeScript examples cover the common cases: offline evolve with metric, online update with example/prediction/feedback, persist/restore via toJSON/load, and the agent playbook handle. The Troubleshooting section pairs concrete error messages with concrete fixes ('wrap them in example: { ... }'). This matches the top anchor.

Workflow Clarity

Multi-step workflows are clearly sequenced (create → evolve/update → applyTo → toJSON/load), the agent evolve verify gate provides an explicit validation checkpoint with rollback semantics, and the Troubleshooting section supplies error-recovery feedback loops for each listed failure mode. This matches 'Clear sequence with explicit validation steps; feedback loops for error recovery'.

Progressive Disclosure

No bundle files exist, so all content is inline in a single well-sectioned SKILL.md (~130 lines) with a clearly signaled cross-skill 'See Also' section. Structure is good and navigation is easy, but peripheral bulk — the five-language generated-packages table and the deep agent-evolve/failureSignals detail — is inlined where a reference file would keep the overview lean. This matches 'Good structure; most content is appropriately placed; minor organization gaps' rather than the well-split anchor 5.

Specificity

The description names the domain ('generate correct playbook code using @ax-llm/ax') and several concrete actions: 'growing/applying task knowledge offline and online with evolve() and update()'. It lists several specific operations, though coverage has minor gaps (no mention of persist/restore or applyTo), matching the anchor 'Lists several specific actions; minor gaps in coverage' rather than the comprehensive anchor 5.

Completeness

Both questions are explicitly answered: what — 'helps an LLM generate correct playbook code using @ax-llm/ax'; when — 'Use when the user asks about playbook(), AxPlaybook, ... evolve() and update()' with concrete trigger phrases. This matches the anchor 'Clearly and explicitly answers both what AND when with concrete trigger phrases'.

Trigger Term Quality

Trigger terms are strong and natural for this niche: 'playbook(), AxPlaybook, context playbooks, evolving context, ACE / Agentic Context Engineering, agent.playbook()'. A user of this library would naturally say these. It sits at 'Good keyword coverage; a few natural terms missing' (e.g., persist/snapshot/load phrasings) rather than the fully comprehensive anchor 5.

Distinctiveness Conflict Risk

The description carves out a clear niche (the @ax-llm/ax playbook/ACE feature) with distinct trigger terms, and it does not claim optimize()/GEPA territory that sibling skills cover. Minimal conflict risk with other skills.