Conciseness

The body is dense and assumes Claude's competence throughout — no space is spent explaining pip, GPUs, or general ML concepts, and every section carries Laya-specific knowledge (token budgets, head_max_len errors, checkpoint differences, calibration recipe). However, time-sensitive claims are woven into the main text rather than isolated: "As of laya 0.3.4 the detector only recognises...", "(English checkpoint, no tuning, September 2026)", and the long benchmark narrative could be tightened. This is 'efficient; minor instances of over-explanation that could be trimmed' rather than the lean-every-token-earns-its-place anchor at 5.

Actionability

All guidance is copy-paste executable: the pip install command, a complete predict() call with the full question schema, threshold code for routing, a working FastAPI sidecar, the exact temperature-refit procedure, and a numbered evaluation protocol. This matches 'fully executable; copy-paste ready code or commands; specific examples cover the common cases' — the common cases (English load, choice/score/noul questions, routing thresholds, sidecar serving) are all concretely covered.

Workflow Clarity

The multi-step integration flow is clearly sequenced (install → load once → write questions → pick checkpoint → set thresholds → wire in → evaluate) with explicit validation checkpoints: the 'Evaluate before shipping' section is a measure-rephrase-fine-tune feedback loop ('Try alternative phrasings and checkpoints on the same set, and keep the best. If accuracy is still short, fine-tune'), and a closing checklist consolidates the checkpoints. No destructive or batch operations apply, so no cap is triggered.

Progressive Disclosure

There are no bundle files; the skill is a single ~220-line markdown file. Sections are well-organized and navigable (Install, question types, writing questions, checkpoints, probabilities, wiring, evaluation, checklist), which fits 'good structure; most content is appropriately placed'. It falls short of 5 because detail that would naturally live one level deep — the calibration refit procedure, the independent benchmark numbers, the fine-tuning pointer — is inlined in the single file rather than split into reference files, which the length arguably warrants. It is above 3 because nothing is buried and the structure is clear.

Specificity

The description lists multiple concrete actions — "classifies, routes, scores and answers yes/no questions about a piece of text, returning calibrated probabilities" — plus concrete performance characteristics ("roughly 20-35 ms on a laptop GPU", "no LLM call and no data leaving the machine"). This matches the anchor for comprehensive coverage of multiple specific concrete actions; score 4 would require minor gaps in coverage, which are not present.

Completeness

It explicitly answers both what ("an open-source non-generative decision model... classifies, routes, scores and answers yes/no questions... returning calibrated probabilities") and when ("Use this skill whenever the user wants to classify or route text... Also use it when they mention Laya, Jev..."). This matches the anchor for clearly and explicitly answering both what AND when with concrete trigger phrases; score 4 would require the 'when' to be less explicit, which it is not.

Trigger Term Quality

Natural user phrases are covered comprehensively with synonyms: "classify or route text, triage tickets or emails, detect spam, phishing, toxicity or intent, put a guardrail in front of an agent or LLM", "replace an LLM-based classifier", plus brand and concept names ("Laya, Jev, a 'System 1 model', 'typed decisions'") that catch users who never name Laya. This is comprehensive natural-term coverage including synonyms; score 4 would mean a few natural terms are missing, and none are apparent.

Distinctiveness Conflict Risk

The niche is largely distinct — a local non-generative decision model with unique brand triggers (Laya, Jev, typed decisions) — but a few triggers overlap other skill domains: "score something against a rubric" overlaps rubric/judge-scoring skills, and "detect spam, phishing, toxicity" overlaps moderation skills. This fits 'mostly distinct; minor overlap risk with closely related skills' rather than the minimal-conflict anchor at 5.