Conciseness

The body is lean and command-dominated with essentially no explanation of concepts Claude already knows, and every metric cited ('~84 s on an M4 Pro', '92.9% PCK@20') is decision-relevant. Not 5: time-sensitive details are inlined rather than isolated — 'RuVector v2.0.4', 'cognitum-20260110', '~$0.80/hr, A100 40GB ~$3.60/hr' — which will silently rot as versions and prices change.

Actionability

Tracks A, B, C, and E give copy-paste-ready commands with exact flags ('cargo run -p wifi-densepose-sensing-server -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf'), and publishing/validation paths are concrete. Not 5: Track D offers no command at all ('Configured through the training pipeline's domain-generalization options; see ADR-027'), and '--epochs <N>' in Track B is an unfilled placeholder.

Workflow Clarity

Tracks are clearly sequenced with an ordered 3-step pipeline in Track B (collect → align → train → evaluate) and a dedicated 'Validation after a training change' section stating expected outcomes ('1,400+ pass, 0 fail', 'VERDICT: PASS'), plus a destructive-op guardrail ('VM is auto-deleted after training unless --keep-vm'). Not 5: there is no fix-and-retry feedback loop around validation, and what to do when PCK@20 or verify.py underperforms is left implicit.

Progressive Disclosure

Sections are well organized by track, and external references (ADR-079, docs/tutorials/cognitum-seed-pretraining.md, docs/huggingface/) are one level deep and clearly signaled. Not 5: no bundle files exist, yet the body inlines reference-style material — the ADR index (015/016/017/024/027/076/079/084/085/095/096) and the full data-layout table — that belongs in a references file the reader would consult only when needed.

Specificity

The description enumerates concrete, distinct capabilities across all training tracks — 'camera-free WiFlow pose (10 sensor signals, no labels)', 'camera-supervised pose (MediaPipe + ESP32 CSI)', 'RuVector contrastive embeddings', 'domain generalization', 'local SNN environment adaptation', 'GPU training on GCloud and Hugging Face publishing' — in third person, comprehensively covering the skill's scope. It exceeds the 4 anchor because the actions are both numerous and fully specific rather than having minor coverage gaps.

Completeness

Both halves are explicit: the 'what' is a detailed capability list, and the 'when' is stated verbatim — 'Use when building, fine-tuning, evaluating, or shipping a model'. This matches the anchor-5 pattern of a concrete what paired with explicit trigger phrases; the 4 anchor's 'when could be more explicit' caveat does not apply.

Trigger Term Quality

Good natural keyword coverage: 'train', 'building', 'fine-tuning', 'evaluating', 'shipping a model', 'GPU training', 'Hugging Face' are phrases a user on this project would actually say. Not 5: heavy internal jargon (AETHER, MERIDIAN, ADR-079/024/027) dominates several clauses, and no synonyms or file extensions (e.g., .rvf, model files) are included.

Distinctiveness Conflict Risk

The skill occupies a clear niche — 'Train RuView models' with track-specific triggers (WiFlow pose, MediaPipe/CSI, RuVector embeddings, MERIDIAN generalization) — making it unlikely to fire for unrelated skills. Only the generic tail 'shipping a model' has mild overlap risk, which is insufficient to drop to the 4 anchor.