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ruview-model-training

Train RuView models — camera-free WiFlow pose (10 sensor signals, no labels), camera-supervised pose (MediaPipe + ESP32 CSI → 92.9% PCK@20, ADR-079), RuVector contrastive embeddings (AETHER, ADR-024), domain generalization (MERIDIAN, ADR-027), local SNN environment adaptation, plus GPU training on GCloud and Hugging Face publishing. Use when building, fine-tuning, evaluating, or shipping a model.

72

Quality

89%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is lean, well-structured by track, and action-packed with executable commands, plus a validation checkpoint after training changes. Minor placeholders and a few tracks without explicit validation loops keep actionability and workflow clarity just below the top anchor.

Suggestions

Replace the '<N>' epochs placeholder in Track B with a concrete default or guidance on choosing the value.

Add an explicit validation/eval checkpoint (e.g., run eval-wiflow.js and confirm PCK@20) at the end of each training track, not just the global validation section.

Surface the expected output or success signal for commands like --build-index env and publish-huggingface so Claude knows when a step succeeded.

DimensionReasoningScore

Conciseness

Lean track-organized body with no concept explanations Claude already knows; every section earns its place and assumes competence.

5 / 5

Actionability

Provides concrete, executable cargo/python/node/gcloud commands per track that are largely copy-paste ready, with only minor gaps such as the '<N>' epochs placeholder in Track B.

4 / 5

Workflow Clarity

Multi-track processes are sequenced with numbered steps (Track B) and a dedicated validation section ('cargo test --workspace', 'verify.py' VERDICT: PASS, handoff to ruview-verify); checkpoint framing is strong though a couple of tracks lack explicit validation feedback loops.

4 / 5

Progressive Disclosure

A clear overview split into well-signaled tracks with one-level-deep references to ADR docs and scripts; no bundle files are present, and the body is appropriately organized for navigation.

5 / 5

Total

18

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, complete, and distinctive, enumerating concrete training tracks with metrics and an explicit 'Use when' trigger clause. Trigger-term coverage is strong but could add a few more natural synonyms.

DimensionReasoningScore

Specificity

Names five concrete training tracks with specific details (WiFlow 10-sensor unsupervised pose, MediaPipe+ESP32 camera-supervised pose, RuVector contrastive embeddings, MERIDIAN domain generalization, SNN adaptation) plus GPU training on GCloud and HF publishing — comprehensive coverage.

5 / 5

Completeness

Explicitly answers what (enumerates each model track with concrete metrics) and when ('Use when building, fine-tuning, evaluating, or shipping a model') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes natural phrases users would say ('building, fine-tuning, evaluating, or shipping a model') but lacks common synonyms or model-type variations; strong but a few natural terms missing.

4 / 5

Distinctiveness Conflict Risk

A clear niche (RuView model training across named tracks) with distinct triggers and named ADRs/crates; minimal conflict risk with other skills.

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

referenced_paths_exist

Referenced path issues: 18 missing

Warning

Total

15

/

16

Passed

Repository
ruvnet/RuView
Reviewed

Table of Contents

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