ruview-advanced-sensing

Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.

Quality

86%

Does it follow best practices?

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Securityby

Critical

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Quality

Content

87%

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

A lean, actionable reference skill body with strong organization and concrete commands; the only gap is the absence of an explicit validation feedback loop for the test workflow.

Suggestions

Turn the 'Validate advanced changes' block into an explicit feedback loop: run tests, and if any fail, list how to interpret and re-run the failing crate before proceeding.

Briefly state what to check for in cargo output so a failed run has a clear recovery path.

Dimension	Reasoning	Score
Conciseness	Dense module tables and brief prose assume Claude's competence with no padding or explanation of basics, matching the lean score-3 anchor; every section earns its tokens.	3 / 3
Actionability	Provides concrete executable commands ("cargo test --workspace --no-default-features", "node scripts/mesh-graph-transformer.js") and specific module/file pointers that are copy-paste ready, matching the score-3 anchor.	3 / 3
Workflow Clarity	The 'Validate advanced changes' section lists a command sequence but lacks an explicit validate->fix->retry feedback loop, so checkpoints are implicit; not score 3 (no recovery loop) but above score 1 (a sequence does exist).	2 / 3
Progressive Disclosure	No bundle files exist, but the body is well-organized into clearly signaled sections/tables with one-level-deep external references (ADRs, docs paths), matching the score-3 anchor for clean overview navigation.	3 / 3
	Total	11 / 12 Passed

Description

85%

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

A specific, well-scoped description with an explicit use-trigger and a clear niche; its main weakness is heavy technical jargon in place of natural trigger terms.

Suggestions

Add a few natural-language trigger variants (e.g., 'WiFi sensing', 'through-wall imaging', 'pose sensing') alongside the technical terms so users are more likely to match it in plain language.

Keep the explicit 'Use for ...' clause but consider widening the trigger to everyday phrasings like 'Use when deploying multi-node WiFi sensing or tomography.'

Dimension	Reasoning	Score
Specificity	Lists multiple concrete capabilities — "attention-weighted fusion, geometric diversity", "RF tomography (ISTA L1 solver, voxel grids)", "adversarial signal detection", "multistatic mesh security hardening" — matching the score-3 anchor for several specific concrete actions; not vague like score 2.	3 / 3
Completeness	Answers both what (the enumerated capabilities) and when via the explicit trigger clause "Use for research-grade or multi-node deployments", satisfying the score-3 anchor; a score-2 example lacks the explicit "Use when/for" guidance which is present here.	3 / 3
Trigger Term Quality	Terms are domain-relevant (multistatic sensing, RF tomography, cross-viewpoint fusion) but lean technical jargon rather than natural user phrasings, and common variations are missing; not score 1 because real keywords exist, not score 3 because coverage of natural terms is thin.	2 / 3
Distinctiveness Conflict Risk	Occupies a clear niche (advanced RuView multistatic sensing/tomography/security) with distinct triggers unlikely to fire for unrelated skills; well above the score-2 overlap anchor.	3 / 3
	Total	11 / 12 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
referenced_paths_exist	Referenced path issues: 3 missing	Warning

	Total	15 / 16 Passed

Repository: ruvnet/RuView
Path: plugins/ruview/skills/ruview-advanced-sensing/SKILL.md
Commit: d9dfea2

Reviewed: about 14 hours ago

Table of Contents

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