train-pose

Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.

Quality

91%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

Quality

Content

100%

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

A tight, honest skill body that assumes Claude's competence, gives actionable reporting guidance, and sequences the publish-a-number workflow with a real validation checkpoint.

Dimension	Reasoning	Score
Conciseness	Lean and efficient with no padding — it never explains what CSI, Pose, MediaPipe, or PCK are, and every line earns its place.	3 / 3
Actionability	Provides concrete, executable guidance — a real example result ('PCK@20 59.5% vs 50% baseline = +9.4 pp'), an explicit reporting format, and a specific command ('ruview_claim_check the writeup') — copy-paste ready as a process.	3 / 3
Workflow Clarity	The 'Before you publish a number' checklist is a clear four-step sequence with an explicit validation checkpoint (claim_check flags untagged/perfect claims), matching the validated-workflow anchor.	3 / 3
Progressive Disclosure	Under 50 lines with well-organized sections (baseline-first, Paths, publish checklist) and no need for external references; per the simple-skills note this earns a 3.	3 / 3
	Total	12 / 12 Passed

Description

82%

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, distinctive description with good domain trigger terms; its only gap is the lack of an explicit 'Use when...' trigger clause, which caps completeness.

Suggestions

Append an explicit trigger clause, e.g. 'Use when training or evaluating WiFi CSI pose models, or before quoting any PCK result.'

Add common lay phrasing such as 'human pose estimation' to broaden natural discovery beyond the WiFi-specific framing.

Dimension	Reasoning	Score
Specificity	Names multiple concrete actions and paths — 'Train/evaluate WiFi pose models', 'camera-supervised (MediaPipe + CSI)', 'camera-free (WiFlow)', and 'checked against the mean-pose baseline' — matching the multi-action anchor.	3 / 3
Completeness	Clearly states what the skill does, but there is no explicit 'Use when...' or equivalent trigger clause; per the guidelines a missing trigger clause caps completeness at 2.	2 / 3
Trigger Term Quality	Covers natural terms a user in this domain would say — 'train', 'evaluate', 'pose models', 'WiFi pose', 'PCK' — alongside the technical labels, giving good trigger coverage.	3 / 3
Distinctiveness Conflict Risk	The WiFi CSI→pose niche plus the honesty/baseline-checking framing is a clear, distinctive trigger set unlikely to collide with other skills.	3 / 3
	Total	11 / 12 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: ruvnet/RuView
Path: harness/ruview/.claude/skills/train-pose/SKILL.md
Commit: d9dfea2

Reviewed: about 14 hours ago

Table of Contents

Discovery Implementation Validation

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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.