Mostly actionable but padded with low-value sections ('Best Practices', 'Future Enhancements', 'Example Applications'), an oversized when-to-use list, and a stray non-English line, so it could be tightened; not a 1 because the core is not explaining concepts Claude already knows.

Provides complete, copy-paste-ready Python, bash, and JavaScript examples with concrete parameters (thresholds, bbox formats, cv2 calls), matching the fully-executable anchor; not a 2 because the code is real rather than pseudocode.

Installation and processing steps are sequenced, but there are no explicit validation checkpoints, and the batch-processing example (ThreadPoolExecutor) lacks the feedback loops the rubric requires for batch operations, capping this at 2.

No bundle files exist and the 480-line body is monolithic, with model specs, troubleshooting, and references inlined that could be split into separate files; the simple-skill exemption (under 50 lines) does not apply, so it is not a 3, but sections are reasonably organized so it is above 1.

Lists multiple concrete actions — 'Real-time hand detection', 'Outputs bounding boxes for hands', 'Supports video input/output with labeled hand boxes' — matching the multiple-specific-actions anchor.

Clearly states what the skill does but lacks any 'Use when...' clause or equivalent explicit trigger guidance, so per the rubric it is capped at 2.

Includes natural terms like 'hand detection' and 'egocentric videos' but misses common variations a user might say ('hand tracking', 'first-person/wearable camera'), so it sits at the some-keywords anchor rather than full coverage.

The egocentric/EgoHands-trained bounding-box niche is distinctive and unlikely to trigger for unrelated skills, matching the clear-niche anchor; not a 2 because the specialization is explicit rather than merely 'somewhat specific'.