Conciseness

The body is efficient: terse bullets, a sharp-edges table, and model-specific facts ("Distribution Focal Loss (DFL) removal and MuSGD optimizer") that a model may not already know. Minor trimmable instances remain — the "Role" and "Purpose" lines restate the description, and parentheticals like "without Non-Maximum Suppression (reducing latency and complexity)" explain a concept Claude already knows — which matches anchor 4 rather than the lean 5.

Actionability

Almost everything describes rather than instructs: "Mastery of end-to-end inference", "Ability to segment objects using natural language descriptions", "Leverage Florence-2, PaliGemma 2, or Qwen2-VL". There is no executable code, command, API call, or parameter anywhere — only high-level hints like "Combine YOLO26 for fast 'candidate proposal' and SAM 3 for 'precise mask refinement'", which matches anchor 2 (minimal concrete guidance, missing the specific steps to execute) and is not the entirely vague anchor 1 since the Sharp Edges table gives a few semi-concrete remedies ("Use quantized/distilled versions", "Use descriptive prompts ('the 5mm bolt' instead of just 'bolt')").

Workflow Clarity

No multi-step process is ever sequenced with concrete steps or validation checkpoints. The Patterns section gestures at rough sequences ("Combine YOLO26 for fast 'candidate proposal' and SAM 3 for 'precise mask refinement'"; "Integrate monocular depth maps with geometric homographies") but never enumerates ordered steps or how to verify results, matching anchor 2. It is not a destructive/batch skill so the cap-3 rule is irrelevant, and the partial pipelines keep it above the incoherent anchor 1.

Progressive Disclosure

The body (~65 lines) is well organized into clear sections (When to Use, Capabilities, Patterns, Anti-Patterns, Sharp Edges, Related Skills) with no nested or dead references and no bundle files to mislink — matching anchor 4 (good structure, content appropriately placed). It is not anchor 5 because it slightly exceeds the 'under 50 lines with no need for external references' exception, and per-capability details (e.g., calibration pipelines, deployment recipes) could live in one-level-deep reference files.

Specificity

The description names the domain and sub-areas ("Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis") but frames them as expertise claims rather than concrete actions — there is no action verb like "detects", "segments", or "deploys". This matches anchor 3 (domain plus limited concrete content) but the role framing ("SOTA Computer Vision Expert (2026)") rather than a list of several specific actions keeps it below anchor 4.

Completeness

The 'what' is clear (specializations in detection, segmentation, VLMs, spatial analysis) but there is no 'Use when...' clause or equivalent trigger guidance anywhere in the description. Per the judging guideline, a missing 'Use when' clause caps completeness at 3, which this squarely matches.

Trigger Term Quality

Relevant keywords exist ("Computer Vision", "Segment Anything 3", "spatial analysis") but common natural phrases users would say — "object detection", "image segmentation", "depth estimation", "tracking", "YOLO" — are absent. It is above anchor 2 (not purely generic jargon) but lacks the variation coverage of anchor 4.

Distinctiveness Conflict Risk

"Computer Vision" with distinctive model names (YOLO26, SAM 3) carves a clear niche that is mostly distinguishable from other skills, matching anchor 4. It stays below anchor 5 because the related skills listed in the body (ai-engineer, robotics-expert) indicate some overlap risk with adjacent AI/engineering skills, and "real-time spatial analysis" is fairly broad.