Conciseness

The body is mostly efficient, teaching only bdistill-specific facts Claude would not know, with copy-paste commands and no padding like explaining what Ollama or pandas is. Minor trims possible: the two-paragraph promotional Overview ('turns your AI subscription sessions into a compounding knowledge base') could be tightened, and parts of Security & Safety Notes restate the obvious. This is score 4 ('efficient; minor instances that could be trimmed') rather than 5, and clearly above score 3's 'some unnecessary explanation'.

Actionability

Every section gives concrete, copy-paste-ready commands: 'pip install bdistill', 'claude mcp add bdistill -- bdistill-mcp', '/distill --adversarial medical', 'bdistill kb export -d medical -f csv', '/schema sepsis | hr:float, ... | risk:category[low,moderate,high,critical]', and 'bdistill extract --domain medical --model qwen3:4b'. These cover the common cases end-to-end (install, extract, search, export, local mode), matching the top anchor.

Workflow Clarity

A clear three-step sequence (Install → Extract → Search/export) with concrete commands per step, and the extraction operations are non-destructive so the destructive/batch validation cap does not apply. It falls short of 5 because there are no validation or feedback checkpoints at all (e.g., verifying the MCP server connected, checking extraction output before export), which is the gap the score-4 anchor describes.

Progressive Disclosure

No bundle files exist, and the ~90-line body is well-organized into focused sections that reasonably belong inline at this size — there are no buried or nested references and no monolithic wall of text. Score 4 rather than 5 because the body exceeds the under-50-line simple-skill threshold and the Output Format JSON example and Tabular ML schema syntax would be the natural candidates for one-level-deep reference files if the skill grows; not 3 because nothing is misfiled or clearly inlined that belongs elsewhere.

Specificity

The description names its domain ('structured domain knowledge', 'AI models') and one concrete action ('Extract'), with two modes ('in-session', 'via Ollama'), but does not list the other specific actions the skill performs (structuring, quality-scoring, searching, exporting). This matches 'Names domain and 1-2 concrete actions, but not comprehensive' (3) more than score 4's 'several specific actions'.

Completeness

The 'what' is clear ('Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama'), but there is no 'use when' clause or equivalent trigger guidance anywhere in the description — the body's 'When to Use This Skill' list is not part of the description. Per the judging guidelines, a missing 'Use when...' clause caps completeness at 3.

Trigger Term Quality

Relevant keywords are present ('domain knowledge', 'Ollama', 'open-source models', 'AI models'), but the natural phrases a user would actually say for the skill's use cases — 'knowledge base', 'Q&A dataset', 'lookup tables', 'training data' — are absent, so coverage lacks common variations. Not score 4 because the keyword set is thinner than 'good coverage; a few missing'; not score 2 because multiple genuinely relevant terms exist.

Distinctiveness Conflict Risk

The niche is fairly distinct — extracting knowledge from AI models via Ollama/bdistill is specific and unlikely to trigger general document or data skills — but it could overlap with data-collection/RAG-dataset skills ('AI models', 'knowledge' are broad terms). Mostly distinct with minor overlap risk (4); not 5 because the trigger surface is not as crisply unique as a file-extension-anchored skill.