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Render Obsidian-style weighted graph views from book JSON, keyword indexes, TOCs, card data, or node-link data.
Passed 16 of 19 test cases with the skill, and 10 of 19 without it.
and 4 more in the full report
7 fixed12 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our note data contains terms such as 'Agent', 'Skill', '卡片盒', '半人馬', and '五維空間'. What semantic Trail sequence name should be inferred for this group?
| Checked | Without skill | With skill |
|---|---|---|
| Assigns the Trail group name '卡片系統線' to the keyword cluster. | Missed | Passed |
The instructions your model reads when the skill loads.
Create an Obsidian-like graph visualization from structured note/book data.
Produce three files when possible:
*.png for direct viewing and sharing*.svg for editable vector output*.html for browser re-renderingPrefer PNG dimensions around 1800 x 1300 unless the user asks otherwise.
PingFang TC / Heiti TC / system CJK fonts, white text, dark translucent label boxes, and avoid placing labels directly on dark edges.For EPUB/project-note JSON:
toc[] becomes chapter or directory nodes.index[] becomes keyword nodes.index[].links[] becomes keyword-to-chapter edges.toc[].cjk_count, keyword weight, and index link weights contribute to node size.chunks[].keyword_backlinks[] can be used to enrich link weights.For generic graph JSON:
test cases passed, vs 10 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.