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Get Started Free →Render Obsidian-style weighted graph views from book JSON, keyword indexes, TOCs, card data, or node-link data. Use when the user asks for Obsidian graph view, graph view png, weighted graph, keyword graph, directory-keyword graph, spherical graph visualization, or high-weight nodes centered with clear Chinese labels.
.claude/skills/twhsi-obsidian-graph-view/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -47% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -19% | 0% |
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:
nodes[] with id, label, weight, and optional kind.edges[] with source, target, and optional weight.N nodes by descending weight; default to 15 if the user asks for a focused graph.When the data does not already provide Trail groups, infer them from repeated keywords, chapter labels, or user-provided concepts. For example:
危機演化線: 馬爾薩斯、達爾文、危機、演化夢想救援線: TARS、Brand、Plan A、夢想、救援檢查背叛線: 曼恩、異常、數據、背叛、檢查卡片系統線: Agent、Skill、卡片盒、半人馬、五維空間If a node matches two Trail groups, draw it as a two-color taiji sphere and keep the label explicit.
Use scripts/render_obsidian_graph_view.py as the starting point for JSON-to-SVG/HTML/PNG graph rendering. Patch the script for project-specific schemas rather than rewriting from scratch.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 17,960 | 4,679 | -74% | 1 | 1 | 0% | 3,362 | 1,022 | -70% | 0 | 0 | — |
case-02 | fail→fail | 17,573 | 6,286 | -64% | 1 | 1 | 0% | 3,004 | 1,212 | -60% | 0 | 0 | — |
case-03 | fail→fail | 20,154 | 5,240 | -74% | 1 | 1 | 0% | 3,718 | 1,052 | -72% | 0 | 0 | — |
case-04 | fail→pass | 15,349 | 12,082 | -21% | 1 | 1 | 0% | 2,375 | 2,883 | +21% | 0 | 0 | — |
case-05 | pass→pass | 5,671 | 5,438 | -4% | 1 | 1 | 0% | 940 | 1,740 | +85% | 0 | 0 | — |
case-10 | fail→pass | 16,219 | 13,634 | -16% | 1 | 1 | 0% | 2,459 | 2,843 | +16% | 0 | 0 | — |
case-06 | fail→pass | 10,140 | 4,402 | -57% | 1 | 1 | 0% | 1,507 | 1,485 | -1% | 0 | 0 | — |
case-07 | pass→pass | 13,305 | 6,102 | -54% | 1 | 1 | 0% | 1,828 | 1,588 | -13% | 0 | 0 | — |
case-08 | pass→pass | 19,047 | 13,980 | -27% | 1 | 1 | 0% | 2,978 | 3,124 | +5% | 0 | 0 | — |
case-09 | fail→pass | 15,467 | 2,628 | -83% | 1 | 1 | 0% | 2,311 | 1,218 | -47% | 0 | 0 | — |
case-11 | pass→pass | 18,737 | 16,292 | -13% | 1 | 1 | 0% | 3,125 | 3,606 | +15% | 0 | 0 | — |
case-12 | pass→pass | 18,713 | 6,899 | -63% | 1 | 1 | 0% | 3,288 | 1,993 | -39% | 0 | 0 | — |
case-13 | pass→pass | 18,141 | 16,660 | -8% | 1 | 1 | 0% | 2,580 | 3,262 | +26% | 0 | 0 | — |
case-14 | fail→pass | 8,885 | 2,585 | -71% | 1 | 1 | 0% | 1,418 | 1,144 | -19% | 0 | 0 | — |
case-15 | fail→pass | 9,841 | 2,531 | -74% | 1 | 1 | 0% | 1,527 | 1,181 | -23% | 0 | 0 | — |
case-16 | pass→pass | 9,140 | 2,600 | -72% | 1 | 1 | 0% | 1,357 | 1,210 | -11% | 0 | 0 | — |
case-17 | fail→pass | 8,325 | 3,126 | -62% | 1 | 1 | 0% | 1,275 | 1,235 | -3% | 0 | 0 | — |
case-18 | pass→pass | 16,229 | 7,340 | -55% | 1 | 1 | 0% | 2,408 | 1,936 | -20% | 0 | 0 | — |
case-19 | pass→pass | 10,637 | 7,839 | -26% | 1 | 1 | 0% | 1,881 | 2,043 | +9% | 0 | 0 | — |
case-20 | pass→pass | 18,914 | 18,225 | -4% | 1 | 1 | 0% | 3,371 | 4,539 | +35% | 0 | 0 | — |
case-21 | pass→pass | 7,704 | 7,281 | -5% | 1 | 1 | 0% | 1,287 | 1,955 | +52% | 0 | 0 | — |
case-22 | pass→pass | 15,679 | 17,121 | +9% | 1 | 1 | 0% | 3,068 | 4,021 | +31% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 19 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +32 percentage points is the difference between those two pass rates over the 19 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.