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Get Started Free →Use this plugin when the user wants a "Takram Organic Frame" HyperFrames motion video — Soft-tech radial node graph as art — frosted rounded card, curved links drawing in, nodes popping outward, gentle float.
.claude/skills/nexu-io-video-template-frame-takram-organic/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -61% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -22% | 0% |
Soft-tech radial node graph as art — frosted rounded card, curved links drawing in, nodes popping outward, gentle float.
A HyperFrames-ready HTML + CSS + GSAP motion composition, bundled under source/. It renders deterministically to MP4 / WEBM at 16:9, 1:1, default 15s, 60fps.
Best for: System / architecture concept reveal · Warm, human product story · Network or memory-graph explainer
source/index.html to understand the named layers and the animation timeline.source/; do not introduce external network assets that would break a headless render.Source: html-video templates/frame-takram-organic (license Apache-2.0). Derived from huashu-design (alchaincyf (花叔 · 花生), MIT) — https://github.com/alchaincyf/huashu-design. Stylistic inspiration (L1, not affiliated): Takram.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 11,979 | 20,319 | +70% | 1 | 1 | 0% | 2,140 | 2,545 | +19% | 0 | 0 | — |
case-07 | fail→pass | 14,082 | 10,590 | -25% | 1 | 1 | 0% | 2,378 | 2,144 | -10% | 0 | 0 | — |
case-02 | pass→pass | 12,207 | 12,458 | +2% | 1 | 1 | 0% | 2,468 | 2,844 | +15% | 0 | 0 | — |
case-03 | pass→pass | 16,204 | 37,940 | +134% | 1 | 1 | 0% | 2,989 | 8,516 | +185% | 0 | 0 | — |
case-04 | fail→fail | 39,260 | 36,987 | -6% | 1 | 1 | 0% | 8,237 | 8,222 | -0% | 0 | 0 | — |
case-05 | fail→fail | 41,640 | 39,122 | -6% | 1 | 1 | 0% | 8,231 | 8,525 | +4% | 0 | 0 | — |
case-06 | fail→fail | 3,038 | 5,325 | +75% | 1 | 1 | 0% | 476 | 520 | +9% | 0 | 0 | — |
case-08 | fail→pass | 9,500 | 1,614 | -83% | 1 | 1 | 0% | 1,433 | 565 | -61% | 0 | 0 | — |
case-09 | fail→pass | 8,760 | 3,955 | -55% | 1 | 1 | 0% | 1,368 | 971 | -29% | 0 | 0 | — |
case-10 | fail→pass | 7,747 | 3,906 | -50% | 1 | 1 | 0% | 1,304 | 834 | -36% | 0 | 0 | — |
case-11 | fail→pass | 17,492 | 4,644 | -73% | 1 | 1 | 0% | 1,378 | 1,074 | -22% | 0 | 0 | — |
case-12 | pass→pass | 16,814 | 13,919 | -17% | 1 | 1 | 0% | 2,369 | 2,304 | -3% | 0 | 0 | — |
case-13 | fail→pass | 13,191 | 11,302 | -14% | 1 | 1 | 0% | 1,943 | 1,938 | -0% | 0 | 0 | — |
case-14 | pass→pass | 11,954 | 3,680 | -69% | 1 | 1 | 0% | 1,841 | 922 | -50% | 0 | 0 | — |
case-15 | pass→pass | 14,629 | 10,642 | -27% | 1 | 1 | 0% | 2,231 | 1,858 | -17% | 0 | 0 | — |
case-16 | fail→pass | 13,620 | 6,581 | -52% | 1 | 1 | 0% | 2,135 | 1,284 | -40% | 0 | 0 | — |
case-17 | pass→pass | 14,546 | 12,384 | -15% | 1 | 1 | 0% | 1,936 | 2,130 | +10% | 0 | 0 | — |
case-18 | fail→pass | 19,579 | 15,854 | -19% | 1 | 1 | 0% | 2,869 | 2,671 | -7% | 0 | 0 | — |
case-19 | fail→pass | 9,556 | 7,299 | -24% | 1 | 1 | 0% | 1,358 | 1,383 | +2% | 0 | 0 | — |
case-20 | fail→pass | 15,791 | 1,617 | -90% | 1 | 1 | 0% | 2,354 | 579 | -75% | 0 | 0 | — |
case-21 | fail→pass | 20,445 | 2,616 | -87% | 1 | 1 | 0% | 3,059 | 718 | -77% | 0 | 0 | — |
case-22 | pass→pass | 13,824 | 2,226 | -84% | 1 | 1 | 0% | 2,067 | 651 | -69% | 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 21 counted toward the lift figure. The other 1 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 +50 percentage points is the difference between those two pass rates over the 21 comparable cases.
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.