native-transparent-ima…
Generate new raster assets that must contain native pixel transparency, then verify the untouched PNG or WebP before delivery.…
Develop and evaluate logo concepts when the explicit design problem is semantic fusion: two or more brand meanings must share a contour, stroke, negative space, glyph skeleton, or shape system. Use for fusion-focused design or redesign, critique, comparison, or prompt-only work
$ npx -y skills add zseven-w/craft-skills --skill logo-semantic-fusion --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/logo-semantic-fusionContext preview
The summary Claude sees to decide when to auto-load this skill.
Develop and evaluate logo concepts when the explicit design problem is semantic fusion: two or more brand meanings must share a contour, stroke, negative space, glyph skeleton, or shape system. Use for fusion-focused design or redesign, critique, comparison, or prompt-only work
name: logo-semantic-fusion description: "Develop and evaluate logo concepts when the explicit design problem is semantic fusion: two or more brand meanings must share a contour, stroke, negative space, glyph skeleton, or shape system. Use for fusion-focused design or redesign, critique, comparison, or prompt-only work on a logo, symbol, app icon, monogram, or wordmark. Do not use for generic logo creation, general identity systems, illustration, packaging, or type selection unless semantic fusion is a stated or clearly central requirement."
Build one structurally shared idea. Do not force fusion when a simpler wordmark, single symbol, or broader identity system serves the brand better.
Proceed with fusion concepting only when all are true:
1. At least two distinct brand signals are worth encoding. 2. A plausible shared contour, stroke, void, glyph skeleton, or module system can carry them. 3. Fusion improves the brand idea without sacrificing required-script legibility, category trust, or recognition at the intended scale.
If any condition fails, state why and recommend a pure wordmark, single symbol, abstract mark, or identity-system route. In critique or compare mode, treat forced fusion as a finding instead of ending the review.
Extract or reasonably infer:
State consequential assumptions. Do not improvise unfamiliar letterforms or characters: consult authoritative form references and require a fluent reader or native reviewer before calling the result production-ready.
Map up to three primary signals to visual structures and language structures. Describe geometry, not only nouns.
During early ideation, list provisional category clichés and mark the avoid map `not screened`; do not block concept generation on open-ended research. Before advancing a direction or claiming distinctiveness, review a bounded current sample of relevant-category marks and the closest structural analogues. Record sources, dates, recurring silhouettes, distinctive constructions, and unresolved gaps without implying an exhaustive search. If current screening is unavailable, keep the work `not screened` and make no originality claim. Treat this as design-direction screening, never trademark clearance.
Create at least six thumbnails across at least three families and three fusion methods. Make every family change both:
Count a change of method alone as a variant, not a new family. Name the shared structural move for every thumbnail. Reject pasted icons, complete-symbol overlaps, generic containers, explanation-dependent readings, and close imitation.
Prefer two signals in the core mark. Add a third only when the same geometry carries it.
Score candidates from 1 to 5 on brand fit, fusion integrity, screened distinctiveness, smallest-size legibility, one-color reproducibility, and cultural/category safety. Use higher scores for better or safer outcomes.
Advance only when no score is below 3, fusion integrity and cultural/category safety are at least 4, and the mean is at least 3.8. A hard-gate failure in the review checklist overrides the score. Lock silhouette, fusion relationship, dominant angle, stroke or fill logic, and essential negative space before rendering.
1. Draw in black and white. 2. Normalize weight, corners, curves, spacing, and optical balance. 3. Remove or responsively replace details that fail at the smallest committed size. 4. Add color only after monochrome works. 5. Solve the wordmark lockup separately unless it is the fused mark.
Use image generation for isolated flat concept sheets, not precision masters. Rebuild selected geometry as clean vector paths when production precision matters.
Run the review checklist on the rendered output. Test required sizes, one color, reverse, grayscale, and silhouette. If the full mark fails at small sizes, create a responsive micro mark and define the switch point.
Do not report visual QA from a prompt, source file, or intention. If no artifact was inspected, state `visual QA not run`. Preserve locked invariants during local fixes, then rerun every applicable check.
For production delivery, define clear space, minimum sizes, responsive variants, lockups, color variants, and basic vector/export requirements. Mark non-applicable items explicitly. For concept-only or prompt-only work, list these checks as pending rather than implying completion
面向 AI Agent 的研究驱动、评测驱动型 Skill 集合。 Craft Skills 把可迁移的专业方法提炼成聚焦的工作流,并为每个 Skill 明确触发边界、退出条件、原创案例与前向测试。它不是视频摘要库、提示词合集, 也不是模仿创作者风格的素材仓库。 由 Fini Yang 创建并策划, ZSeven 维护。
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