
ccf-visual-composer
Render and redesign scientific figures, visual tables, and method/architecture diagrams from supplied content, including biological data. Use for 绘图美化, 排版, 配色, GPT Image 2 generation, pure SVG, and editable SVG/PDF/PPTX. Preserve values and topology. Experiment evidence design belongs to ccf-experiment-designer; manuscript rewriting and PDF-to-writing exemplars are separate.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/mikubaka88/ccfa-skills --skill ccf-visual-composer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads ccf-visual-composer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
CCF Visual Composer
Family File Contract
Before file work, reuse canonical paths and one task/artifact working directory; default to project-root ccfa-workfiles/<purpose>/<artifact-id>/. Update in place, preserve inputs, and use UTF-8 with Chinese-text checks. Apply artifact-contracts.md for naming, ownership, and task-created cleanup; handoffs inherit these paths.
Collaboration Contract
Before specialist execution, read and apply ccf-humanization first, then ccf-common. At every handoff, reuse their applicable active rules or refresh missing/changed ones. Both preflights are required even without prose; detailed editing, experiment, and maintenance modes run only when relevant.
Integrate necessary specialist contributions under one owner; reuse valid prerequisites and verify affected results. Follow the cooperation routes without duplicate reports.
Invocation Controls
CCFA Handoff Mode: PARTIAL (Recommended). Follow metadata.ccf_skill_controls.handoff_question_mode, ../ccf-common/references/handoff-modes.md, and ../ccf-common/references/task-modes.md. Reuse shared rules already in context.
For a new scientific method or architecture concept, GPT Image 2 remains the default first-pass renderer unless the user requests pure SVG/code-first output or opts out. Use the verified host capability and its privacy/image instructions. Do not label an unknown backend as GPT Image 2. Existing authorization covers necessary generation and requested reconstruction; offer extra formats only when outside the request.
Core Rule
Create readable visuals from supplied content. Preserve values, units, uncertainty, exact labels, method modules, and typed connections. Quantitative plots use reproducible code. Missing data or topology requires the relevant evidence owner; do not invent it to complete a composition. Preserve scientific terminology and canonical uppercase acronyms; use natural case for ordinary labels.
Classify the destination as paper mechanism figure, presentation/poster, or README/outreach. Paper figures show representations and computation. Choose visual grammar from the method, not a fixed stage-card template.
For new compositions, use references/visual-contract.md: a compact content-fit canvas, shared alignment anchors, scaled gaps, and typography checked at final size. Do not default to a square or force a fixed aspect ratio. Times New Roman is the default; Comic Sans MS serves a requested comic treatment, subject to user/venue typography. For a new visual direction, choose one functional preset from references/adaptive-architecture-style.md. Inspect supplied references, allocate space by explanatory importance, and keep only necessary labels/numbers.
Modes And Selective References
Read only the relevant sections below. A local edit starts from the existing source and applicable QA; it does not reload the complete generation workflow.
| Mode | Use and reference |
|---|---|
visual-contract | Non-trivial content/evidence/output decisions: references/visual-contract.md. |
figure-design, python-plotting | Numerical plots across fields, including biological data: references/python-plot-recipes.md provides runnable examples for shared scales, entity colors, physical type size, and validated inputs. Import the needed recipe from resources/python/ccfa_plot_recipes.py; read implementation only to debug or adapt it. references/plot-inspiration-map.md is optional for an unresolved chart choice. |
architecture-generation | New concept: references/architecture-diagram-generation.md; paper-specific grammar only from references/paper-vs-presentation-diagrams.md. |
pure-svg-generation | Explicit deterministic route: use the supported topology and vector authoring source directly. |
editable-reconstruction | Semantic reconstruction sections in references/architecture-diagram-generation.md; load references/editable-pptx.md only for PPTX. |
reference-layout-blueprint | Supplied reference composition: references/reference-layout-blueprint.md; use references/adaptive-architecture-style.md when prompt refinement is needed. |
icon-system | Native primitives, reusable licensed icons, or necessary custom assets: references/icon-system.md. |
table-design, layout-integration | Supplied values, panel/float/caption placement: references/figure-table-layout.md. |
render-qa | Inspect the requested formats using the relevant checks in references/render-qa.md. |
Use references/palette-and-accessibility.md only when choosing or changing color semantics. Reuse an established palette and icon family when they remain suitable.
Workflow
- Resolve the requested artifact, existing source, final size, destination, formats, and scientific takeaway. For file work, read
../ccf-common/references/artifact-contracts.mdonce and resolve canonical output and working paths before rendering. Keep unrelated figures in separate stable working directories; honor existing project paths. - Resolve scientific prerequisites before rendering dependent content: source values, units, metric meaning, topology, and intended message. For related panels, resolve entity IDs/order, shared encodings, comparable scales, and any required image aspect ratios. Reject invalid inputs before plotting; do not clamp them into plausible results. Use
ccf-experiment-designerfor unresolved result semantics, the method's owner for topology, orccf-integrity-auditorfor source conflicts; reuse valid evidence and integrate only the missing contribution. Keep one specification for topology, labels, data locations, layout, style, and provenance, without duplicate contract, prompt, wireframe, or QA files. - Select the smallest rendering route that completes the task. A new architecture concept uses the default image workflow. For an existing SVG, PPTX, or plot script, directly edit that authoring source and export affected requested formats; do not run a new raster concept pass for a label, color, spacing, data, or export change. Raster edits follow the host image-editing workflow and use the existing image as reference.
- Preserve a detailed user prompt and add missing constraints once. For a full redesign, include the reference roles, layout geometry, text inventory, typography, and palette needed to make it concrete; do not truncate these to an arbitrary word limit. Reuse approved topology, layout tokens, and assets. Start with one complete candidate. Requested alternatives or Common's applicable
figure_variantspreference can add a meaningfully different composition using the same approved content;autorequires an unresolved design choice andonapplies to new design comparisons, not local edits. Keep each retained candidate in the existing artifact working directory and identify the chosen canonical result. Search or generate assets only to resolve a specific unmet need. - Inspect the draft against the layout and scientific contract. Correct vector/native text and geometry during requested reconstruction; for raster delivery, use a targeted image edit for text, spacing, or illustration defects and preserve accepted regions. If two attempts fail to fix the same defect, diagnose the cause and change the relevant strategy. Never stop merely at an attempt count while a feasible correction remains.
- Build requested editable outputs as semantic groups, live text, shapes, and typed connectors. Do not embed a whole raster and claim editability. Keep unavoidable raster assets separate and describe their actual editability. Generate downstream PDF/PPTX from the canonical authoring source without lossy round trips; retain only necessary reusable source and assets.
- Use one integrated inspection with two distinct decisions: scientific fidelity (data, scales, IDs, topology, labels) and visual quality (alignment, compactness, final-size type, contrast, clipping, editability). A local edit checks its affected dependencies and protected regions; reuse satisfactory inspections. Save one current preview under the working build directory. An additional independent assessment follows Common's effective
independent_reviewpreference or an explicit request; ordinary QA does not require another agent or generation call. - Finish the requested deliverable and check file placement, current exports, and disposable temporary files. New data/protocol decisions belong to
ccf-experiment-designer, manuscript prose toccf-paper-writer, and claim mismatches toccf-integrity-auditor; keep unaffected authorized work moving.
Output Contract
Deliver the requested visual and necessary editable source/formats first. A specification-only request needs no render. Return canonical paths, useful provenance, actual QA results, and material editability limits. Keep the full prompt, specification, and inventory in one reusable source record only when needed or requested; do not also repeat them in the final answer. Preserve explicit exact-output requests.
FAQ
What does the ccf-visual-composer skill do?
Render and redesign scientific figures, visual tables, and method/architecture diagrams from supplied content, including biological data. Use for 绘图美化, 排版, 配色, GPT Image 2 generation, pure SVG, and editable SVG/PDF/PPTX. Preserve values and topology. Experiment evidence design belongs to ccf-experiment-designer; manuscript rewriting and PDF-to-writing exemplars are separate.
How do I install ccf-visual-composer?
Run npx skills add https://github.com/mikubaka88/ccfa-skills --skill ccf-visual-composer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ccf-visual-composer/ (personal) or .claude/skills/ccf-visual-composer/ (project).
Which AI agents can use ccf-visual-composer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ccf-visual-composer safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains ccf-visual-composer?
It is published by mikubaka88 in mikubaka88/ccfa-skills. It has 1,918 recorded installs.
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