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Ccf Visual Composer

CommunityPopular
mikubaka88
ccf-visual-composer

Render and redesign CCF figures, visual tables, and method/architecture diagrams from supplied content. 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.

Overview

Publishermikubaka88
RepositoryCCFA-Skills
Skill nameccf-visual-composer
Stars
2.6K
Forks
116
Bundled files
18
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 18 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mikubaka88 on GitHub. Read the source before you install it.

Installation

Install the Ccf Visual Composer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mikubaka88/CCFA-Skills.git /tmp/CCFA-Skills
mkdir -p .claude/skills
cp -r /tmp/CCFA-Skills/ccf-visual-composer .claude/skills/ccf-visual-composer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ccf Visual Composer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ccf Visual Composer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ccf Visual Composer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CCF Visual Composer

Family File Contract

Before writing, resolve the canonical output and one stable working directory per task/artifact. Reuse explicit or established task paths; otherwise use project-root ccfa-workfiles/<purpose>/<artifact-id>/, with source/, assets/, cache/, and build/ only as needed. Update current files in place; do not scatter intermediates or create iteration copies. Preserve inputs and required evidence; clean only verified disposable files created by this task. Use UTF-8 text I/O and check Chinese text after saving or rendering. For file work, apply artifact-contracts.md and reuse the same paths across skill transitions.

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.

Keep one integrating owner and actively use other skills to resolve missing prerequisites or check material findings. Reuse applicable evidence; do not skip necessary groundwork to save tokens. Before finalizing, integrate contributions and verify affected results. Follow the conditional cooperation routes; avoid unrelated stages and 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.

ModeUse and reference
visual-contractNon-trivial content/evidence/output decisions: references/visual-contract.md.
figure-design, python-plottingNumerical plots: select from references/python-plot-recipes.md, then 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-generationNew concept: references/architecture-diagram-generation.md; paper-specific grammar only from references/paper-vs-presentation-diagrams.md.
pure-svg-generationExplicit deterministic route: use the supported topology and vector authoring source directly.
editable-reconstructionSemantic reconstruction sections in references/architecture-diagram-generation.md; load references/editable-pptx.md only for PPTX.
reference-layout-blueprintSupplied reference composition: references/reference-layout-blueprint.md; use references/adaptive-architecture-style.md when prompt refinement is needed.
icon-systemNative primitives, reusable licensed icons, or necessary custom assets: references/icon-system.md.
table-design, layout-integrationSupplied values, panel/float/caption placement: references/figure-table-layout.md.
render-qaInspect 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

  1. Resolve the requested artifact, existing source, final size, destination, formats, and scientific takeaway. For file work, read ../ccf-common/references/artifact-contracts.md once and resolve canonical output and working paths before rendering. Keep unrelated figures in separate stable working directories; honor existing project paths.
  2. Resolve scientific prerequisites before rendering dependent content: source values, units, metric meaning, topology, and intended message. Use ccf-experiment-designer for unresolved result semantics, the method's owner for topology, or ccf-integrity-auditor for source conflicts; integrate their evidence without inventing missing content. Reuse one specification for topology, labels, data locations, layout, style, and provenance. Do not save overlapping contracts, prompt drafts, wireframes, or QA logs. A minor edit reuses unchanged prerequisites and checks only affected dependencies.
  3. 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.
  4. 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 unless alternatives are requested. Search or generate assets only to resolve a specific unmet need.
  5. 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.
  6. 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.
  7. Check changed outputs for data/topology, whole-figure compactness, shared edges/baselines, final-size text, clipping, contrast, and requested editability. A local edit also checks attached connectors and protected regions. Render affected pages/slides first; broaden only when a changed shared style/layout affects them. Save one current preview under the working build directory; reuse satisfactory inspections.
  8. 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 to ccf-paper-writer, and claim mismatches to ccf-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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ccf Visual Composer AI skill do?

Render and redesign CCF figures, visual tables, and method/architecture diagrams from supplied content. 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.

Why use Ccf Visual Composer on TypingMind?

Because you install it once and use it with any model. Ccf Visual Composer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ccf Visual Composer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mikubaka88/CCFA-Skills/tree/main/ccf-visual-composer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ccf Visual Composer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ccf Visual Composer?

As many as you like. As long as a model supports skills, you can use Ccf Visual Composer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ccf Visual Composer AI skill free?

Yes. It is published on GitHub by mikubaka88 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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