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Visual

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EricTechPro
visual

Turn the current work into one polished, self-contained HTML visual page — a recap of a branch's changes, a plan, a map of a folder, endpoint set, or architecture, or an interactive drill-down map of a skill pack or system (zoom, pan, click a node for details, double-click to open its inner view). Use when the user runs /visual or asks to visualise, picture, or visually recap a branch, PR, diff, plan, or part of a codebase, or to map a skill pack, its lanes, scripts, and hooks.

Overview

PublisherEricTechPro
Repositorysuper-board
Skill namevisual
Stars
464
Forks
136
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Visual 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.
    https://github.com/EricTechPro/super-board/tree/main/skills/visual
  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/EricTechPro/super-board.git /tmp/super-board
mkdir -p .claude/skills
cp -r /tmp/super-board/skills/visual .claude/skills/visual
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Visual 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 Visual 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 Visual 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.

/visual

One command, one page. You write a data.json; scripts/visual.py render injects it into assets/template.html, which draws everything inline (SVG diagrams, file map, diffs) with no build step and no runtime CDN. Paths below are relative to this skill's folder; run the script from inside the project being visualised.

Pick the mode

An argument wins: /visual recap [base], /visual plan [file], /visual map [skills-dir or map.json], /visual <path or thing> (explore).

With no argument, run python3 scripts/visual.py detect and take the first that holds:

  1. A plan was drafted in this conversation and not yet built → plan, from that text.
  2. suggested is recap (branch ahead of base, or uncommitted changes) → recap.
  3. planCandidates is non-empty → plan, from the newest one.
  4. Otherwise → explore the current folder.

Say the chosen mode and its source in one line, then keep going.

Run it

  1. Gather. Recap: python3 scripts/visual.py facts [--base REF], then read git diff <mergeBase> once, in order, taking notes; scope is the whole work unit of this conversation. Plan: read the plan and the real files it names. Explore: read the target; hand wide sweeps to a sub-agent.
  2. Write data.json in the scratchpad, following references/schema.md and the section bar in references/sections.md.
  3. Render: python3 scripts/visual.py render data.json. It prints {"out", "bytes", "missingHunks", "check"} and opens the page. Fix each missing hunk (wrong path or contains) and re-render.
  4. Check. render runs visual.py check in headless Chrome: light and dark screenshots (plus one drilled-in view for a map), and an audit of every diagram or view: edge crossings, edges sharing a line, labels lying on a line or a node, and text overflowing its box (check.counts). Exit 2 means a problem: move nodes (col/row, or a view layout), cut edges a view does not need, shorten labels, and re-render. Read the PNGs it lists; a broken diagram or empty section means fix and re-render. --no-check skips it; --shots DIR picks where the PNGs go.
  5. Reply with the path, the mode, and a 1–3 line gist. Offer to publish it as an Artifact (the page is fully inline); publish only on a yes.

Map mode: a drill-down map

For a skill pack or any system with nested levels. The page is a clean flowchart, light first with a dark mode: one top bar (breadcrumbs, ⌘K search over nodes and views, theme, export, keys), a column browser of views on the left, the map in the middle, and a short details strip under it (header, then What · When · How · Links tabs). Wheel or pinch zooms, drag pans, the minimap moves the camera, a click opens the details, and double-click or Enter on a stacked node zooms into its inner view. Run-order views get swimlanes (Ask → Check → Fix → Finish …) when their parts name the phase. Breadcrumbs, Esc, and the browser back button go up; #view=<id>&node=<id> deep-links. On a phone the details become a bottom sheet.

  1. Skeleton. python3 scripts/visual.py skillmap <skills-dir> --deep --out map.json reads SKILL.md frontmatter, families.json, /skill mentions, referenced scripts, and hooks/settings-snippet.json; --deep follows every external skill into the skills it triggers. Not a skill pack? Write map.json by hand.
  2. Enrich. Read the skills and fill every node's what, when, how, and real source — the panel shows all four, so none stays empty. Main steps first: keep process views to roughly four to six columns, set mainSteps: true, and move scripts, state, and policies into a step’s child view. Every original node stays reachable. Hub views keep comb connectors. Mark outside skills kind: external with author (GitHub login) and add authors; render embeds their avatars. Fix edges and give each view a short caption. references/schema.md § Map model is the contract.
  3. Render: python3 scripts/visual.py render --map map.json. It searches each view for a crossing-free layout and bakes it into the page, then runs the check. Publishing the page (GitHub Pages)? Add --source-base https://github.com/OWNER/REPO/blob/main/ (and --source-root <dir> when the hosted repo is a subfolder) so Source links open the hosted file. Finish with steps 4–5 of Run it.

Rules

  • Grounded: in recap mode, files, +/-, and hunks come from git through render (hunks[].file + contains); hand-typed diff lines are for plan sketches only. Prose is the one free-text surface; label anything inferred as inferred.
  • Diagram first: every page carries at least one diagram of the real mechanism — new endpoints, modules, data flow — with change flags on what moved. Columns follow the flow, rows hold peers.
  • Lean, not thin: no boilerplate intro or "review the diff anyway" prose; every block says something specific about this change.
  • Secrets: redact keys, tokens, and .env values in every block (sk-•••).
  • Plan is read-only: make no source edits while planning.
  • Output lands in a gitignored place: _tmp/<date>-visual/ when the repo ignores _tmp/, else .visual/ (self-ignoring). --out overrides.

References

  • references/schema.md — the data.json contract, diagram layout, and the map model; open before writing data.
  • references/sections.md — per-mode section list and quality bar; open before writing data.
  • THIRD_PARTY_NOTICES.md — Adapted from BuilderIO/skills (MIT); diagram style and viewer UX from tt-a1i/archify (MIT).

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 Visual AI skill do?

Turn the current work into one polished, self-contained HTML visual page — a recap of a branch's changes, a plan, a map of a folder, endpoint set, or architecture, or an interactive drill-down map of a skill pack or system (zoom, pan, click a node for details, double-click to open its inner view). Use when the user runs /visual or asks to visualise, picture, or visually recap a branch, PR, diff, plan, or part of a codebase, or to map a skill pack, its lanes, scripts, and hooks.

Why use Visual on TypingMind?

Because you install it once and use it with any model. Visual 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 Visual in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EricTechPro/super-board/tree/main/skills/visual. 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 Visual?

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 Visual?

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

Is the Visual AI skill free?

Yes. It is published on GitHub by EricTechPro 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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