Agent skill

Mindmap Render

by ai4s-research in ai4s-research/ai4s-skills

Generate beautiful, high-resolution mindmaps from Markdown unordered lists.

MITAuto-check passedDocuments & Office

Install Mindmap Render

skills CLI
$ npx skills add ai4s-research/ai4s-skills --skill mindmap-render -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ai4s-research/ai4s-skills mindmap-render --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ai4s-research/ai4s-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mindmap-render .claude/skills/mindmap-render && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mindmap-render
GitHub stars
237
Used in
1 other repo
Token cost
~3.1k tokens
SKILL.md length
1,640 words
Files
4 (incl. scripts)
Skills in repo
7
Repo updated
First seen
Licence
MIT

At a glance

Generate beautiful, high-resolution mindmaps from Markdown unordered lists.

  • Works in 3 steps: Determine input source → Render the mindmap → Deliver results
  • Tasks that involve HTML artifacts
  • SKILL.md covers When to use this skill, Prerequisites, Workflow and Important notes
  • Runs Python scripts from its folder; calls python, pip and playwright

What it does

Mindmap Render is an agent skill from ai4s-research/ai4s-skills. Generate beautiful, high-resolution mindmaps from Markdown unordered lists. Outputs interactive HTML, HD PNG, and PDF with colorful branch themes.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts (for example `scripts/generate_mindmap.py` and `tests/test_generate_mindmap.py`).

It sits in Documents & Office, covering HTML artifacts, Markdown and PDF. The repository describes itself as: Open-source agent skills for AI for Science: topic exploration, literature survey, experiments, paper writing, and integrity audit — driven by any coding agent. The licence is MIT.

When your agent uses it

  • Tasks that involve HTML artifacts
  • Tasks that involve Markdown
  • Tasks that involve PDF

Example prompts

  • “/mindmap-render”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Determine input source
  2. Render the mindmap
  3. Deliver results

What it can do on your machine

Read from SKILL.md and the folder at commit 744ab20. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • pip
    • playwright

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mindmap Render loads about 3.1k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 1,640 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from ai4s-research/ai4s-skills at commit 744ab20, republished under its MIT licence (© ai4s-research). 1,640 words, ~3,093 tokens.

Download SKILL.mdSave it as .claude/skills/mindmap-render/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
mindmap-render
description
Generate beautiful, high-resolution mindmaps from Markdown unordered lists. Outputs interactive HTML, HD PNG, and PDF with colorful branch themes.

When to use this skill

Use this skill when the user asks to:

  • Create a mindmap from a topic or data.
  • Convert a Markdown outline into a visual mindmap image or PDF.
  • Generate a colorful, presentation-quality mindmap with auto-export to PNG/PDF.
  • Build a structured outline (unordered list) and then render it as a mindmap.

Prerequisites

Assume the runtime environment already has Python 3.10+, Playwright, and Chromium installed (they are provisioned in the container). Do not proactively run pip install or playwright install — just run the render script directly. Only if the first run fails with a clear missing-dependency error (ImportError, missing-browser error, etc.), then repair the environment:

bash
pip install -r scripts/requirements.txt
playwright install chromium

and retry. Never install speculatively before a failure is observed.

Workflow

Step 1 — Determine input source

Ask the user (or infer from context):

  • Topic: What is the central theme?
  • Data source: Do they already have a Markdown file, or should you research and write one?
  • Source fidelity vs synthesis — a spectrum, not a binary. The more specifically the request points to a named existing artifact (a particular book's table of contents (ToC), a particular course's syllabus, a specific spec or documentation structure, a numbered chapter list), the more you should reproduce the source's real structure verbatim — preserve original labels and numbering, follow the source's natural depth, and add NO fabricated descriptions. The more the request is a broad topic with no single canonical source, the more the structural targets below apply. Most requests sit somewhere on this spectrum; judge and lean accordingly.
  • Theme: air (light blue glow + white cards, default), editorial (warm paper + jewel-tone branches), midnight (deep black + neon accents), or zen (soft misty background + muted pastels).
  • Language:
    • If the user explicitly specifies a language (e.g. "in English", "in Japanese"), use that language for all node text (labels + layer-5 descriptions).
    • Otherwise, match the language of the user's request; default to English when the request language is unclear.
    • Do not translate proper nouns, model names, or established technical terms — preserve them inline.

Default structural targets — for synthesis work only. These do NOT apply when you are faithfully reproducing a named source (in that case, follow the source's actual shape). They also yield to any numbers the user gives explicitly.

  • 1 root + 6–10 top-level branches (default aim: ~9).
  • Maximum depth = 5 layers (root → branch → subtopic → item → leaf-with-description). Layer 5 is reserved for the important, information-dense nodes — it is not a mandatory floor for every path.
  • Layer-5 leaves (when present) MUST carry a substantive description — around 200 Chinese characters (or ~150 English words if the mindmap is in English), roughly 2–4 sentences — that explains mechanism, why it matters, quantitative detail, or a concrete example. A one-line label is not enough at layer 5; if you cannot write ~200 Chinese characters of real content, the node does not belong at layer 5.
  • Intermediate nodes (layers 2–4) stay concise (1–10 words) and may themselves be terminal leaves when that is the right level of detail.
  • Asymmetry is required, not a flaw. Branches should be weighted by importance and information value, not padded for visual symmetry:
    • Pillar branches (where the real substance lives) should go deep and wide, with many children and rich layer-5 descriptions.
    • Supporting / well-known branches can stop at layer 2 or 3. Do not expand common knowledge the target reader already owns, and do not invent filler children just to match sibling counts.
    • When deciding "expand or stop," ask: Would a knowledgeable reader learn something here? If no, prune.
  • The hierarchy is intentionally irregular. In Step 2c, report the shape honestly rather than forcing every branch to layer 5.

If the user gives different numbers, use theirs; otherwise treat the defaults above as guidance with judgment — breadth/depth targets are firm, but per-branch expansion is deliberately uneven.

Step 2a — User provided a Markdown file

If the user already has a .md file, note its path and proceed to Step 3.

Step 2b — Generate the Markdown outline yourself

CRITICAL: If the user has NOT provided a .md file, you MUST perform web research BEFORE writing the outline. Do not rely solely on internal knowledge.

  1. Research (mandatory): Use WebSearch to find authoritative, high-quality sources:

    • Official book table of contents (publisher's catalog, Douban Books listing).
    • Wikipedia structured sections.
    • Academic course syllabi or reputable blog series.
    • Official documentation / white-paper outlines.
    • Recent industry reports, survey papers, or conference proceedings (e.g. NeurIPS, ICML, JPMorgan Quantitative Research).

    Fail loudly if the authoritative source cannot be found. When the user names a specific artifact (a particular book, edition, course, spec) and repeated searches do not surface its real ToC / syllabus / structure, STOP and tell the user: "I couldn't find the authoritative structure of [X]. Please paste the ToC, confirm the edition/title, or allow me to produce a synthesized overview instead." Never invent chapter/section structure to fill the gap. This is the single most important rule of this step.

  2. Write the outline — choose the mode based on Step 1's fidelity-vs-synthesis judgment:

    a) Faithful reproduction (user pointed to a specific named artifact and you located its real structure): copy the ToC/outline verbatim into a single-root bullet list. Preserve original labels, chapter numbering, and natural depth. Do not add layer-5 descriptions, do not force 6–10 top-level branches, do not pad to 5 layers — follow whatever shape the source actually has. The only transformations allowed are: wrapping everything under one root node, and cleaning trivial typography (e.g. converting full-width numbers consistently).

    b) Synthesis (broad topic, multi-source): distill the research into a single-root unordered-list Markdown file following the Step-1 structural targets.

    • Use standard - bullet lists; nesting = depth.
    • One top-level bullet = the root (layer 1).
    • Layers 2–4 (branch / subtopic / item): concise labels, 1–10 words each. A layer-2/3/4 node can be a terminal leaf when no deeper breakdown adds value.
    • Layer 5 (when used): a substantive description, ~200 Chinese characters (or ~150 English words for English mindmaps), covering mechanism + why it matters + concrete detail (numbers, names, example). Only create a layer-5 node when you have real content of that density; never pad.
    • Weight by importance. Give the pillar branches many children and deep layer-5 content; let well-known or low-information branches stay shallow. Target reader: an informed practitioner — skip what they already know, dwell on what is surprising, recent, or load-bearing.
    • Irregular depth is expected. A tree with 3 deep pillar branches and 6 shallow supporting ones is healthier than 9 uniformly-expanded branches full of filler.
  3. Save the file:

    • Save to mindmap-output/<topic>.md (or the current project directory).
    • Show the user the saved path and the first ~30 lines of the outline for confirmation.
Show full SKILL.md (557 more words)Show less
Step 2c — Self-check before rendering

If you wrote a faithful reproduction (Step 2b-a), skip the full audit. Just verify two things and report one line each: (1) the outline's labels and numbering match the source, (2) you did not inject any fabricated descriptions or extra layers. Then proceed to Step 3.

If you wrote a synthesis (Step 2b-b), do not skip. After saving the outline and before running the render script, verify against the structural target. Output a short audit block to the user:

Structure audit:
- Top-level branches: <N>   (target 6–10)
- Max depth reached:  <D>   (ceiling 5)
- Pillar branches (reach layer 5 with substantive content): <X>
- Shallow branches (stop at layer 2–3 by design): <Y>
- Layer-5 leaves with ≥~200 Chinese characters description: <A> / <total layer-5 leaves>
- Shape note: <one sentence justifying which branches go deep and which stay shallow, and why>

Red flags — rework the outline before rendering if any apply:

  • Layer-5 leaves that are one-line labels or under ~100 Chinese characters → either enrich them to ~200 Chinese characters of real content, or demote the node to layer 4.
  • Every branch reaches the same depth with similar child counts → you are padding for symmetry; prune the weakest branches back.
  • A branch exists only to list common knowledge the target reader already owns → cut it or collapse it.
  • Pillar branches are shallower than supporting branches → rebalance so information density follows importance.
Step 3 — Render the mindmap

Run the rendering script:

bash
python scripts/generate_mindmap.py \
  --md <path-to-md> \
  --output-dir ./mindmap-output \
  --title "<Topic Title>" \
  --theme <air|editorial|midnight|zen> \
  --scale 2

Arguments:

  • --md (required): Path to the Markdown file.
  • --output-dir: Where to place the results. Default is ./mindmap-output.
  • --title: Used for the HTML <title> and the output base file name.
  • --theme: air (designer-style airy blue glow + white rounded cards + soft pastel branch accents), editorial (magazine-style warm paper background + jewel-tone branches + dark serif text), midnight (pitch-dark background + neon accents + crisp light text), or zen (soft misty background + muted Morandi pastels + gentle serif text).
  • --scale: Upscale factor for the exported image (default 2). 1 = compact (~2–3 MB), 2 = crisp readable (~3–5 MB), 3+ = poster size. Larger numbers produce physically larger, more readable text.

Example (default shape from Step 1: ~9 branches, 5 layers, leaf descriptions):

bash
python scripts/generate_mindmap.py \
  --md mindmap-output/large-test.md \
  --output-dir ./mindmap-output \
  --title "Artificial Intelligence Panorama" \
  --theme air \
  --scale 3
Step 4 — Deliver results

Report the three generated files to the user:

  1. {title}.html — interactive mindmap (open in browser to zoom/pan/collapse). Live rendering: after starting an HTTP server in the same directory (e.g. python -m http.server) and accessing it through a browser, edit the .md file and refresh the page to see the update; opening it directly as a local file uses the embedded content, behaving the same as before.
  2. {title}.png — high-resolution full-page image (suitable for slides, social media, docs).
  3. {title}.pdf — vector-like PDF export with print background.

Important notes

  • Color system — "rainbow branches" with in-family shading. Each top-level branch owns one color family (hue); its descendants use the same hue with depth-based variation (deeper layers → slightly lighter + less saturated on light themes; slightly dimmer + less saturated on dark themes). You do not need to configure this — it is applied automatically by the render script based on the theme palette.
  • Do not pass raw paragraphs as the Markdown input. The renderer works best with bullet-list outlines. If the source text is prose, convert it into a hierarchical bullet list first.
  • The script automatically strips YAML frontmatter from the Markdown file so markmap can focus on the outline.
  • If the mindmap is very large, Playwright will resize the viewport to fit the entire diagram; full_page=True guarantees the PNG captures everything without clipping.
  • Never skip research when the user only gives a topic. The mindmap's quality depends on accurate, up-to-date, well-sourced hierarchies.
  • When researching, prefer sources that already have a clear hierarchy (ToCs, syllabi, wiki sections) so the resulting mindmap is accurate and useful.

© ai4s-research, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts) in skills/mindmap-render of ai4s-research/ai4s-skills.

  • SKILL.md
  • scripts/generate_mindmap.py
  • scripts/requirements.txt
  • tests/test_generate_mindmap.py

Open the folder on GitHubat commit 744ab20

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in ai4s-research/ai4s-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Paper Interpretationdigoal/blog8.6k—~1.5kAutomated safety check: PassGPL-2.0
Fulltext RetrievalAperivue/medsci-skills329—~1.9kAutomated safety check: PassMIT
Sci HTMLShZhao27208/Aut_Sci_Write208—~1.4kAutomated safety check: NotesMIT
Literatureimages Interpretationaipoch/medical-research-skills2k—~2.1kAutomated safety check: PassMIT

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Questions about Mindmap Render

What does Mindmap Render do?

Generate beautiful, high-resolution mindmaps from Markdown unordered lists. Mindmap Render is an agent skill from ai4s-research/ai4s-skills. Generate beautiful, high-resolution mindmaps from Markdown unordered lists.

When should I use Mindmap Render?

Mindmap Render fits situations like: tasks that involve HTML artifacts; tasks that involve Markdown; tasks that involve PDF.

How do I install Mindmap Render in Claude Code?

Run `npx skills add ai4s-research/ai4s-skills --skill mindmap-render -a claude-code`. Or copy the skill folder (skills/mindmap-render in ai4s-research/ai4s-skills) into .claude/skills/mindmap-render in your project. Claude Code loads it when a task matches its description.

How do I install Mindmap Render in Codex?

Run `npx skills add ai4s-research/ai4s-skills --skill mindmap-render -a codex`. Or copy the skill folder (skills/mindmap-render in ai4s-research/ai4s-skills) into .agents/skills/mindmap-render in your project. Codex loads it when a task matches its description.

Can I use Mindmap Render in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ai4s-research/ai4s-skills --skill mindmap-render -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mindmap-render, .gemini/skills/mindmap-render, .github/skills/mindmap-render and .opencode/skills/mindmap-render in your project.

What does Mindmap Render need to run?

Going by SKILL.md and its folder, Mindmap Render needs Python for the scripts in its folder and the command-line tools its instructions call (python, pip and playwright). Our summary lists: Python 3.

Does Mindmap Render access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Mindmap Render safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Mindmap Render use?

Mindmap Render is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mindmap Render use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mindmap Render?

Skills that share tags, products or a category with Mindmap Render: Ky Markdown Rebuilder (KyrieCheungYep/ky-markdown-rebuilder, 117 stars), Paper Interpretation (digoal/blog, 8.6k stars), Fulltext Retrieval (Aperivue/medsci-skills, 329 stars) and Sci HTML (ShZhao27208/Aut_Sci_Write, 208 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mindmap Render?

ai4s-research (a GitHub organization) maintains it in ai4s-research/ai4s-skills, which has 237 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on July 28, 2026.

Source: ai4s-research/ai4s-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.