Agent skill

Mindmap Mcts

by cheshireyang in cheshireyang/mindmap-mcts-skill

Run a visible reasoning-tree workflow with lightweight MCTS for complex Codex or Claude Code tasks.

MITAuto-check passedDevelopment

Install Mindmap Mcts

skills CLI
$ npx skills add cheshireyang/mindmap-mcts-skill --skill mindmap-mcts -a claude-code

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

GitHub CLI
$ gh skill install cheshireyang/mindmap-mcts-skill mindmap-mcts --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/cheshireyang/mindmap-mcts-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/mindmap-mcts .claude/skills/mindmap-mcts && 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-mcts
GitHub stars
110
Token cost
~1.9k tokens
SKILL.md length
683 words
Files
11 (incl. scripts)
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Run a visible reasoning-tree workflow with lightweight MCTS for complex Codex or Claude Code tasks.

  • Works in 2 steps: Decide whether this skill applies. Skip… → If no tree exists, create one
  • A task has multiple plausible hypotheses
  • SKILL.md covers Command Prefix, Startup, Per-Round Loop and Evaluation Scale, plus 3 more sections
  • Runs Python, Batch and PowerShell scripts from its folder; calls python

What it does

Mindmap Mcts is an agent skill from cheshireyang/mindmap-mcts-skill. Run a visible reasoning-tree workflow with lightweight MCTS for complex Codex or Claude Code tasks. Use when a task has multiple plausible hypotheses or designs, needs systematic debugging, requires option tradeoff exploration, involves repeated trial-and-error, or the user asks for a mindmap, reasoning tree, MCTS, visible exploration, branch evaluation, or evidence-backed problem solving.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts (for example `agents/openai.yaml`, `scripts/mindmap.py` and `scripts/mindmap_mcts/__init__.py`).

It sits in Development, covering Debugging. The repository describes itself as: 给智能体安装了思维导图,让 Codex/Claude code/Agent 在复杂问题上用“可见思维树 + 轻量 MCTS”来探索,而不是线性瞎试。 The licence is MIT.

When your agent uses it

  • A task has multiple plausible hypotheses
  • Needs systematic debugging
  • Requires option tradeoff exploration
  • Involves repeated trial-and-error

Example prompts

  • “/mindmap-mcts”

Requirements

  • Python 3
  • PowerShell

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Decide whether this skill applies. Skip it for one-step commands, obvious one-file edits, or direct factual lookups.
  2. If no tree exists, create one

What it can do on your machine

Read from SKILL.md and the folder at commit 74ba5a5. 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 9 files in scripts/ (Python, Batch and PowerShell), which the agent can run.

    Shell commands in SKILL.md call:

    • python

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

  • Network

    No URLs in SKILL.md.

    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 Mcts loads about 1.9k tokens when it runs. Until then it costs about 101 tokens; SKILL.md has 683 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~101
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

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 cheshireyang/mindmap-mcts-skill at commit 74ba5a5, republished under its MIT licence (© cheshireyang). 683 words, ~1,948 tokens.

Download SKILL.mdSave it as .claude/skills/mindmap-mcts/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
mindmap-mcts
description
Run a visible reasoning-tree workflow with lightweight MCTS for complex Codex or Claude Code tasks. Use when a task has multiple plausible hypotheses or designs, needs systematic debugging, requires option tradeoff exploration, involves repeated trial-and-error, or the user asks for a mindmap, reasoning tree, MCTS, visible exploration, branch evaluation, or evidence-backed problem solving.

MindMap-MCTS

Use this skill to keep complex work on a visible, evidence-backed reasoning tree. The bundled CLI owns the JSON truth source, UCB selection, backpropagation, markdown rendering, static HTML rendering, and interactive Markmap HTML rendering; the agent owns expansion, real probes, and judgment.

Command Prefix

Choose the command form that matches the current shell. First set the skill directory for the host that loaded this skill.

Codex on Unix shells:

bash
export MINDMAP_MCTS_SKILL_DIR="${CODEX_HOME:-$HOME/.codex}/skills/mindmap-mcts"

Claude Code on Unix shells:

bash
export MINDMAP_MCTS_SKILL_DIR="${CLAUDE_HOME:-$HOME/.claude}/skills/mindmap-mcts"

Default install locations are $HOME/.codex/skills/mindmap-mcts for Codex and $HOME/.claude/skills/mindmap-mcts for Claude Code.

Linux, macOS, WSL, or Git Bash:

bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" --help

Cross-platform Python launcher:

bash
python "$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap.py" --help

Windows PowerShell:

powershell
# Claude Code:
$env:MINDMAP_MCTS_SKILL_DIR = "$env:USERPROFILE\.claude\skills\mindmap-mcts"

# Codex:
# $env:MINDMAP_MCTS_SKILL_DIR = "$env:USERPROFILE\.codex\skills\mindmap-mcts"

& "$env:MINDMAP_MCTS_SKILL_DIR\scripts\mindmap.ps1" --help

If PowerShell execution policy blocks .ps1 scripts, run the Python module directly after setting PYTHONPATH:

powershell
$env:PYTHONPATH = "$env:MINDMAP_MCTS_SKILL_DIR\scripts;$env:PYTHONPATH"
$env:PYTHONIOENCODING = "utf-8"
[Console]::OutputEncoding = [System.Text.Encoding]::UTF8
python -m mindmap_mcts.cli --help

For repeated commands in one shell session, define:

bash
alias mindmap='"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap"'

If aliases are not preserved by the current shell call, use the explicit launcher form for the current shell.

On Windows, set UTF-8 output before rendering or showing trees that contain Chinese text:

powershell
$env:PYTHONIOENCODING = "utf-8"
[Console]::OutputEncoding = [System.Text.Encoding]::UTF8

Never hand-edit rendered markdown as the truth source. The .tree.json file is the truth source; .tree.md is only a view.

Startup

  1. Decide whether this skill applies. Skip it for one-step commands, obvious one-file edits, or direct factual lookups.
  2. If no tree exists, create one:
bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" init --title "<task title>" --out <task-name>.tree.json

The root node created by init is n1; use --parent n1 for the first child. Do not use root unless a specific tree file already contains a node with that id.

  1. If a tree exists, inspect it:
bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" show <task-name>.tree.json
  1. Render the readable view:
bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" render <task-name>.tree.json --out <task-name>.tree.md
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" render-markmap <task-name>.tree.json --out <task-name>.markmap.html

Use render-markmap when the user wants a browser-based visual tree. It produces interactive HTML through Markmap's browser autoloader. The map starts with an Exploration status branch that shows best path, selected frontier, state counts, open frontier nodes, verified nodes, and pruned nodes. In the reasoning tree, completed exploration uses a green ✓, partial exploration uses a yellow ◐, and unopened frontier nodes use a gray ○; no red cross is used. Node titles are bold black, and status stays immediately after the title, for example **n11 事实性与幻觉** (V=0.90 N=1 verified). Use render-html only when a static offline fallback is preferable.

Per-Round Loop

  1. Ask the CLI for the next recommended action:
bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" next <task-name>.tree.json

Use this to orient before making new changes to the tree.

  1. Select the next frontier directly when you need only the node id:
bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" select <task-name>.tree.json
  1. Expand the selected node with 2 or 3 child nodes. Children must be mutually exclusive, concrete, and verifiable.
bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" add <task-name>.tree.json --parent <node-id> --type hypothesis --content "<specific hypothesis>"
  1. Evaluate each new child using the cheapest real probe available: read code, grep a symbol, run a focused test, inspect a log, or check a config.

  2. Record value and evidence:

bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" eval <task-name>.tree.json --id <node-id> --value <0-to-1> --evidence "<short evidence>" --probe-type <test|grep|log|paper|code-read|user-input> --source "<source pointer>" --confidence <low|medium|high>

Use --probe-type, --source, and --confidence when a score is backed by a concrete probe. Omit them only when there is no useful structured metadata.

  1. Prune disproven branches instead of deleting them:
bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" prune <task-name>.tree.json --id <node-id> --evidence "<why this branch is ruled out>" --probe-type <test|grep|log|paper|code-read|user-input> --source "<source pointer>" --confidence <low|medium|high>
  1. Backpropagate from evaluated nodes:
bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" backprop <task-name>.tree.json --from <node-id>
  1. Render and show the updated state:
bash
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" render <task-name>.tree.json --out <task-name>.tree.md
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" render-html <task-name>.tree.json --out <task-name>.tree.html
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" render-markmap <task-name>.tree.json --out <task-name>.markmap.html
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" show <task-name>.tree.json
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" path <task-name>.tree.json
"$MINDMAP_MCTS_SKILL_DIR/scripts/mindmap" doctor <task-name>.tree.json
Show full SKILL.md (213 more words)Show less

Evaluation Scale

  • 0.90 to 1.00: real evidence verifies this path reaches the target.
  • 0.60 to 0.80: strong signal, still needs confirmation.
  • 0.30 to 0.50: uncertain or neutral signal.
  • 0.10 to 0.20: evidence mostly rules this out.
  • 0.00: confirmed dead branch; prune it.

Prefer real probes over model self-estimates. Evidence such as focused tests, logs, source reads, grep results, or config checks should be recorded directly on the node.

Stop Conditions

Stop exploring and write an execution plan when one of these is true:

  • A root-to-leaf path has value at least 0.85 and key nodes have real evidence.
  • The iteration budget is exhausted.
  • Several paths remain close in value and no cheap probe can separate them.

When multiple paths remain close, stop and ask the user to choose. Show the best path, close alternatives, and evidence for each.

Reporting

When reporting progress, include:

  • Current selected frontier.
  • New child nodes and why they are distinct.
  • Probe evidence for each evaluated node.
  • Pruned branches and why they should not be retried.
  • Updated best path.
  • doctor output if the tree was edited by hand or imported from another run.

Forward Testing

This repository includes evaluation prompts in evals/evals.json. Use them when checking whether the skill actually improves agent behavior on debugging, architecture tradeoff, and research synthesis tasks.

© cheshireyang, 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 10 other files (scripts) in mindmap-mcts of cheshireyang/mindmap-mcts-skill.

  • SKILL.md
  • agents/openai.yaml
  • scripts/mindmap
  • scripts/mindmap.cmd
  • scripts/mindmap.ps1
  • scripts/mindmap.py
  • scripts/mindmap_mcts/__init__.py
  • scripts/mindmap_mcts/cli.py
  • scripts/mindmap_mcts/engine.py
  • scripts/mindmap_mcts/model.py
  • scripts/mindmap_mcts/render.py

Open the folder on GitHubat commit 74ba5a5

Compare with similar skills

Mindmap Mcts next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Mindmap Mcts compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Mindmap Mcts this skillcheshireyang/mindmap-mcts-skill110—~1.9kAutomated safety check: PassMIT
Trellis Session Insightmindfold-ai/Trellis15k4 repos~1.7kAutomated safety check: PassAGPL-3.0
Native Data FetchingCherryHQ/cherry-studio-app4k6 repos~2.9kAutomated safety check: NotesMIT
Debugging Executionsn8n-io/n8n207k—~2.6kAutomated safety check: PassCustom licence
Aoti Debugpytorch/pytorch104k1 repos~1.7kAutomated safety check: PassCustom licence
Herdr Throwaway Reproductionherdrdev/herdr43k—~2.4kAutomated safety check: PassApache-2.0

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Categories

Questions about Mindmap Mcts

What does Mindmap Mcts do?

Run a visible reasoning-tree workflow with lightweight MCTS for complex Codex or Claude Code tasks. Mindmap Mcts is an agent skill from cheshireyang/mindmap-mcts-skill. Run a visible reasoning-tree workflow with lightweight MCTS for complex Codex or Claude Code tasks.

When should I use Mindmap Mcts?

Mindmap Mcts fits situations like: A task has multiple plausible hypotheses; needs systematic debugging; requires option tradeoff exploration; involves repeated trial-and-error.

How do I install Mindmap Mcts in Claude Code?

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

How do I install Mindmap Mcts in Codex?

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

Can I use Mindmap Mcts 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 cheshireyang/mindmap-mcts-skill --skill mindmap-mcts -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-mcts, .gemini/skills/mindmap-mcts, .github/skills/mindmap-mcts and .opencode/skills/mindmap-mcts in your project.

What does Mindmap Mcts need to run?

Going by SKILL.md and its folder, Mindmap Mcts needs Python, Windows cmd and PowerShell for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3; PowerShell.

Does Mindmap Mcts access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Mindmap Mcts 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 Mcts use?

Mindmap Mcts 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 Mcts use?

About 1.9k tokens (SKILL.md is roughly 7.8k 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 Mcts?

Skills that share tags, products or a category with Mindmap Mcts: Trellis Session Insight (mindfold-ai/Trellis, 15k stars), Native Data Fetching (CherryHQ/cherry-studio-app, 4k stars), Debugging Executions (n8n-io/n8n, 207k stars) and Aoti Debug (pytorch/pytorch, 104k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mindmap Mcts?

cheshireyang (a GitHub user) maintains it in cheshireyang/mindmap-mcts-skill, which has 110 GitHub stars. The repository was last updated on June 24, 2026.

Source: cheshireyang/mindmap-mcts-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.