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Agent skill

Bonsai MCP

by autonomous-ai in autonomous-ai/openharness

Create and inspect architecture projects in the Bonsai MCP Harness workspace, including its local starter and optional upstream integration.

MITAuto-check passedAgent Workflows

Install Bonsai MCP

skills CLI
$ npx skills add autonomous-ai/openharness --skill bonsai-mcp -a claude-code

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

GitHub CLI
$ gh skill install autonomous-ai/openharness bonsai-mcp --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/autonomous-ai/openharness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/store/agents/bonsai-mcp/skills/bonsai-mcp .claude/skills/bonsai-mcp && 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
bonsai-mcp
GitHub stars
1.2k
Token cost
~508 tokens
SKILL.md length
243 words
Files
1
Skills in repo
100
Repo updated
First seen
Licence
MIT

At a glance

Create and inspect architecture projects in the Bonsai MCP Harness workspace, including its local starter and optional upstream integration.

  • Tasks that involve MCP servers
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Bonsai MCP is an agent skill from autonomous-ai/openharness. Create and inspect architecture projects in the Bonsai MCP Harness workspace, including its local starter and optional upstream integration.

Its SKILL.md is about 510 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across… The licence is MIT.

When your agent uses it

  • Tasks that involve MCP servers

Example prompts

  • “/bonsai-mcp”

What it can do on your machine

Read from SKILL.md and the folder at commit cc4983e. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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

Bonsai MCP loads about 508 tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 243 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~508

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from autonomous-ai/openharness at commit cc4983e, republished under its MIT licence (© autonomous-ai). 243 words, ~508 tokens.

Download SKILL.mdSave it as .claude/skills/bonsai-mcp/SKILL.md (or your agent's skills folder).
name
bonsai-mcp
description
Create and inspect architecture projects in the Bonsai MCP Harness workspace, including its local starter and optional upstream integration.

House of ideas

Read studio.json to understand the current controls; "$STUDIO_TOOLCHAIN/../studio.config.json" describes their ranges. Run "$STUDIO_TOOLCHAIN/run.sh" build to make a new result. Successful artifacts and their measurements are in out/runs/<id>/; out/latest.json names the current result. A failed run preserves the last success and records the error in the verdict.

The local workflow creates and reopens a real IFC4 building with IfcOpenShell, including storeys, slabs, walls, spaces and quantities. The in-pane drawing is a schematic model inspector. It does not certify structural adequacy or code compliance. Blender/Bonsai editing remains available through the pinned optional bridge.

Use "$STUDIO_TOOLCHAIN/../README.md" for the integration contract and commands. Read the relevant files under $STUDIO_UPSTREAM before using an upstream API. Keep controls within their documented ranges, preserve the data needed to reproduce a comparison, and distinguish preview results from native service or hardware output. The viewer supports history and artifact downloads; tell the user which run contains the result, and what was actually measured.

Author and check building data

Choose the footprint, storeys, height, and room use, then run build. Reopen building.ifc with IfcOpenShell, inspect spaces/walls/openings, and read the quantity sets. Match their areas and volumes to quantities.csv. The canvas is a schematic view; IFC is the source artifact for downstream editing. Preserve element identifiers when extending an existing model.

Read $STUDIO_UPSTREAM/docs/examples.md for Bonsai edits and read-back verification, docs/installation.md for the bridge, and docs/tools.md before native commands. The wrapper's bonsai action only requests scene information from an existing Blender session.

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

Files

Just SKILL.md in store/agents/bonsai-mcp/skills/bonsai-mcp of autonomous-ai/openharness.

Open the folder on GitHubat commit cc4983e

Compare with similar skills

Bonsai MCP 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.

Bonsai MCP compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Bonsai MCP this skillautonomous-ai/openharness1.2k—~508Automated safety check: PassMIT
MCP Server Builderanthropics/skills180k63 repos~2.3kAutomated safety check: PassApache-2.0
MCP Server BuildershareAI-lab/learn-claude-code78k4 repos~1.2kAutomated safety check: PassMIT
MCP Integration for Pluginsanthropics/claude-plugins-official38k11 repos~3.1kAutomated safety check: PassApache-2.0
Crush Configurationcharmbracelet/crush29k—~3.7kAutomated safety check: PassCustom licence
Context Mode Output Sandboxmksglu/context-mode26k—~4.1kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Bonsai MCP

What does Bonsai MCP do?

Create and inspect architecture projects in the Bonsai MCP Harness workspace, including its local starter and optional upstream integration. Bonsai MCP is an agent skill from autonomous-ai/openharness. Create and inspect architecture projects in the Bonsai MCP Harness workspace, including its local starter and optional upstream integration.

When should I use Bonsai MCP?

Bonsai MCP fits situations like: tasks that involve MCP servers.

How do I install Bonsai MCP in Claude Code?

Run `npx skills add autonomous-ai/openharness --skill bonsai-mcp -a claude-code`. Or copy the skill folder (store/agents/bonsai-mcp/skills/bonsai-mcp in autonomous-ai/openharness) into .claude/skills/bonsai-mcp in your project. Claude Code loads it when a task matches its description.

How do I install Bonsai MCP in Codex?

Run `npx skills add autonomous-ai/openharness --skill bonsai-mcp -a codex`. Or copy the skill folder (store/agents/bonsai-mcp/skills/bonsai-mcp in autonomous-ai/openharness) into .agents/skills/bonsai-mcp in your project. Codex loads it when a task matches its description.

Can I use Bonsai MCP 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 autonomous-ai/openharness --skill bonsai-mcp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bonsai-mcp, .gemini/skills/bonsai-mcp, .github/skills/bonsai-mcp and .opencode/skills/bonsai-mcp in your project.

What does Bonsai MCP need to run?

SKILL.md names no scripts, command-line tools or credentials: Bonsai MCP is instructions for the agent only.

Does Bonsai MCP 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 Bonsai MCP 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. Review the folder before installing.

What licence does Bonsai MCP use?

Bonsai MCP 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 Bonsai MCP use?

About 508 tokens (SKILL.md is roughly 2k 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 Bonsai MCP?

Skills that share tags, products or a category with Bonsai MCP: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Crush Configuration (charmbracelet/crush, 29k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bonsai MCP?

autonomous-ai (a GitHub organization) maintains it in autonomous-ai/openharness, which has 1,210 GitHub stars. The repository holds 100 skills in this directory. The repository was last updated on October 10, 2026.

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