How do I install the Tooluniverse Cs Setup skill?

Run npx skillmds@latest add mims-harvard/tooluniverse-cs-setup in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Tooluniverse Cs Setup safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Tooluniverse Cs Setup?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Tooluniverse Cs Setup free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Tooluniverse Cs Setup?

mims-harvard (@mims-harvard) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Tooluniverse Cs Setup by mims-harvard

Name: Tooluniverse Cs Setup
Rating: 4.5 (4 reviews)
Author: mims-harvard

Tooluniverse Cs Setup

Install or update ToolUniverse in Claude Science — create the conda env, install the tooluniverse pip package, and (re)build the tooluniverse-research skill by fetching the current workflow library from GitHub. Use for first-time setup, upgrading the ToolUniverse version, refreshing the bundled workflows after an upstream release, or reinstalling on a new machine.

mims-harvard e6ede66 4 files · 13.7 KB Updated Sep 9, 2026

File contents

Set up ToolUniverse for Claude Science

The upstream ToolUniverse ships a Claude Code plugin (MCP server + uvx + slash commands). Claude Science loads capabilities differently, so this skill installs the equivalent natively: the tooluniverse pip package supplies the 2500+ tools, and the workflow library is packaged into a single dynamically-loaded skill, tooluniverse-research. No uv, no MCP server, no plugin marketplace.

Loading this skill defines tu_build_research_bundle() in the kernel (run cells in the tooluniverse conda env).

Full install / update — four steps

1. Create the conda env (skip if it already exists):

manage_environments(mode="create", name="tooluniverse", python_version="3.11", packages=["pip"])

2. Install (or upgrade) the tools — the pip package is the tool layer:

manage_packages(mode="install", environment="tooluniverse", packages=["tooluniverse"], use_pip=True)

Pin a version for reproducibility with ["tooluniverse==1.3.0"].

3. Stage the workflow bundle — fetch the current repo and rebuild the file tree (run in a python cell, env tooluniverse):

res = tu_build_research_bundle(staging="./tu_staging")
res  # {out_dir, n_workflows, n_files, dropped, files_head}

This downloads the repo tarball, parses every tooluniverse-* workflow (dropping the plugin/installer entries), and writes ./tu_staging/out/ = SKILL.md, kernel.py, index.json, workflows/*.md.

4. Publish the skill — push the staged tree into the catalog (run in the repl tool; host.skills.* lives there, not in python):

import os
SKILL = "tooluniverse-research"
out = os.path.abspath("./tu_staging/out")
if any(s["name"] == SKILL for s in host.skills.list()):
    host.skills.delete(SKILL)                       # clean rebuild
for root, _d, fs in os.walk(out):
    for f in fs:
        p = os.path.join(root, f)
        rel = os.path.relpath(p, out)
        host.skills.edit(SKILL, rel, open(p, encoding="utf-8").read())
print(host.skills.publish(SKILL, overwrite=True))

(host.skills.publish refuses if kernel.py fails the sidecar gate — the edit result carries the verdict.)

Verify

skill("tooluniverse-research")                      # loads router + injects helpers
tu = get_tu()
tu.run({"name": "PubChem_get_CID_by_compound_name", "arguments": {"name": "metformin"}})
# -> {'status': 'success', 'data': {'IdentifierList': {'CID': [4091]}}}

Notes

Sandbox cache: ToolUniverse defaults its cache to ~/.tooluniverse, which is read-only here; get_tu() redirects it to the workspace via TOOLUNIVERSE_CACHE_DIR. Nothing to configure.
API keys (optional): most tools work without them. For NCBI / OncoKB / NVIDIA etc., add keys under Customize → Credentials, then expose them in the tooluniverse env.
What is NOT ported: the plugin's slash commands (/tooluniverse:research) and MCP server — replaced by natural-language routing (search_skills → find_tu_workflow). The two *-plugin installer docs are dropped as non-research entries.
Updating: rerun steps 2–4. Step 2 upgrades the tools; steps 3–4 refresh the workflow library from the latest GitHub state.

---name: tooluniverse-cs-setupdescription: Install or update ToolUniverse in Claude Science — create the conda env, install the tooluniverse pip package, and (re)build the tooluniverse-research skill by fetching the current workflow library from GitHub. Use for first-time setup, upgrading the ToolUniverse version, refreshing the bundled workflows after an upstream release, or reinstalling on a new machine.--- # Set up ToolUniverse for Claude Science The upstream ToolUniverse ships a Claude **Code** plugin (MCP server + `uvx` + slash commands). Claude **Science** loads capabilities differently, so this skill installs the equivalent natively: the `tooluniverse` **pip package** supplies the 2500+ tools, and the workflow library is packaged into a single dynamically-loaded skill, `tooluniverse-research`. No `uv`, no MCP server, no plugin marketplace. Loading this skill defines `tu_build_research_bundle()` in the kernel (run cells in the **`tooluniverse`** conda env). ## Full install / update — four steps **1. Create the conda env** (skip if it already exists): ``` manage_environments(mode="create", name="tooluniverse", python_version="3.11", packages=["pip"]) ``` **2. Install (or upgrade) the tools** — the pip package is the tool layer: ``` manage_packages(mode="install", environment="tooluniverse", packages=["tooluniverse"], use_pip=True) ``` Pin a version for reproducibility with `["tooluniverse==1.3.0"]`. **3. Stage the workflow bundle** — fetch the current repo and rebuild the file tree (run in a `python` cell, env `tooluniverse`): ```python res = tu_build_research_bundle(staging="./tu_staging") res # {out_dir, n_workflows, n_files, dropped, files_head} ``` This downloads the repo tarball, parses every `tooluniverse-*` workflow (dropping the plugin/installer entries), and writes `./tu_staging/out/` = `SKILL.md`, `kernel.py`, `index.json`, `workflows/*.md`. **4. Publish the skill** — push the staged tree into the catalog (run in the **`repl`** tool; `host.skills.*` lives there, not in `python`): ```python import os SKILL = "tooluniverse-research" out = os.path.abspath("./tu_staging/out") if any(s["name"] == SKILL for s in host.skills.list()): host.skills.delete(SKILL) # clean rebuild for root, _d, fs in os.walk(out): for f in fs: p = os.path.join(root, f) rel = os.path.relpath(p, out) host.skills.edit(SKILL, rel, open(p, encoding="utf-8").read()) print(host.skills.publish(SKILL, overwrite=True)) ``` (`host.skills.publish` refuses if `kernel.py` fails the sidecar gate — the `edit` result carries the verdict.) ## Verify ```python skill("tooluniverse-research") # loads router + injects helpers tu = get_tu() tu.run({"name": "PubChem_get_CID_by_compound_name", "arguments": {"name": "metformin"}}) # -> {'status': 'success', 'data': {'IdentifierList': {'CID': [4091]}}} ``` ## Notes - **Sandbox cache**: ToolUniverse defaults its cache to `~/.tooluniverse`, which is read-only here; `get_tu()` redirects it to the workspace via `TOOLUNIVERSE_CACHE_DIR`. Nothing to configure. - **API keys** (optional): most tools work without them. For NCBI / OncoKB / NVIDIA etc., add keys under Customize → Credentials, then expose them in the `tooluniverse` env. - **What is NOT ported**: the plugin's slash commands (`/tooluniverse:research`) and MCP server — replaced by natural-language routing (`search_skills` → `find_tu_workflow`). The two `*-plugin` installer docs are dropped as non-research entries. - **Updating**: rerun steps 2–4. Step 2 upgrades the tools; steps 3–4 refresh the workflow library from the latest GitHub state.

Frequently asked questions