mims-harvard/tooluniverse-cs-setup
skills/tooluniverse-cs-setup/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add mims-harvard/tooluniverse tooluniverse-cs-setupInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- tooluniverse-cs-setup
- description:
- 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):
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 viaTOOLUNIVERSE_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
tooluniverseenv. - 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*-plugininstaller 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.
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