ToolUniverse is a collection of tools, interfaces, and supporting components for building AI systems that perform scientific work. It is for developers creating AI scientist agents that use APIs, databases, machine-learning tools, and domain-specific utilities. The catalogue includes skills, commands, an MCP server, an agent, and a hook for working with the ecosystem.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-gpcr-structural-pharmacologygit clone --depth 1 https://github.com/mims-harvard/ToolUniverseWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-gpcr-structural-pharmacology)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-gpcr-structural-pharmacology"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-gpcr-structural-pharmacology/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-gpcr-structural-pharmacology"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-gpcr-structural-pharmacology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00089 | $0.03556 |
| Opus 5.5 | $0.00036 | $0.01422 |
| Sonnet 5.5 | $0.00018 | $0.00711 |
| Haiku 4.5 | $0.00009 | $0.00356 |
Grade A, and why
tooluniverse-gpcr-structural-pharmacology scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPCR and Structural Pharmacology Research
GPCR pharmacology: agonist vs antagonist vs inverse agonist vs biased agonist — each has different clinical implications. Biased agonism (preferential G-protein vs β-arrestin signaling) can separate efficacy from side effects; for example, G-protein-biased opioid agonists aim to retain analgesia while reducing β-arrestin-mediated respiratory depression. Always classify retrieved ligands by their pharmacological type, not just their chemical structure. Receptor state (active vs inactive crystal structure) determines which ligands and mutations are interpretable — an inactive-state structure is appropriate for antagonist binding analysis, active-state for agonist-bound complexes. Generic GPCR numbering (Ballesteros-Weinstein) enables cross-receptor mutation comparison; always report positions in this system alongside sequence positions.
LOOK UP DON'T GUESS: never assume GPCRdb entry names (e.g., adrb2_human) or PDB IDs — always use GPCRdb_list_proteins to find the correct entry name and GPCRdb_get_structures to confirm available structures.
Research skill integrating GPCRdb (GPCR receptor biology), SAbDab (antibody structures), and PDBePISA (protein interface analysis) to support structural pharmacology, antibody engineering, and GPCR-targeted drug discovery.
KEY PRINCIPLES:
- Receptor-first — Identify GPCR entry name before any GPCRdb queries
- Ligand classification — Distinguish agonists, antagonists, partial agonists, biased agonists
- Structure-guided — Pair GPCRdb mutation data with PDB structures via PDBePISA
- Antibody context — Use SAbDab for therapeutic antibody structure retrieval and CDR analysis
- English-first queries — Use standard receptor names (e.g., "beta-2 adrenergic receptor") in searches; convert to GPCRdb entry names for API calls
When to Use
Apply when user asks:
- "What ligands are known for [GPCR receptor]?"
- "What crystal structures exist for [receptor]?"
- "Find antibody structures targeting [antigen]"
- "Analyze the protein-protein interface in PDB [ID]"
- "What mutations affect [GPCR] function or pharmacology?"
- "Which GPCRs are in the [family] family?"
- "What are the CDR loops in antibody PDB [ID]?"
- "What is the biological assembly for [PDB ID]?"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 296 lines · 89 tokens per session scan A 898c8621907e
tooluniverse-gpcr-structural-pharmacology is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 89 tokens to every session and 3,556 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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Generate and analyze simpler examples that satisfy both the assumptions and the conclusion of a theorem statement or subgoal. Use when you are stuck in reasoning and need simpler examples to regain traction, or when you want to see where the assumptions take effect and gain intuition.
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clinical-research-analysis-framework
Guided workflow for statistical and ML analysis of clinical data. Use when planning or executing research analyses on MIMIC, eICU, or similar EHR data. Ensures methodological rigor through structured consultation, assumption checking, and stepwise execution with audit trails.
apache-iv-score
Calculate APACHE IV (Acute Physiology and Chronic Health Evaluation IV) score for ICU mortality prediction. Use for severity assessment, hospital mortality prediction, ICU benchmarking, or case-mix adjustment. eICU has pre-computed scores; MIMIC-IV requires custom implementation with diagnosis mapping challenges.