Tooluniverse Protein Therapeutic Design
CheckedDesigns novel protein therapeutics targeting specified diseases using a vast database of molecular information and predictive modeling.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add mims-harvard/tooluniverse --skill tooluniverse-protein-therapeutic-design -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
mims-harvard
Taken from the source listing. We haven't verified who controls this account.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
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No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned mims-harvard/tooluniverse at f5c37eebeb.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- f5c37eebeb
- Scanned
- 18 files, 4,236 lines
- Copies in repo
- 3 — all of them scanned
About this skill
What it does
This skill enables AI agents to design protein therapeutics by leveraging advanced computational models for structure prediction and molecular optimization. It facilitates the discovery of novel therapeutic candidates through automated simulation and analysis of protein interactions.
When to use it
- Accelerating early-stage drug discovery pipelines for specific disease targets.
- Generating candidate protein sequences with desired binding affinities or stability profiles.
- Simulating how mutations affect protein folding and function before wet-lab validation.
- Integrating structural biology data into automated AI-driven research workflows.
Key capabilities
- Predicts 3D protein structures from amino acid sequences using deep learning models.
- Optimizes protein sequences for enhanced therapeutic properties like solubility and potency.
- Analyzes molecular dynamics to assess stability and interaction mechanisms.
- Supports iterative design cycles for refining therapeutic candidates.
Example prompts
- "Design a novel antibody fragment that binds with high affinity to the PD-L1 receptor."
- "Optimize this protein sequence for increased thermal stability while maintaining its catalytic activity."
- "Simulate the structural impact of introducing a point mutation at residue 45 in this kinase inhibitor."
Tips & gotchas
Ensure input sequences are accurate and validated, as errors can propagate through predictive models. While powerful, computational predictions should always be followed by experimental verification to confirm real-world efficacy.