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Cell Free Expression

Checked
adaptyvbio·vlatest·backend·MIT

Cell Free Expression enables rapid protein production from DNA templates, accelerating research and development by bypassing traditional cell culture.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add adaptyvbio/protein-design-skills --skill cell-free-expression -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

adaptyvbio

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.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

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

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned adaptyvbio/protein-design-skills at 59dd633743.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
59dd633743
Manifest
skills/cell-free-expression/SKILL.md
Scanned
1 file, 290 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill allows AI agents to simulate and predict outcomes in cell-free protein expression (CFE) systems. It can estimate protein yield based on input parameters, optimize reaction conditions for improved production, and troubleshoot potential issues that arise during CFE experiments. The skill leverages established biochemical principles to provide realistic simulations.

When to use it

  • Predicting Yield: Before running a physical CFE experiment, use the skill to predict expected protein yields based on gene sequence, cell lysate concentration, and other factors.
  • Optimizing Conditions: Explore different combinations of reaction parameters (e.g., temperature, incubation time) to identify conditions that maximize protein production.
  • Troubleshooting Problems: If a CFE experiment fails or produces low yield, use the skill to analyze potential bottlenecks and suggest corrective actions.
  • Experimental Design: Plan efficient CFE experiments by simulating various scenarios and prioritizing conditions with the highest likelihood of success.

Key capabilities

  • Yield prediction based on gene sequence and lysate concentration
  • Reaction condition optimization
  • Troubleshooting assistance for low yield or failed reactions
  • Simulation of biochemical processes within a CFE system

Example prompts

  • "Predict the protein yield for this gene sequence [insert sequence] using 10% E. coli lysate."
  • "What temperature and incubation time would maximize protein production given these parameters: [list parameters]?"
  • "My CFE reaction produced very little protein. What are some possible reasons, and how can I fix it?"

Tips & gotchas

The accuracy of the simulation depends on the quality of input data; ensure gene sequences and lysate concentrations are accurate for best results. The skill is a predictive tool and should be validated with physical experiments.