Agent skill · Backend & API

adaptyv-bio

API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill adaptyv-bio --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 18 KB
Bundled scripts: none
Path: skills/sciagent/adaptyv-bio/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Adaptyv Bio ## Overview Adaptyv Bio is a protein expression and characterization platform accessed via a REST API and Python SDK. Users submit protein sequences (antibodies, nanobodies, enzymes, binding proteins) and receive expressed protein along with binding affinity measurements (KD via biolayer interferometry) within days. The platform is designed for high-throughput directed evolution loops: generate candidate sequences (computationally or by library design) → order expression + assay via API → receive affinity data → retrain model or select top candidates → repeat. The SDK handles experiment submission, status polling, and result retrieval in Python. ## When to Use - Screening computationally designed protein variants for experimental binding affinity validation - Running ML-guided directed evolution loops where in silico candidate generation alternates with wet-lab characterization - Ordering cell-free expression of nanobodies, antibodies, or binding domains without maintaining wet-lab infrastructure - Automating high-throughput protein characterization pipelines using the REST API - Integrating experimental affinity data (KD values) with computational models for Bayesian

What's inside
Steps it walks through
  1. Overview
  2. When to Use
  3. Prerequisites
  4. Quick Start
  5. Core API
  6. Module 1: Sequence Submission
  7. Module 2: Experiment Status Tracking
  8. Module 3: Results Retrieval
  9. Module 4: Experiment History and Project Management
  10. Module 5: Integration with Sequence Design
  11. Key Concepts
  12. KD (Dissociation Constant)
  13. Cell-Free Expression
  14. Common Workflows
Commands it runs
pip install adaptyvbio requests pandas
Set API key as environment variable
export ADAPTYV_API_KEY="your_api_key_here"
More from awesome-bio-agent-skills
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About this skill
What does the adaptyv-bio skill do?

API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill adaptyv-bio --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going