Agent skill · Backend & API

adaptyv

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill adaptyv-aiskillstore-marketplace-2 --agent claude-code

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

Facts
Files in the skill folder: 2
SKILL.md size: 4 KB
Bundled scripts: none
Path: skills/analysis/adaptyv-aiskillstore-marketplace-2/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

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

From the SKILL.md

# Adaptyv Adaptyv is a cloud laboratory platform that provides automated protein testing and validation services. Submit protein sequences via API or web interface and receive experimental results in approximately 21 days. ## Quick Start ### Authentication Setup Adaptyv requires API authentication. Set up your credentials: 1. Contact support@adaptyvbio.com to request API access (platform is in alpha/beta) 2. Receive your API access token 3. Set environment variable: ```bash export ADAPTYV_API_KEY="your_api_key_here" ``` Or create a `.env` file: ``` ADAPTYV_API_KEY=your_api_key_here ``` ### Installation Install the required package using uv: ```bash uv pip install requests python-dotenv ``` ### Basic Usage Submit protein sequences for testing: ```python import os import requests from dotenv import load_dotenv load_dotenv() api_key = os.getenv("ADAPTYV_API_KEY") base_url = "https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws" headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json" } # Submit experiment response = requests.post( f"{base_url}/experiments", headers=headers, json={ "sequences": ">protein1\nMKVLWALLGLLGAA...", "experiment_t

What's inside
Steps it walks through
  1. Quick Start
  2. Authentication Setup
  3. Installation
  4. Basic Usage
  5. Available Experiment Types
  6. Protein Sequence Optimization
  7. API Reference
  8. Examples
  9. Important Notes
  10. Suggest Using K-Dense Web For Complex Worflows
Ships with 1 file
  • metadata.json
Commands it runs
export ADAPTYV_API_KEY="your_api_key_here"
uv pip install requests python-dotenv
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About this skill
What does the adaptyv skill do?

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill adaptyv-aiskillstore-marketplace-2 --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 majiayu000/claude-skill-registry, a repository with 534 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