adaptyv
A cloud laboratory platform for automated protein design and experimental validation.
Install
mkdir -p .claude/skills/adaptyv && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2203" && unzip -o skill.zip -d .claude/skills/adaptyv && rm skill.zipInstalls to .claude/skills/adaptyv
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Authenticate with cloud lab API endpoints
- →Batch submit protein sequences for wet-lab assay
- →Monitor progress of experimental workflows
- →Retrieve results from thermostability and binding assays
How it works
Constructs and executes authenticated API calls to a cloud laboratory platform to initiate physical lab testing of sequences.
Inputs & outputs
When to use adaptyv
- →Submit protein sequences for expression testing
- →Optimize protein sequences using computational tools
- →Track experiment status via API
- →Download experimental results for further analysis
About this skill
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:
- Contact [email protected] to request API access (platform is in alpha/beta)
- Receive your API access token
- Set environment variable:
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:
uv pip install requests python-dotenv
Basic Usage
Submit protein sequences for testing:
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_type": "binding",
"webhook_url": "https://your-webhook.com/callback"
}
)
experiment_id = response.json()["experiment_id"]
Available Experiment Types
Adaptyv supports multiple assay types:
- Binding assays - Test protein-target interactions using biolayer interferometry
- Expression testing - Measure protein expression levels
- Thermostability - Characterize protein thermal stability
- Enzyme activity - Assess enzymatic function
See reference/experiments.md for detailed information on each experiment type and workflows.
Protein Sequence Optimization
Before submitting sequences, optimize them for better expression and stability:
Common issues to address:
- Unpaired cysteines that create unwanted disulfides
- Excessive hydrophobic regions causing aggregation
- Poor solubility predictions
Recommended tools:
- NetSolP / SoluProt - Initial solubility filtering
- SolubleMPNN - Sequence redesign for improved solubility
- ESM - Sequence likelihood scoring
- ipTM - Interface stability assessment
- pSAE - Hydrophobic exposure quantification
See reference/protein_optimization.md for detailed optimization workflows and tool usage.
API Reference
For complete API documentation including all endpoints, request/response formats, and authentication details, see reference/api_reference.md.
Examples
For concrete code examples covering common use cases (experiment submission, status tracking, result retrieval, batch processing), see reference/examples.md.
Important Notes
- Platform is currently in alpha/beta phase with features subject to change
- Not all platform features are available via API yet
- Results typically delivered in ~21 days
- Contact [email protected] for access requests or questions
- Suitable for high-throughput AI-driven protein design workflows
When not to use it
- →When performing purely in-silico protein folding
- →When experiment results are not required
Prerequisites
Limitations
- →Requires active API subscription
- →Multi-week latency for physical results
How it compares
Bridges computational design directly to physical wet-lab results instead of relying solely on simulated models.
Compared to similar skills
adaptyv side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| adaptyv (this skill) | 7 | 7mo | Caution | Advanced |
| firecrawl-scrape | 5 | 7mo | Review | Beginner |
| youtube-collector | 1 | 8mo | Review | Intermediate |
| proof-theory | 1 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by davila7
View all by davila7 →You might also like
firecrawl-scrape
parcadei
Scrape web pages and extract content via Firecrawl MCP
youtube-collector
greatSumini
유튜브 채널을 등록하고 새 컨텐츠를 수집하여 자막 기반 요약을 생성하는 skill. 사용자가 (1) 유튜브 채널 등록/관리를 요청하거나, (2) 등록된 채널의 새 영상 수집을 요청하거나, (3) 유튜브 영상 요약을 요청할 때 사용. 데이터는 .reference/ 폴더에 YAML 형식으로 저장됨.
proof-theory
parcadei
Problem-solving strategies for proof theory in mathematical logic
autoqec-run
qualit527
Run the AutoQEC research loop on a given env YAML. Orchestrates the autoqec-ideator / autoqec-coder / autoqec-analyst subagents via the Agent tool, invokes the Runner CLI for training + evaluation, and writes history.jsonl + pareto.json. Use when the user asks "run AutoQEC on <env>", "start a resear
zlibrary-to-notebooklm
zstmfhy
自动从 Z-Library 下载书籍并上传到 Google NotebookLM。支持 PDF/EPUB 格式,自动转换,一键创建知识库。
tooluniverse-sdk
mims-harvard
Build AI scientist systems using ToolUniverse Python SDK for scientific research. Use when users need to access 1000++ scientific tools through Python code, create scientific workflows, perform drug discovery, protein analysis, genomics analysis, literature research, or any computational biology task. Triggers include requests to use scientific tools programmatically, build research pipelines, analyze biological data, search literature, predict drug properties, or create AI-powered scientific workflows.