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.

1,802 stars

Best use case

adaptyv is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using adaptyv should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/adaptyv/SKILL.md --create-dirs "https://raw.githubusercontent.com/FreedomIntelligence/OpenClaw-Medical-Skills/main/skills/adaptyv/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/adaptyv/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How adaptyv Compares

Feature / AgentadaptyvStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this 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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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_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 support@adaptyvbio.com for access requests or questions
- Suitable for high-throughput AI-driven protein design workflows

Related Skills

zinc-database

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.

zarr-python

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

xlsx

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

writing-skills

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Use when creating new skills, editing existing skills, or verifying skills work before deployment

writing-plans

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Use when you have a spec or requirements for a multi-step task, before touching code

wikipedia-search

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Search and fetch structured content from Wikipedia using the MediaWiki API for reliable, encyclopedic information

wellally-tech

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Integrate digital health data sources (Apple Health, Fitbit, Oura Ring) and connect to WellAlly.tech knowledge base. Import external health device data, standardize to local format, and recommend relevant WellAlly.tech knowledge base articles based on health data. Support generic CSV/JSON import, provide intelligent article recommendations, and help users better manage personal health data.

weightloss-analyzer

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

分析减肥数据、计算代谢率、追踪能量缺口、管理减肥阶段

<!--

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

# COPYRIGHT NOTICE

verification-before-completion

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

vcf-annotator

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

vaex

1802
from FreedomIntelligence/OpenClaw-Medical-Skills

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.