adaptyv
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval.
Skill by Masterofowls
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: design, backend
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What's inside
- Skill name
adaptyv- Description
- How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
- License
- MIT
- Compatibility
- Requires Python 3.10+, an Adaptyv Foundry account, and an API key from foundry.adaptyvbio.com. Install adaptyv-sdk from GitHub with uv pip install.
- Metadata
- version: 1.2 · skill-author: K-Dense Inc.
- Instructions
- 232 lines in SKILL.md · view on GitHub
Before installing
- Instructions call the network
Files (2)
- SKILL.md
- api-endpoints.md · references/
SKILL.md (first lines)
# Adaptyv Bio Foundry API
Adaptyv Bio is a cloud lab that turns protein sequences into experimental data. Users submit amino acid sequences via API or UI; Adaptyv's automated lab runs assays (binding, thermostability, expression, fluorescence) and delivers results in ~21 days.
**Official docs:** [docs.adaptyvbio.com/api-reference](https://docs.adaptyvbio.com/api-reference) · [llms.txt index](https://docs.adaptyvbio.com/llms.txt) · [OpenAPI spec](https://foundry-api-public.adaptyvbio.com/api/v1/openapi.json)
## Quick Start
**Base URL:** `https://foundry-api-public.adaptyvbio.com/api/v1`
**Authentication:** Bearer token in the `Authorization` header. Tokens are obtained from [foundry.adaptyvbio.com](https://foundry.adaptyvbio.com/) sidebar.
When writing code, always read the API key from the environment variable `ADAPTYV_API_KEY` or from a `.env` file — never hardcode tokens. Check for a `.env` file in the project root first; if one exists, use a library like `python-dotenv` to load it.
The [official API docs](https://docs.adaptyvbio.com/api-reference/api-introduction) use `FOUNDRY_API_TOKEN` in curl examples; that is the same bearer token — prefer `ADAPTYV_API_KEY` in Python and new shell scripts for consistency with the SDK.
```bash
export ADAPTYV_API_KEY="abs0_..."
curl https://foundry-api-public.adaptyvbio.com/api/v1/targets?limit=3 \
-H "Authorization: Bearer $ADAPTYV_API_KEY"
```
Every request except `GET /openapi.json` requires authentication. Store tokens in environment variables or `.env` files — never commit them to source control.
## Python SDK
**Version note:** `adaptyv-sdk` **0.1.0** (beta) is not yet on PyPI — install from GitHub:
```bash
uv pip install "git+https://github.com/adaptyvbio/adaptyv-sdk.git"
```
In a project with `pyproject.toml`:
```bash
uv add "adaptyv-sdk @ git+https://github.com/adaptyvbio/adaptyv-sdk.git"
```
**Environment variables** (set in shell or `.env` file):
```bash
ADAPTYV_API_KEY=your_api_key
ADAPTYV_API_URL=https://foundry-api-public.adaptyvbio.com/api/v1
ADAPTYV_ORGANIZATION_ID=your_org_id # optional
```
The `@lab.experiment` decorator and `FoundryClient` both read `ADAPTYV_API_KEY` and `ADAPTYV_API_URL` from the environment when not passed explicitly.
### Decorator Pattern
```python
from adaptyv import lab
@lab.experiment(target="PD-L1", experiment_type="screening", method="bli")
def design_binders():
return {"design_a": "MVKVGVNG...", "design_b": "MKVLVAG..."}
result = design_binders()
print(f"Experiment: {result.experiment_url}")
```
### Client Pattern
```python
import os
from adaptyv import FoundryClient
client = FoundryClient(
api_key=os.environ["ADAPTYV_API_KEY"],
base_url=os.environ.get(
"ADAPTYV_API_URL",
"https://foundry-api-public.adaptyvbio.com/api/v1",
),
)
# Browse targets
targets = client.targets.list(search="EGFR", selfservice_only=True)
# Estimate cost
estimate = client.experiments.cost_estimate({
"experiment_spec": {
"experiment_type": "screening",
"method": "bli",
"target_id": "target-uuid",
"sequences": {"seq1": "EVQLVESGGGLVQ..."},
"n_replicates": 3
}
})
# Create and submit
exp = client.experiments.create({...})
client.experiments.submit(exp.experiment_id)
# Later: retrieve results
results = client.experiments.get_results(exp.experiment_id)
```
## Experiment Types
| Type | Method | Measures | Requires Target |
|---|---|---|---|
| `affinity` | `bli` or `spr` | KD, kon, koff kinetics | Yes |
| `screening` | `bli` or `spr` | Yes/no binding | Yes |
| `thermostability` | — | Melting temperature (Tm) | No |
| `expression` | — | Expression yield | No |
| `fluorescence` | — | Fluorescence intensity | No |
## Experiment Lifecycle
```
Draft → WaitingForConfirmation → QuoteSent → WaitingForMaterials → InQueue → InProduction → DataAnalysis → InReview → Done
```
| Status | Who Acts | Description |
|---|---|---|
| `Draft` | You | Editable, no cost commitment |
| `WaitingForConfirmation` | Adaptyv | Under review, quote being prepared |
| `QuoteSent` | You | Review and confirm the quote |
| `WaitingForMaterials` | Adaptyv | Gene fragments and target ordered |From Masterofowls/skills, MIT.
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Signals
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- Open issues
- 0
- Last commit (this folder)
- Aug 4, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.2
- License
- MIT
- Repository
- github.com/Masterofowls/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
adaptyv skill. Agent Mart. https://agentmart.dev/skills/masterofowls-adaptyv (last updated 2026-10-24)