Alterlab adaptyv
Skill AlterLab-IEU/AlterLab-Academic-Skills/skills/domain-specific/alterlab-adaptyv
239 evaluated academic Claude/agent skills across 17 research domains (bioinformatics, data science, clinical, social-science methods, Turkish academia & more). Executable eval per skill, deterministic citation verifier, research→write→review→publish pipeline, and a skill-finder front door. Claude Code, Cursor, Codex, Gemini CLI & Copilot.
npx -y skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-adaptyvAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
Copied from the file, not written here
Submits and tracks protein-testing experiments on the Adaptyv Bio Foundry cloud lab (wet-lab validation), and optimizes protein sequences before submission with computational tools (NetSolP, SoluProt, SolubleMPNN, ESM). Use when designing proteins that need wet-lab validation - binding/affinity screening, expression testing, thermostability, or fluorescence assays - or when submitting experiments to the Foundry API, browsing the target catalog, tracking experiment status, retrieving results, or pre-screening sequences for solubility/expression. Triggers on "Adaptyv", "Foundry API", "cloud lab", "biolayer interferometry / BLI", "wet-lab validation". Part of the AlterLab Academic Skills suite.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
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Adaptyv
Adaptyv Bio runs the Foundry cloud lab: submit protein sequences and a target, the lab runs the assay, and you retrieve experimental data (binding/affinity, thermostability, expression, fluorescence). The public Foundry API drives the full lifecycle programmatically. Turnaround is on the order of weeks; confirm the current estimate from the per-experiment quote rather than assuming a fixed number.
The exact request/response shapes evolve. This skill captures the verified API contract and conventions; for the authoritative spec see the OpenAPI doc at
https://foundry-api-public.adaptyvbio.com/api/v1/openapi.jsonandhttps://docs.adaptyvbio.com.
Prefer the official tooling first
Adaptyv ships its own integrations - reach for them before hand-rolling requests:
- Official Python SDK —
github.com/adaptyvbio/adaptyv-sdk(MIT). Decorator-based: wrap a design function with@lab.experiment(target=...); readsADAPTYV_API_KEY/ADAPTYV_API_URL(and optionalADAPTYV_ORGANIZATION_ID) from the environment. Install from source (pip install -e .after cloning — no PyPI release confirmed; verify before pinning). - Adaptyv's own Claude Code skills —
github.com/adaptyvbio/protein-design-skills. Useful prior art for protein-design + Foundry workflows.
Use this skill's raw-requests recipes when the SDK is unavailable or you need fine control over the lifecycle.
Quick Start
Authentication Setup
- Create a token in the Foundry portal:
https://foundry.adaptyvbio.com/→ Organization → Settings → Tokens (pick a role: Member = read/write, Viewer = read-only; set an expiry). The token value is shown only once — copy it immediately. - Set it in your environment (never commit it):
export ADAPTYV_API_KEY="your_token_here"
Or put it in a gitignored .env:
ADAPTYV_API_KEY=your_token_here
Installation
If using the raw API directly:
uv pip install requests python-dotenv
Basic Usage
The API uses a draft → submit flow: create an experiment (it starts as a draft), then submit it. sequences is a {label: amino_acid_string} map (multi-chain constructs join chains with a colon, e.g. "heavy:light").
import os
import requests
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("ADAPTYV_API_KEY")
base_url = "https://foundry-api-public.adaptyvbio.com/api/v1"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
# 1. Create a draft experiment
resp = requests.post(
f"{base_url}/experiments",
headers=headers,
json={
"name": "mini-binder round 1",
"experiment_spec": {
"experiment_type": "affinity", # screening|affinity|thermostability|fluorescence|expression
"method": "bli", # bli|spr (for binding-type assays)
"target_id": "<target uuid from GET /targets>",
"sequences": {
"design_a": "MKVLWALLGLLGAA...",
"design_b": "MATGVLWALLG...",
},
},
},
)
resp.raise_for_status()
experiment_id = resp.json()["experiment_id"]
# 2. Submit it to the lab (after reviewing the quote — see reference/api_reference.md)
requests.post(f"{base_url}/experiments/{experiment_id}/submit", headers=headers).raise_for_status()
Available Experiment Types
Foundry supports these experiment_type values:
- screening - Binding detection via biolayer interferometry (BLI) or SPR. Requires a target.
- affinity - Kinetic constants (KD, kon, koff) by BLI/SPR. Requires a target.
- thermostability - Melting temperature (Tm) via DSF. No target required.
- fluorescence - Fluorescence intensity. No target required.
- expression - Protein yield quantification. No target required.
See reference/experiments.md for detailed information on each assay and its outputs.
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 (both are web services, not pip packages)
- SolubleMPNN - Solubility-biased sequence redesign (a weight set within the ProteinMPNN / LigandMPNN family)
- ESM (
fair-esm) - Sequence likelihood / naturalness scoring - ipTM (AlphaFold-Multimer / ColabFold) - Interface stability for binder designs
- pSAE - Solvent-accessible hydrophobic exposure, from a predicted/known structure
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
- The Foundry API is public but still evolving — treat the OpenAPI doc (
/api/v1/openapi.json) as the source of truth and verify field names before relying on them. - Submission is two-step: create a
draft, review the cost quote, thenPOST .../submit. Nothing is charged until you confirm the quote. affinity/screeningrequire atarget_idfrom the catalog (GET /targets);thermostability,fluorescence, andexpressiondo not.- Turnaround is multiple weeks — read the estimate from the experiment/quote rather than assuming a fixed number.
- Support and docs: [email protected] /
https://docs.adaptyvbio.com. - Suitable for high-throughput AI-driven protein design workflows (closed-loop design → test → learn).