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Skills/k-dense-ai/scientific-agent-skills/adaptyv
adaptyv logo

adaptyv

k-dense-ai/scientific-agent-skills
574 installs28K stars
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Installation

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill adaptyv

Summary

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`.

SKILL.md

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 · llms.txt index · OpenAPI spec

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 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 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.

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:

uv pip install "git+https://github.com/adaptyvbio/adaptyv-sdk.git"

In a project with pyproject.toml:

uv add "adaptyv-sdk @ git+https://github.com/adaptyvbio/adaptyv-sdk.git"

Environment variables (set in shell or .env file):

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

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

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

TypeMethodMeasuresRequires Target
affinitybli or sprKD, kon, koff kineticsYes
screeningbli or sprYes/no bindingYes
thermostability—Melting temperature (Tm)No
expression—Expression yieldNo
fluorescence—Fluorescence intensityNo

Experiment Lifecycle

Draft → WaitingForConfirmation → QuoteSent → WaitingForMaterials → InQueue → InProduction → DataAnalysis → InReview → Done
StatusWho ActsDescription
DraftYouEditable, no cost commitment
WaitingForConfirmationAdaptyvUnder review, quote being prepared
QuoteSentYouReview and confirm the quote
WaitingForMaterialsAdaptyvGene fragments and target ordered
InQueueAdaptyvMaterials arrived, queued for lab
InProductionAdaptyvAssay running
DataAnalysisAdaptyvRaw data processing and QC
InReviewAdaptyvFinal validation
DoneYouResults available
CanceledEitherExperiment canceled

The results_status field on an experiment tracks: none, partial, or all.

Common Workflows

1. Submit a Binding Screen (Step by Step)

# 1. Find a target
targets = client.targets.list(search="EGFR", selfservice_only=True)
target_id = targets.items[0].id

# 2. Preview cost
estimate = client.experiments.cost_estimate({
    "experiment_spec": {
        "experiment_type": "screening",
        "method": "bli",
        "target_id": target_id,
        "sequences": {"seq1": "EVQLVESGGGLVQ...", "seq2": "MKVLVAG..."},
        "n_replicates": 3
    }
})

# 3. Create experiment (starts as Draft)
exp = client.experiments.create({
    "name": "EGFR binder screen batch 1",
    "experiment_spec": {
        "experiment_type": "screening",
        "method": "bli",
        "target_id": target_id,
        "sequences": {"seq1": "EVQLVESGGGLVQ...", "seq2": "MKVLVAG..."},
        "n_replicates": 3
    }
})

# 4. Submit for review
client.experiments.submit(exp.experiment_id)

# 5. Poll or use webhooks until Done
# 6. Retrieve results
results = client.experiments.get_results(exp.experiment_id)

2. Automated Pipeline (Skip Draft + Auto-Accept Quote)

exp = client.experiments.create({
    "name": "Auto pipeline run",
    "experiment_spec": {...},
    "skip_draft": True,
    "auto_accept_quote": True,
    "webhook_url": "https://my-server.com/webhook"
})
# Webhook fires on each status transition; poll or wait for Done

3. Using Webhooks

Pass webhook_url when creating an experiment. Adaptyv POSTs to that URL on every status transition with the experiment ID, previous status, and new status.

Sequences

  • Simple format: {"seq1": "EVQLVESGGGLVQPGGSLRLSCAAS"}
  • Rich format: {"seq1": {"aa_string": "EVQLVESGGGLVQ...", "control": false, "metadata": {"type": "scfv"}}}
  • Multi-chain: use colon separator — "MVLS:EVQL"
  • Valid amino acids: A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y (case-insensitive, stored uppercase)
  • Sequences can only be added to experiments in Draft status

Filtering, Sorting, and Pagination

All list endpoints support pagination (limit 1-100, default 50; offset), search (free-text on name fields), and sorting.

Filtering uses s-expression syntax via the filter query parameter:

  • Comparison: eq(field,value), neq, gt, gte, lt, lte, contains(field,substring)
  • Range/set: between(field,lo,hi), in(field,v1,v2,...)
  • Logic: and(expr1,expr2,...), or(...), not(expr)
  • Null: is_null(field), is_not_null(field)
  • JSONB: at(field,key) — e.g., eq(at(metadata,score),42)
  • Cast: float(), int(), text(), timestamp(), date()

Sorting uses asc(field) or desc(field), comma-separated (max 8):

sort=desc(created_at),asc(name)

Example: filter=and(gte(created_at,2026-01-01),eq(status,done))

Error Handling

All errors return:

{
  "error": "Human-readable description",
  "request_id": "req_019462a4-b1c2-7def-8901-23456789abcd"
}

The request_id is also in the x-request-id response header — include it when contacting support.

Token Management

Tokens use Biscuit-based cryptographic attenuation. You can create restricted tokens scoped by organization, resource type, actions (read/create/update), and expiry via POST /tokens/attenuate. Revoking a token (POST /tokens/revoke) revokes it and all its descendants.

Detailed API Reference

For the full list of all 32 endpoints with request/response schemas, read references/api-endpoints.md.

Score

0–100
65/ 100

Grade

C

Popularity17/30

574 installs — growing adoption. Source repo has 28,214 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Adaptyv skill score badge previewScore badge

Markdown

[![Adaptyv skill](https://www.remoteopenclaw.com/skills/k-dense-ai/scientific-agent-skills/adaptyv/badges/score.svg)](https://www.remoteopenclaw.com/skills/k-dense-ai/scientific-agent-skills/adaptyv)

HTML

<a href="https://www.remoteopenclaw.com/skills/k-dense-ai/scientific-agent-skills/adaptyv"><img src="https://www.remoteopenclaw.com/skills/k-dense-ai/scientific-agent-skills/adaptyv/badges/score.svg" alt="Adaptyv skill"/></a>

Adaptyv FAQ

How do I install the Adaptyv skill?

Run “npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill adaptyv” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Adaptyv skill do?

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`. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Adaptyv skill free?

Yes. Adaptyv is a free, open-source skill published from k-dense-ai/scientific-agent-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Adaptyv work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Adaptyv works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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