K-Dense-AI/scientific-agent-skills/skills/adaptyv/SKILL.md
adaptyv
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`.
- Source repository stars
- 31,966
- Declared platforms
- 0
- Static risk flags
- 1
- Last source update
- 2026-07-28
- Source checked
- 2026-07-28
Decision brief
What it does—and where it fits
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.
Not for
- Tasks that require unconfirmed production actions or broad system permissions.
- Environments where the pinned source and install steps cannot be inspected.
Compatibility matrix
Platform support, with evidence labels
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
Inspect first. Install second.
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill "skills/adaptyv"Inspect the Agent Skill "adaptyv" from https://github.com/K-Dense-AI/scientific-agent-skills/blob/e7ac42510774624f327003c95b6650e2883bc01d/skills/adaptyv/SKILL.md at commit e7ac42510774624f327003c95b6650e2883bc01d. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
What the source asks the agent to do
- 01
Quick Start
Base URL: https://foundry-api-public.adaptyvbio.com/api/v1
Base URL: https://foundry-api-public.adaptyvbio.com/api/v1Authentication: 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 ADAPTYVAPIKEY 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 pyth… - 02
1. Submit a Binding Screen (Step by Step)
Review the “1. Submit a Binding Screen (Step by Step)” section in the pinned source before continuing.
Review and apply the “1. Submit a Binding Screen (Step by Step)” source section. - 03
4. Submit for review
client.experiments.submit(exp.experimentid)
client.experiments.submit(exp.experimentid) - 04
Python SDK
Version note: adaptyv-sdk 0.1.0 (beta) is not yet on PyPI — install from GitHub:
Version note: adaptyv-sdk 0.1.0 (beta) is not yet on PyPI — install from GitHub:In a project with pyproject.toml:Environment variables (set in shell or .env file): - 05
Decorator Pattern
Review the “Decorator Pattern” section in the pinned source before continuing.
Review and apply the “Decorator Pattern” source section.
Permission review
Static risk signals and limitations
Network access
The documentation includes network, browsing, or remote request actions.
curl https://foundry-api-public.adaptyvbio.com/api/v1/targets?limit=3 \Network access
The documentation includes network, browsing, or remote request actions.
uv pip install "git+https://github.com/adaptyvbio/adaptyv-sdk.git"Evidence record
Why each signal appears
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 82/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 31,966 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Provenance and original SKILL.md
- Repository
- K-Dense-AI/scientific-agent-skills
- Skill path
- skills/adaptyv/SKILL.md
- Commit
- e7ac42510774624f327003c95b6650e2883bc01d
- License
- MIT
- Collected
- 2026-07-28
- Default branch
- main
View the original 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
| 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 |
InQueue | Adaptyv | Materials arrived, queued for lab |
InProduction | Adaptyv | Assay running |
DataAnalysis | Adaptyv | Raw data processing and QC |
InReview | Adaptyv | Final validation |
Done | You | Results available |
Canceled | Either | Experiment 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
Draftstatus
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.
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