adaptyv — Claude Code skill: what it does, how to install
Helps design protein characterization assays in Adaptyv Bio Foundry, check catalog targets, estimate run budgets, and collect test results.
npx skills add K-Dense-AI/scientific-agent-skills --skill adaptyv -a claude-codeWhat adaptyv does
The skill connects coding agents to the Adaptyv Bio Foundry API and SDK. It guides experiment setup for binding screening, thermostability, expression, and fluorescence assays. You can look up catalog targets, structure sequence batches, and calculate run costs before placing orders.
Setup requires Python 3.11+, network access, and a bearer token stored in ADAPTYV_API_KEY. Install adaptyv-sdk directly from its GitHub repository commit. If your project stores secrets in a .env file, call python-dotenv.load_dotenv() explicitly, as the SDK does not load it on its own.
Use REST via httpx to export final data. Generated SDK 0.1.0 models can drop newer live fields from the response. The lab.experiment() helper is also fragile: it expects UUIDs and fails on human-readable target names.
What it helps with
- estimate costs for protein sequence batches before lab submission
- query available targets and construct details in the Foundry catalog
- submit binding screening experiments with replicate plans
- fetch complete assay datasets via REST without losing response fields
Example prompts for the agent
- Search the Adaptyv catalog for EGFR targets with detailed pricing
- Estimate the cost for candidate sequences in candidates.json using BLI
- Fetch completed experiment results via REST and check results_status
How to install adaptyv
1Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill adaptyv -a claude-code2Manually: put the files in place
~/.claude/skills/adaptyv/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/adaptyv/
1Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill adaptyv -a codex2Manually: put the files in place
.agents/skills/adaptyv/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill adaptyv -a cursor2Manually: put the files in place
.cursor/skills/adaptyv/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install K-Dense-AI/scientific-agent-skills adaptyv2Manually: put the files in place
.github/skills/adaptyv/SKILL.md1Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill adaptyv -a gemini-cli2Platform CLI
gemini skills install https://github.com/K-Dense-AI/scientific-agent-skills --path skills/adaptyvManually: put the files in place
.devin/skills/adaptyv/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add K-Dense-AI/scientific-agent-skills --skill adaptyv -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set120
The whole set →Similar
Questions about adaptyv
How to install adaptyv in Claude Code?
Run npx skills add K-Dense-AI/scientific-agent-skills --skill adaptyv -a claude-code. It adds the skill files to your Claude Code setup.
What does the adaptyv skill do?
It provides instructions for interacting with the Adaptyv Bio Foundry cloud laboratory. The agent uses them to design wet-lab protein assays, calculate budgets, and retrieve measurements.
What prerequisites does adaptyv need?
You need Python 3.11+, an active Foundry token in ADAPTYV_API_KEY, and adaptyv-sdk installed from its GitHub repository. For archiving full responses without schema truncation, install httpx.
Source text
SKILL.md · MIT
---
name: adaptyv
description: Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress, and retrieve results. Applies to Adaptyv Foundry, its target catalog, binding screening and affinity assays, thermostability, expression, fluorescence, epitope binning, and enzyme activity workflows, including code using adaptyv or FoundryClient.
license: MIT
compatibility: Requires network access, an Adaptyv Foundry account and bearer token. Python examples require Python 3.11+ and adaptyv-sdk installed from its official GitHub repository; direct REST examples use httpx.
metadata:
version: "1.5"
skill-author: K-Dense Inc.
last-reviewed: "2026-09-30"
---
# Adaptyv Bio Foundry API
Use this skill to turn protein sequences into experimentally measured data through
Adaptyv's cloud laboratory. Confirm the target construct, assay conditions, controls,
and replicate plan before submitting a batch; turnaround depends on the experiment.
Reviewed against the deployed [OpenAPI schema](https://devs.adaptyvbio.com/api/v1/openapi.json)
(`info.version: 0.0.2`) and official SDK `0.1.0` at commit
`cdf207819ed5a58e0c127453626d2bce125c8064`. The schema version alone does not identify
all changes. See the [endpoint reference](references/api-endpoints.md) for current
request/response details and discrepancies in upstream examples.
## Connect
**Base URL:** `https://devs.adaptyvbio.com/api/v1`. The schema is at
`/openapi.json`; never append that filename to normal endpoint requests.
Create a bearer token in [Foundry](https://foundry.adaptyvbio.com/) under
Organization → Settings → Tokens. Use Viewer for reads and Member for experiment
writes. Read credentials from `ADAPTYV_API_KEY`; the documentation's
`FOUNDRY_API_TOKEN` is an alternative variable name for the same token. If using a
project `.env`, explicitly call `python-dotenv.load_dotenv()` before SDK setup;
the SDK does not load that file automatically. Never print or commit tokens.
```bash
# ADAPTYV_API_KEY is already set in the environment.
curl --fail-with-body 'https://devs.adaptyvbio.com/api/v1/targets?limit=3' \
-H "Authorization: Bearer $ADAPTYV_API_KEY"
```
Resource endpoints require bearer authentication. The schema and liveness endpoint
`GET /info/health` are public. `GET /whoami` reports the active organization and
permissions; check it when account scope is unclear.
## Python SDK
Install the reviewed revision in your project environment (requires Python 3.11+):
```bash
uv pip install "git+https://github.com/adaptyvbio/adaptyv-sdk.git@cdf207819ed5a58e0c127453626d2bce125c8064"
```
Import from `adaptyv`, not `adaptyv_sdk`. `FoundryClient` requires explicit `api_key`
and `base_url`; the `lab` convenience object reads `ADAPTYV_API_KEY` and
`ADAPTYV_API_URL`. Set the latter to the base URL above to override the SDK's
older hostname default.
The following examples are illustrative for authenticated use. Their request
construction and parsing were checked with mocked HTTP responses, not a paid lab
run. Replace environment inputs with real, reviewed sequence data and catalog IDs.
### Browse, estimate, create a draft
```python
import json
import os
from pathlib import Path
from adaptyv import FoundryClient
client = FoundryClient(
api_key=os.environ["ADAPTYV_API_KEY"],
base_url="https://devs.adaptyvbio.com/api/v1",
)
# Review vendor, catalog number, construct, and pricing before selecting an ID.
targets = client.targets.list(search="EGFR", selfservice_only=True, detailed=True)
for target in targets.items:
print(target.id, target.name, target.vendor_name, target.catalog_number)
# candidates.json is a map of unique names to full amino acid sequences.
sequences = json.loads(Path("candidates.json").read_text())
spec = {
"experiment_type": "screening",
"method": "bli",
"target_id": os.environ["ADAPTYV_TARGET_ID"],
"sequences": sequences,
"n_replicates": 3,
}
# SDK takes the spec itself; REST takes {"experiment_spec": spec}.
estimate = client.experiments.cost_estimate(spec)
if estimate.breakdown is None:
raise ValueError("Incomplete estimate: review warnings and obtain a full quote")
print("Estimated USD cents, excluding VAT:", estimate.breakdown.total_cents)
# After the batch and estimated cost have been reviewed:
exp = client.experiments.create(name="EGFR binder screen batch 1", experiment_spec=spec)
experiment_id = exp.experiment_id
client.close()
```
`create(name=..., experiment_spec=...)` does not accept a single REST-body dictionary.
`cost_estimate(spec)` wraps the spec itself; do not wrap it a second time.
For an affinity experiment, the SDK additionally requires explicit
`antigen_concentrations`, even though REST supplies a default when omitted.
### Submit, then inspect and confirm the quote
With a configured client and the saved `experiment_id`:
```python
client.experiments.submit(experiment_id)
# Quote generation is asynchronous. Retry get_quote with a bounded timeout if it
# returns 404 after successful submission; a draft has no forthcoming quote.
quote = client.experiments.get_quote(experiment_id)
print(quote.amount_total, quote.currency, quote.expires_at)
# Once the quote is accepted within the user's authorized scope:
accepted = client.experiments.confirm_quote(experiment_id)
print(accepted.invoice_id, accepted.hosted_invoice_url)
```
Confirmation creates/finalizes an invoice; it does not settle payment. Use the
returned hosted invoice URL or the current REST payment pointer. SDK 0.1.0 does
not expose invoice payment, organization webhooks, or `whoami` helpers.
Avoid the reviewed SDK's `lab.experiment(target="EGFR")` shortcut: it passes only
UUID-shaped targets and does not resolve a target name. `auto_confirm=True` and
`lab.confirm_experiment()` call submission, not the quote-confirm endpoint. Use
the explicit client methods above. The decorator also supplies `method` for
non-binding experiments, which the current API rejects.
### Retrieve every result without losing fields
The SDK's generated models predate some live result fields and may discard them.
Use REST JSON when archiving results, target references, or newer kinetic fits:
```python
import json
import os
from pathlib import Path
import httpx
experiment_id = os.environ["ADAPTYV_EXPERIMENT_ID"]
with httpx.Client(
base_url="https://devs.adaptyvbio.com/api/v1",
headers={"Authorization": f"Bearer {os.environ['ADAPTYV_API_KEY']}"},
timeout=30,
) as api:
response = api.get(f"/experiments/{experiment_id}")
response.raise_for_status()
experiment = response.json()
if experiment["results_status"] != "all":
raise RuntimeError("Results are not complete; inspect status before analysis")
results, offset = [], 0
while True:
response = api.get(f"/experiments/{experiment_id}/results",
params={"limit": 100, "offset": offset})
response.raise_for_status()
page = response.json()
results.extend(page["items"])
offset += len(page["items"])
if not page["items"] or offset >= page["total"]:
break
Path("foundry-results.json").write_text(json.dumps(
{"experiment": experiment, "results": results}, indent=2
))
```
Archive raw data packages when available, plus sequence/target identities, assay
method, units, conditions, replicate measurements and fit quality. Null kinetic
values are missing/unresolved measurements, not zero. A screening binding call is
not a measured affinity. Compare KD values only under compatible assay conditions;
the schema's `kd_mean` averages strong-binding replicates and is not an unbiased
summary of every tested replicate.
## Experiment types and validation
| `experiment_type` | `method` | `target_id` | Sequence count | Replicates |
|---|---|---|---|---|
| `affinity` | Required: `bli` or `spr` | Required | At least 1 | Optional, 1–5; default 3 |
| `screening` | Required: `bli` or `spr` | Required | At least 1 | Optional, 1–5; default 3 |
| `thermostability` | Omit | Omit | At least 1 | Optional, 1–5; default 3 |
| `expression` | Omit | Omit | At least 1 | Optional, 1–5; default 3 |
| `fluorescence` | Omit | Omit | At least 1 | Optional, 1–5; default 3 |
| `epitope_binning` | Omit | Required | 4–28, multiple of 4 | Omit |
| `enzyme_activity` | Omit | Omit | At least 1 | Optional, 1–5; default 3 |
Inapplicable fields are rejected. `antigen_concentrations` is affinity-only, in nM;
REST's default is `[1000.0, 316.2, 100.0, 31.6, 0.0]`. `parameters` holds optional
assay settings; coordinate any nonstandard configuration with the laboratory.
Sequences accept full strings or rich entries such as
`{"candidate": {"aa_string": "EVQLVESGGGLVQPGGSLRLSCAAS", "control": false}}`.
Use the 20 standard amino acid letters; inputs are case-insensitive and stored
uppercase. Colons separate chains. Ellipses are not valid sequence characters.
Rich creation metadata is constrained by `SequenceMetadata`, not arbitrary JSON:
use `SingleChain`, `ScFv`, `FAB`, or `IgG`; ScFv needs `VH` and `VL`, FAB needs
`framework_regions.ch` and `.cl`. The SDK accepts the schema's Portal-style enum
values. Add sequences through `POST /sequences` only while status is `draft`.
## Lifecycle and updates
Wire status values are lowercase snake case:
```text
draft -> waiting_for_confirmation -> quote_sent -> waiting_for_materials
-> in_queue -> in_production -> data_analysis -> in_review -> done
```
`canceled` is also possible. `results_status` is independently `none`, `partial`,
or `all`. Inspect it before treating a result set as complete. Most experiment
PATCH fields are editable in `draft` or `in_review`; `webhook_url` remains editable
at any status. A sequence PATCH replaces the sequence list; `POST /sequences`
appends to a draft.
Create-time REST flags `skip_draft` and `auto_accept_quote` enable automation;
`auto_accept_quote` implies `skip_draft` and requires full pricing. They can commit
the batch to laboratory processing and billing, so use them only within the
user's approved experiment/budget scope. The SDK's public `create()` method does
not expose `auto_accept_quote`, `webhook_secret`, or the payment selector; use the
REST contract in the reference when these fields are needed.
For signed delivery, set a per-experiment `webhook_url` and `webhook_secret`
(minimum 32 characters) at creation, or register an organization webhook. Store
the secret when setting it; it is write-only. An experiment webhook overrides
organization delivery. Verify `X-Adaptyv-Signature` (`sha256=<hex>`) as HMAC-SHA256
over the raw body with constant-time comparison. Events are `experiment_update`
customer-facing updates, not a complete stream of status transitions. Deduplicate
by `data.update_id`; `delivery_id` identifies an attempt. Return `2xx`; network
failures and `5xx` retry up to three times, while `4xx` is permanent. Continue to
poll status/results if delivery is missing.
## Lists, filters and errors
Paginated endpoints return `{items, total, count, offset}`; use `limit` 1–100
(default 50) and `offset` (default 0). Fetch all pages. Query support varies by
endpoint: sequence lists lack `filter`, token lists only paginate, and organization
webhooks return an unpaginated array. See the endpoint reference.
Where supported, filters use `eq(field,value)`, `neq`, `gt`, `gte`, `lt`, `lte`,
`contains`, `between`, `in`, `is_null`, `is_not_null`, combined with `and`, `or`,
`not`. Example: `and(gte(created_at,2026-01-01),eq(status,done))`. Sorting accepts
`asc(field)` / `desc(field)` (up to eight comma-separated terms), `-field` / `+field`,
or `field:asc` / `field:desc`. Use the HTTP client's query encoder. Advanced
expressions support `at(field,key)` and casts (`float`, `int`, `text`, `timestamp`,
`date`), subject to each endpoint's allowed fields.
Structured errors generally include `error` and `request_id`; record the
`x-request-id` header for support. Handle non-JSON transport failures too. Check
status before retrying mutations after an ambiguous timeout: the server may
already have created the experiment or accepted a quote. The SDK retries 429/5xx
responses; its retries do not establish mutation idempotency.
Token attenuation only narrows permissions. `POST /tokens/revoke` revokes the
calling token's **root family**, including siblings descended from that root,
even when authenticating with an attenuated token.
## Citing Scientific Agent Skills
This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a
manuscript, report, presentation, or code release, add the paper to the references or
software section and tell the user you did so:
> Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent
> Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065.
> https://doi.org/10.48550/arXiv.2609.00065
Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the
latest arXiv version, so never append a version suffix such as `v1`. When network access is
available, fetch https://arxiv.org/abs/2609.00065 (or
http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take
the author list, year, and version from that record. If the record lists a journal reference
or publisher DOI, cite the published version instead.
© K-Dense-AI · MIT · K-Dense-AI/scientific-agent-skills