Skills adaptyv

📦

adaptyv

Name: adaptyv
Author: davila7

Content revision r1 High Risk 🌐 Network access📁 Filesystem access🔑 Env variables⚙️ External commands

Validate Protein Designs with Adaptyv

Protein design teams need a clear path from computational sequences to wet-lab evidence. This skill guides Adaptyv experiment setup, API submission, status tracking, and result retrieval.

Supports: Claude Codex Code(CC)

⚠️ 38 Poor

Stop for confirmation before installing.

Review the plan and obtain explicit user consent before changing files.

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "adaptyv" from https://skillstore.io/skills/davila7-adaptyv.md and its manifest at https://skillstore.io/api/skills/davila7-adaptyv/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/davila7-adaptyv.md Signed manifest GET /api/skills/davila7-adaptyv/manifest Signed lockfile GET /api/skills/davila7-adaptyv/lockfile

Test it

Using "adaptyv". I have one antibody binder candidate and need expression testing.

Expected outcome:

A concise experiment plan with FASTA preparation steps, the expression assay choice, required authentication, and expected status tracking milestones.

Using "adaptyv". I need to compare 50 variants before sending the best ones to the lab.

Expected outcome:

Screen variants for solubility and naturalness.
Rank candidates by combined computational scores.
Submit the top set with metadata and controls.
Track results and iterate on the next design round.

Using "adaptyv". My Adaptyv result does not match the prediction.

Expected outcome:

A troubleshooting checklist covering sequence validity, assay selection, controls, host system effects, and when to run additional validation.

Security Audit

High Risk

v14 • 7/9/2026 Open versioned report

Most static findings are documentation false positives, including Markdown fences, placeholder credentials, FASTA text, and biology terminology misread as reconnaissance. The real security concern is disclosed but meaningful external network activity: examples send protein sequences and bearer tokens to hardcoded third-party endpoints, including an opaque AWS Lambda URL.

Files scanned

2,337

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

High

SKILL.md:49-64 reference/examples.md:16-21 reference/protein_optimization.md:600-601 reference/api_reference.md:6-14

Opaque External API Endpoint Handles Secrets and Sequence Data

The skill directs users to send bearer API keys and protein sequences to a hardcoded AWS Lambda URL rather than an obvious Adaptyv-owned domain. This may be legitimate alpha infrastructure, but users cannot easily verify the service identity from the documentation alone.

The endpoint and bearer-token usage are explicit in multiple files. The finding is not higher confidence because the endpoint may be the legitimate Adaptyv alpha API.

Capability review items (22)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

High

reference/examples.md:21

Generic API/secret keys

"Authorization": f"Bearer {API_KEY}",

The example places an API key into an Authorization header for an external API request. This is intended, but it handles a secret and depends on trusting the endpoint.

High

reference/protein_optimization.md:601

Generic API/secret keys

headers={"Authorization": f"Bearer {api_key}"},

The example sends a bearer API key with a request to the hardcoded Adaptyv endpoint. This is intended, but it is real secret-bearing network traffic.

High

SKILL.md:53

Generic API/secret keys

"Authorization": f"Bearer {api_key}",

The quick-start example sends a bearer API key to the hardcoded Adaptyv endpoint. This is intended, but it is real secret-bearing network traffic.

Low

reference/api_reference.md:6

Hardcoded URL

https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws

The documentation points users to an opaque external Lambda URL as the Adaptyv API base. This is disclosed functionality, but it creates real network egress to a third-party endpoint.

Low

reference/examples.md:777

HTTP client library

response = requests.request(method, url, **kwargs)

The helper performs arbitrary HTTP methods against a provided URL in example code. It is documentation, but copied use could make outbound requests.

Low

reference/examples.md:28

Python HTTP libraries

response = requests.get(f"{BASE_URL}/organization/credits", headers=HEADERS)

The example performs an authenticated GET request to the Adaptyv API. This is intended, but it sends requests to an external service.

Low

reference/examples.md:79

Python HTTP libraries

response = requests.post(

The example posts protein sequence experiment data to the Adaptyv API. This is intended functionality with external data transfer.

Low

reference/examples.md:129

Python HTTP libraries

response = requests.post(

The example posts batch experiment data to the Adaptyv API. This is intended functionality with external data transfer.

Low

reference/examples.md:185

Python HTTP libraries

response = requests.post(

The example posts experiment data to the Adaptyv API. This is intended functionality with external data transfer.

Low

reference/examples.md:221

Python HTTP libraries

response = requests.get(

The example retrieves experiment status from the external Adaptyv API. This is expected network access.

Low

reference/examples.md:263

Python HTTP libraries

response = requests.get(

The example retrieves data from the external Adaptyv API. This is expected network access.

Low

reference/examples.md:338

Python HTTP libraries

response = requests.get(

The example downloads results from the external Adaptyv API. This is expected network access.

Low

reference/examples.md:479

Python HTTP libraries

response = requests.get(

The example retrieves experiment information from the external Adaptyv API. This is expected network access.

Low

reference/examples.md:528

Python HTTP libraries

response = requests.post(

The example posts workflow data to the external Adaptyv API. This is expected but transmits user-provided biological data.

Low

reference/examples.md:777

Python HTTP libraries

response = requests.request(method, url, **kwargs)

The helper performs outbound HTTP requests in example code. The behavior is documented, but it remains network-capable code if copied.

Low

reference/examples.md:17

Hardcoded URL

BASE_URL = "https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws"

The example configures an opaque external Lambda URL as BASE_URL for API calls. It is intended behavior, but users may send protein data to that endpoint.

Low

reference/protein_optimization.md:84

Python HTTP libraries

response = requests.post(url, data=data)

The example posts sequence data to the external NetSolP service. This is legitimate optimization guidance but sends data externally.

Low

reference/protein_optimization.md:599

Python HTTP libraries

response = requests.post(

The example posts optimized sequence data to the Adaptyv API. This is expected but sends user data externally.

Low

reference/protein_optimization.md:77

Hardcoded URL

url = "https://services.healthtech.dtu.dk/services/NetSolP-1.0/api/predict"

The optimization workflow posts sequence data to an external NetSolP service. This is a legitimate tool integration, but it is still third-party network egress.

Low

reference/protein_optimization.md:600

Hardcoded URL

"https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws/experiments",

The example submits optimized sequences to a hardcoded external Lambda endpoint. This is core functionality, but it can transmit user sequence data externally.

Low

SKILL.md:58

Python HTTP libraries

response = requests.post(

The quick-start example posts experiment data to the external Adaptyv API. This is expected but sends user data externally.

Low

SKILL.md:50

Hardcoded URL

base_url = "https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws"

The skill configures a hardcoded external Lambda endpoint as the API base URL. This is disclosed, but it is real third-party network egress.

Risk Factors

🌐 Network access (28)

📁 Filesystem access (4)

reference/api_reference.md:40 reference/examples.md:350 reference/examples.md:589 reference/examples.md:347

🔑 Env variables (24)

⚙️ External commands (12)

SKILL.md:20-22 SKILL.md:22-24 SKILL.md:24-26 SKILL.md:26-28 SKILL.md:28-34 SKILL.md:34-36 SKILL.md:36-42 SKILL.md:42-69 SKILL.md:69-80 SKILL.md:80-98 SKILL.md:98-102 SKILL.md:102-106

Audited by: claude View Audit History →

Share & cite this report

Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report

Copy report link

https://skillstore.io/skills/davila7-adaptyv/audits/14?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown badge

[![Skillstore security assessment](https://skillstore.io/badges/skills/davila7-adaptyv/security.svg)](https://skillstore.io/skills/davila7-adaptyv?utm_source=security_passport_badge)

HTML badge

<a href="https://skillstore.io/skills/davila7-adaptyv?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/davila7-adaptyv/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

Embed card

<iframe src="https://skillstore.io/embed/skills/davila7-adaptyv.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

Academic citations (APA · BibTeX · CFF)

APA citation

davila7. (2026). adaptyv security audit report (audit version 14) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-adaptyv/audits/14

BibTeX citation

@techreport{davila7-davila7-adaptyv-2026,
  author = {davila7},
  title = {adaptyv security audit report (audit version 14)},
  institution = {Skillstore},
  year = {2026},
  number = {14},
  url = {https://skillstore.io/skills/davila7-adaptyv/audits/14},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "adaptyv security audit report (audit version 14)"
version: "unspecified"
type: report
authors:
  - name: "davila7"
date-released: "2026-07-09"
url: "https://skillstore.io/skills/davila7-adaptyv/audits/14"
identifiers:
  - type: other
    value: "skillstore:davila7-adaptyv:audit:14"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

K-Dense-AI Recommended

k-dense-ai-adaptyv

Skillstore Score 50

Evidence Confidence High

Skillstore usage 8

Updated

2026-09-09

davila7 Current

davila7-adaptyv

Skillstore Score 38

Evidence Confidence Medium

Skillstore usage 8

Updated

2026-09-09

Skillstore Score

Why this score Evidence Confidence: Medium

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Submit optimized protein variants

Prepare FASTA content and submit selected protein variants for expression or binding validation.

Track cloud lab assay progress

Check experiment status, monitor credits, and plan follow-up analysis when results are ready.

Build validation workflows

Combine computational screening with Adaptyv submission examples for repeatable design-test-learn cycles.

Try These Prompts

Prepare a simple experiment

Help me prepare an Adaptyv expression experiment for this protein sequence. Show the required fields and explain what I need before submission.

Choose an assay type

Compare Adaptyv binding, expression, thermostability, and enzyme activity assays for my protein design goal. Recommend one assay and explain the tradeoffs.

Create a batch workflow

Design a batch Adaptyv workflow for 40 protein variants. Include FASTA formatting, metadata, status tracking, and result download steps.

Optimize before wet-lab testing

Review this protein variant library and propose an optimization pipeline using solubility, ESM, and structure-based checks before Adaptyv submission.

Best Practices

Use environment variables or ignored local files for API keys, never committed credentials.
Pre-screen sequences before submission to reduce failed or low-value wet-lab experiments.
Include controls and metadata so experiment results can be interpreted later.