技能 adaptyv

📦

adaptyv

Name: adaptyv
Author: K-Dense-AI

內容修訂 r1 中風險 🌐 網路存取📁 檔案系統存取🔑 環境變數⚙️ 外部命令

將蛋白質實驗提交至 Adaptyv

蛋白質團隊需要一條可靠路徑，將設計好的序列推進到實驗驗證。此技能會引導 Claude、Codex 和 Claude Code 完成 Adaptyv API 設定、實驗提交、狀態追蹤與結果處理。

支援: Claude Codex Code(CC)

⚠️ 50 差

Maintain this Skill? Claim it ⚠️ 50 差

使用我的智慧代理安裝

將此請求複製給你的智慧代理，其中包含權威 Skill 頁面和清單。

智能體請求

Review the Skillstore skill "adaptyv" from https://skillstore.io/skills/k-dense-ai-adaptyv.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-adaptyv/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。

Agent 可讀資源

當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。

Markdown 詳情 GET /skills/k-dense-ai-adaptyv.md 已簽名 manifest GET /api/skills/k-dense-ai-adaptyv/manifest 已簽名 lockfile GET /api/skills/k-dense-ai-adaptyv/lockfile

測試它

正在使用「adaptyv」。我想測試 20 個類抗體變體的結合能力。

預期結果:

此技能會概述批次提交計畫、必要的目標資訊、metadata 欄位，以及傳送序列前的同意檢查。

正在使用「adaptyv」。我的實驗已完成。我應該擷取什麼？

預期結果:

實驗狀態摘要
關鍵測量欄位，例如親和力或表現量
品質指標與可下載報告的連結

正在使用「adaptyv」。我應該先提交哪些變體？

預期結果:

此技能會建議在選擇候選序列前，先預先篩選溶解度、序列有效性與功能性殘基保留情況。

安全審計

中風險

v9 • 2026/7/9 開啟版本化報告

Most high-severity static matches are false positives caused by placeholder API keys, .env guidance, Markdown fences, or scientific sequence terminology. The real risks are intended outbound API calls to Adaptyv and NetSolP, local example file writes, and disclosure of proprietary protein sequences to a third-party lab service.

已掃描檔案

2,336

分析行數

複核項目

已忽略的誤報

已確認的安全問題 (1)

中

SKILL.md:11 SKILL.md:61-67 reference/api_reference.md:31-43

Third-party submission of proprietary protein sequences

The skill is designed to submit protein sequences and experiment metadata to the Adaptyv cloud lab API. This can expose proprietary biological designs or research plans if users do not have permission to share them with that service.

The documentation directly describes submitting protein sequences through the Adaptyv API and shows request payloads containing sequences and webhooks. The risk is contextual data disclosure, not hidden malicious behavior.

能力複核項目 (20)

這些是真實的本機能力，對此技能而言可能是預期行為，因此需要複核，但不會按已確認的惡意行為計分。

中

reference/examples.md:350

Python file write/append

with open(output_file, 'w') as f:

The example writes experiment result data to local files using API-derived identifiers or caller-provided output paths. This is normal example behavior, but it can overwrite local files if copied without path controls.

中

reference/examples.md:589

Python file write/append

with open(f"{experiment_id}_info.json", 'w') as f:

中

reference/examples.md:347

Python os file operations

os.makedirs(output_dir, exist_ok=True)

低

reference/api_reference.md:6

Hardcoded URL

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

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:28

Python HTTP libraries

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

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:79

Python HTTP libraries

response = requests.post(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:129

Python HTTP libraries

response = requests.post(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:185

Python HTTP libraries

response = requests.post(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:221

Python HTTP libraries

response = requests.get(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:263

Python HTTP libraries

response = requests.get(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:338

Python HTTP libraries

response = requests.get(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:479

Python HTTP libraries

response = requests.get(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:528

Python HTTP libraries

response = requests.post(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/examples.md:17

Hardcoded URL

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

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/protein_optimization.md:84

Python HTTP libraries

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

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/protein_optimization.md:599

Python HTTP libraries

response = requests.post(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

reference/protein_optimization.md:77

Hardcoded URL

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

The example sends protein sequence data to an external DTU NetSolP web service. This is intended scientific functionality, but it is still a real outbound data-sharing action.

低

reference/protein_optimization.md:600

Hardcoded URL

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

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

SKILL.md:61

Python HTTP libraries

response = requests.post(

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

低

SKILL.md:53

Hardcoded URL

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

The skill documents real outbound requests to the Adaptyv cloud lab API. This is intended behavior, but users may submit proprietary protein sequences to a third-party service.

風險因素

🌐 網路存取 (28)

📁 檔案系統存取 (4)

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

🔑 環境變數 (24)

⚙️ 外部命令 (12)

SKILL.md:23-25 SKILL.md:25-27 SKILL.md:27-29 SKILL.md:29-31 SKILL.md:31-37 SKILL.md:37-39 SKILL.md:39-45 SKILL.md:45-72 SKILL.md:72-81 SKILL.md:81-98 SKILL.md:98-101 SKILL.md:101-104

審計單位: claude 檢視審計紀錄 →

分享與引用此報告

分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。

開啟版本化報告

複製報告連結

https://skillstore.io/skills/k-dense-ai-adaptyv/audits/9?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown 徽章

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

HTML 徽章

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

嵌入卡片

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

學術引用 (APA · BibTeX · CFF)

APA 引用

K-Dense-AI. (2026). adaptyv security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-adaptyv/audits/9

BibTeX 引用

@techreport{k-dense-ai-k-dense-ai-adaptyv-2026,
  author = {K-Dense-AI},
  title = {adaptyv security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/k-dense-ai-adaptyv/audits/9},
  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 9)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-09"
url: "https://skillstore.io/skills/k-dense-ai-adaptyv/audits/9"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-adaptyv:audit:9"
    description: "Skillstore immutable audit report identifier"

比較變體

2 個可安裝變體

每位作者的技能都保留為獨立的可安裝項目。推薦變體依 Skillstore 證據排序。

為何此變體排名第一

Skillstore 評分最高

作者 / 來源 Skillstore 評分證據置信度 Skillstore 使用量已更新

K-Dense-AI 推薦目前

k-dense-ai-adaptyv

Skillstore 評分 50

證據置信度高

Skillstore 使用量 15

已更新

2026-09-09

davila7

davila7-adaptyv

Skillstore 評分 38

證據置信度中

Skillstore 使用量 13

已更新

2026-09-09

Skillstore 評分

評分依據證據置信度: 高

架構

可維護性

內容

社群

規範符合度

你可以打造什麼

驗證設計的結合蛋白

準備序列 payload、選擇結合測定，並透過 Adaptyv 提交變體進行實驗測試。

自動化實驗追蹤

建立可提交實驗、檢查狀態、處理失敗並擷取已完成結果摘要的腳本。

排定表現候選序列優先順序

在將最有潛力的候選序列送往濕實驗室驗證前，先使用計算式溶解度與序列檢查。

試試這些提示

設定驗證

協助我安全地設定 Adaptyv API 驗證。我有 API 權杖，並想使用環境變數。

提交單一實驗

建立一個安全計畫，將一條蛋白質序列提交至 Adaptyv 進行表現測試。請包含必要欄位與資料共享檢查。

追蹤實驗結果

設計一個工作流程，用於檢查 Adaptyv 實驗狀態、處理常見 API 錯誤，並將最終結果摘要儲存在本機。

最佳化並驗證文庫

協助我使用溶解度與自然性檢查來排定蛋白質變體文庫的優先順序，然後準備最優先的候選序列進行 Adaptyv 驗證。

最佳實務

在將序列資料提交至任何外部服務前，確認該資料可以共享。
將 API 金鑰儲存在環境變數中，並避免將本機憑證檔案納入版本控制。
使用批次提交、webhook 與本機驗證，以減少失敗或重複的實驗。

避免

不要將真實 API 金鑰貼到 prompt、程式碼片段或共享文件中。
未取得資料擁有者核准前，不要提交專有序列。
當需要實驗驗證時，不要只依賴計算分數。

常見問題

這項技能能協助我做什麼？

它有助於規劃並自動化 Adaptyv 蛋白質測試工作流程，包括提交、追蹤與結果擷取。

我需要 Adaptyv 帳戶嗎？

是。API 範例需要 Adaptyv API 權杖與平台存取權限。

它可以自行執行實驗嗎？

不能。它提供指引與 API 範例。實際實驗需要有效憑證、可用額度與使用者核准。

它適合用於機密蛋白質序列嗎？

請謹慎使用。此工作流程會將序列資料傳送至外部服務，因此提交前請先確認權限。

涵蓋哪些實驗類型？

文件涵蓋結合測定、表現測試、熱穩定性測量與酵素活性測定。

它能協助在測試前最佳化序列嗎？

可以。它描述了使用溶解度、結構與序列可能性工具的篩選與最佳化工作流程。

開發者詳細資訊

作者

K-Dense-AI

授權

Unknown

Skillstore 修訂

版本提示

作者未聲明版本。

儲存庫

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/adaptyv

引用

1ffa7643651792ccb4bd3b15d924d2c97edff755

維護新鮮度

2026/7/18

使用量

4 次下載 · 182 次瀏覽

檔案結構

📁 reference/

📄 api_reference.md

📄 examples.md

📄 experiments.md

📄 protein_optimization.md

📄 SKILL.md