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benchling-integration

Benchling Python SDK 與 REST API 整合,用於註冊實體、庫存、ELN 條目、工作流程、Benchling Apps 及資料倉儲查詢。適用於透過 benchling-sdk 或 v2 API 自動化實驗數據處理。

benchling-integration 是什麼?

此技能專為需要與 Benchling 生命科學 R&D 雲端平台進行程式化互動的開發者與 AI 代理所設計。它提供了透過 Python SDK (benchling-sdk 1.25.0) 與 REST API 管理和自動化核心實驗室數據操作的完整指南,包括生物序列(DNA、RNA、蛋白質)的註冊實體管理、庫存管理(樣品、容器、位置、移轉)、電子實驗室筆記本(ELN)條目、工作流程自動化以及 Benchling Apps 的建置。此外,本技能還涵蓋了資料倉儲的 SQL 查詢與事件驅動的整合(如 AWS EventBridge)。為了確保安全且高效的開發實踐,它詳細說明了認證方式(API Key 與 OAuth)、錯誤處理(自動重試與指數退避)、分頁效率(使用生成器)以及 Schema 欄位輔助工具。透過遵循最佳實務與提供的範例(如批次匯入、庫存審計、工作流程自動化),使用者能可靠地將 Benchling 整合進其自動化流程與數據管線中。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/benchling-integration

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

Benchling 整合

概述

Benchling 是一個用於生命科學研發的雲端平台。透過 Python SDK 和 REST API,您可以程式化地存取註冊實體(DNA、RNA、蛋白質)、庫存、電子實驗室筆記本和工作流程。

版本說明: 範例針對 benchling-sdk 1.25.0(PyPI 上的最新穩定版本)。文件:benchling.com/sdk-docs。平台指南:docs.benchling.com。

何時使用此技能

此技能應在以下情況使用:

  • 使用 Benchling 的 Python SDK 或 REST API 時
  • 管理生物序列(DNA、RNA、蛋白質)和註冊實體
  • 自動化庫存操作(樣品、容器、位置、移轉)
  • 建立或查詢電子實驗室筆記本條目
  • 建置工作流程自動化或 Benchling Apps
  • 在 Benchling 與外部系統之間同步資料
  • 查詢 Benchling 資料倉儲以進行分析
  • 使用 AWS EventBridge 設置事件驅動的整合

核心功能

七個功能領域及其程式碼範例位於 references/core_capabilities.md:

  1. 認證與設定 — API 金鑰和 OAuth 應用程式驗證;請參閱 references/authentication.md。
  2. 註冊與實體管理 — DNA 和胺基酸序列、自訂實體、綱要 與註冊。
  3. 庫存管理 — 容器、盒子、盤子、位置和移轉。
  4. 筆記本與文件 — 條目、日常筆記和結構化表格。
  5. 工作流程與自動化 — 任務、流程圖和分析運行。
  6. 事件與整合 — EventBridge 訂閱;請參閱 references/eventbridge.md。
  7. 資料倉儲與分析 — 對倉儲的 SQL 存取。

端點和 SDK 的詳細資訊位於 references/api_endpoints.md 和 references/sdk_reference.md。

最佳實務

錯誤處理

SDK 會自動重試失敗的請求:

# 自動重試 429、502、503、504 狀態碼
# 最多重試 5 次,並採用指數退避
# 必要時可自訂重試行為
from benchling_sdk.retry import RetryStrategy

benchling = Benchling(
    url=tenant_url,
    auth_method=ApiKeyAuth(api_key),
    retry_strategy=RetryStrategy(max_retries=3),
)

分頁效率

使用生成器進行記憶體高效的分頁:

# 基於生成器的迭代
for page in benchling.dna_sequences.list():
    for sequence in page:
        process(sequence)

# 檢查估計數量,無需載入所有頁面
total = benchling.dna_sequences.list().estimated_count()

Schema 欄位輔助工具

使用 fields() 輔助工具處理自訂 Schema 欄位:

# 將字典轉換為 Fields 物件
custom_fields = benchling.models.fields({
    "concentration": "100 ng/μL",
    "date_prepared": "2025-10-20",
    "notes": "高品質製備"
})

向前相容性

SDK 能優雅地處理未知列舉值與型別:

  • 保留未知的列舉值
  • 無法識別的多型型別會回傳 UnknownType
  • 允許與較新版本的 API 協作

安全考量

  • 切勿將 API 金鑰或 OAuth 密碼提交至版本控制
  • 僅讀取命名環境變數(BENCHLING_TENANT_URL、BENCHLING_API_KEY 等)
  • 網路呼叫僅路由至您的租用戶 URL
  • 若金鑰洩露請立即輪換;對多用戶生產應用程式請使用 OAuth
  • 在開發者控制台中為應用程式授予最低必要權限

資源

references/

詳細的參考文件提供深入資訊:

  • authentication.md - 全面的認證指南,包含 OIDC、安全最佳實務與憑證管理
  • sdk_reference.md - 詳細的 Python SDK 參考,包含進階模式、範例與所有實體型別
  • api_endpoints.md - REST API 端點參考,用於不使用 SDK 的直接 HTTP 呼叫
  • eventbridge.md - EventBridge 設定、事件負載綱要、規則範例、Lambda 處理器、驗證與復原

根據特定整合需求載入這些參考文件。

常見使用案例

1. 批次實體匯入:

# 從 FASTA 檔案匯入多個序列
from Bio import SeqIO

for record in SeqIO.parse("sequences.fasta", "fasta"):
    benchling.dna_sequences.create(
        DnaSequenceCreate(
            name=record.id,
            bases=str(record.seq),
            is_circular=False,
            folder_id="fld_abc123"
        )
    )

2. 庫存審計:

# 列出特定位置中的所有容器
containers = benchling.containers.list(
    parent_storage_id="box_abc123"
)

for page in containers:
    for container in page:
        print(f"{container.name}: {container.barcode}")

3. 工作流程自動化:

# 更新工作流程中所有待處理任務
tasks = benchling.workflow_tasks.list(
    workflow_id="wf_abc123",
    status="pending"
)

for page in tasks:
    for task in page:
        # 執行自動檢查
        if auto_validate(task):
            benchling.workflow_tasks.update(
                task_id=task.id,
                workflow_task=WorkflowTaskUpdate(
                    status_id="status_complete"
                )
            )

4. 資料匯出:

# 匯出所有具有特定屬性的序列
sequences = benchling.dna_sequences.list()
export_data = []

for page in sequences:
    for seq in page:
        if seq.schema_id == "target_schema_id":
            export_data.append({
                "id": seq.id,
                "name": seq.name,
                "bases": seq.bases,
                "length": len(seq.bases)
            })

# 儲存為 CSV 或寫入資料庫
import csv
with open("sequences.csv", "w") as f:
    writer = csv.DictWriter(f, fieldnames=export_data[0].keys())
    writer.writeheader()
    writer.writerows(export_data)

其他資源

引用科學代理技能

此技能是 K-Dense 所開發的「科學代理技能」的一部分。若其對稿件、報告、簡報或程式碼發布有實質貢獻,請將該論文加入參考文獻或軟體章節,並告知使用者您已這麼做:

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

務必引用當前版本。DOI 和 https://arxiv.org/abs/2609.00065 會解析至最新的 arXiv 版本,因此切勿附加像是 v1 的版本後綴。當網路連線可用時,請在撰寫參考文獻前擷取 https://arxiv.org/abs/2609.00065(或 http://export.arxiv.org/api/query?id_list=2609.00065),並從該記錄中取得作者列表、年份與版本。若該記錄列出了期刊參考文獻或出版商 DOI,請改為引用已發布的版本。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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`.

aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

alphagenome

Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

analytical-method-validation

Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include

anndata

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

arbor

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g.

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

autoskill

Observe the user

bgpt-paper-search

Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

bids

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biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

bulk-rnaseq

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g.

cellxgene-census

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

cirq

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

citation-management

Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

clinical-decision-support

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

clinical-reports

Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.

cobrapy

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

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