3,522 件 ・ 関連度順
概要と使いどころ
Python-based workflow manager for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake resolves execution order and parallelism. Runs local, SLURM, LSF, AWS, GCP via profiles; per-rule conda/Singularity envs. For NGS pipelines, ML training, and multi-step file processing. Use Nextflow for Groovy dataflow or nf-core integration.
日本語の概要は準備中です。原文の説明を表示しています。
Dataflow workflow engine for scalable bioinformatics pipelines. Defines processes (containerized tasks) connected by channels; runs local, HPC (SLURM/SGE), cloud (AWS/GCP/Azure), or Kubernetes via a single config change. Powers nf-core. Use Snakemake for rule-based Python workflows; use Nextflow for containerized, cloud-native, and nf-core pipelines.
日本語の概要は準備中です。原文の説明を表示しています。
MaxQuant + Perseus proteomics pipeline: run MaxQuant for LFQ and SILAC; parse proteinGroups.txt in Python; filter contaminants/decoys; log2 + median-normalize; impute MNAR; t-test with FDR; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo-native processing; FragPipe/MSFragger for GPU-accelerated DB search.
日本語の概要は準備中です。原文の説明を表示しています。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.
日本語の概要は準備中です。原文の説明を表示しています。
When the user needs to set up or improve CI/CD pipelines — GitHub Actions, GitLab CI, deployment automation, or says "set up CI", "automate deployment", "add tests to pipeline", "fix my build".
日本語の概要は準備中です。原文の説明を表示しています。
When a founder needs to qualify inbound leads, define their ICP, build a lead scoring model, set MQL criteria, or route prospects through pipeline stages. Activate when the user mentions lead scoring, ICP, MQL, SQL, lead qualification, inbound leads, or pipeline design.
日本語の概要は準備中です。原文の説明を表示しています。
Build automated ETL (Extract-Transform-Load) pipelines for construction data. Process PDFs, Excel, BIM exports. Generate reports, dashboards, and integrate with other systems. Orchestrate with Airflow or n8n.
日本語の概要は準備中です。原文の説明を表示しています。
Build automated BIM validation pipelines for IFC/Revit data. Continuous validation against IDS, LOD requirements, COBie, and project-specific BEP standards.
日本語の概要は準備中です。原文の説明を表示しています。
Serverless compute for Databricks jobs, Lakeflow pipelines and Declarative Automation Bundles (DABs), with STANDARD or PERFORMANCE_OPTIMIZED and classic only where serverless cannot run the workload. Use when creating, deploying, scheduling or editing a job or pipeline, or deciding its compute. Invoke BEFORE writing a job spec. For migrating existing classic workloads, use databricks-serverless-migration.
日本語の概要は準備中です。原文の説明を表示しています。
Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
日本語の概要は準備中です。原文の説明を表示しています。
Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; PostgreSQL/MySQL CDC in PuPr) into Unity Catalog with serverless pipelines.
日本語の概要は準備中です。原文の説明を表示しています。
Databricks SQL (DBSQL) advanced features and SQL warehouse capabilities. This skill MUST be invoked when the user mentions: "DBSQL", "Databricks SQL", "SQL warehouse", "SQL scripting", "stored procedure", "CALL procedure", "materialized view", "CREATE MATERIALIZED VIEW", "pipe syntax", "|>", "geospatial", "H3", "ST_", "spatial SQL", "collation", "COLLATE", "ai_query", "ai_classify", "ai_extract", "ai_gen", "AI function", "http_request", "remote_query", "read_files", "Lakehouse Federation", "recursive CTE", "WITH RECURSIVE", "multi-statement transaction", "temp table", "temporary view", "pipe operator". SHOULD also invoke when the user asks about SQL best practices, data modeling patterns, or advanced SQL features on Databricks.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze pipeline health, identify stalled deals, predict close probability, and suggest actions to move deals forward. Improves forecast accuracy and prevents revenue leakage. Use when deals get stuck or forecast accuracy is poor.
日本語の概要は準備中です。原文の説明を表示しています。
Uses MCP Connectors to read Gmail inbound leads, score them by ICP fit, draft personalized responses, and log qualified leads to your CRM. Turns your inbox into an automated pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you have an existing pipeline DOT file that needs review, fixing, or validation. Use when validation produces warnings or errors, when a DOT file was hand-written or generated by another tool, or when you want to verify a pipeline covers 100% of a spec before running it.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you have a spec, design doc, structured plan, or prompt_plan and need to generate a pipeline Graphviz DOT file for headless autonomous building. The agent reads the spec, extracts build pipeline phases, determines tech stack, asks about human gates and model preferences, generates a validated DOT digraph with rich self-contained prompts, and saves it to the project root. Handles any language or framework.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a pipeline DOT file has passed structural validation but may still have flow inefficiencies, spec fidelity gaps, or information loss in prompts. Use after dotfile-audit or dotfile-from-spec when you want to deeply verify a pipeline captures everything from the original spec. Use when prompts might be missing critical domain rules, data models, or invariants.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to build a project but don't have a spec yet and need to brainstorm the idea into a design doc structured for pipeline DOT generation. Use when starting from a vague idea, project concept, or feature request that needs to become a headless autonomous build pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill for Hugging Face Diffusers tasks: pipeline inference, schedulers, adapters/loaders, training recipes, modular pipelines, conversion helpers, CLI checks, and repo maintenance.
日本語の概要は準備中です。原文の説明を表示しています。
CI/CD pipeline attack hunting (GitHub Actions focus) - pwn requests (pull_request_target), script injection, self-hosted runner takeover, cache poisoning, OIDC-to-cloud token theft, poisoned pipeline execution. Wiki-first, FIND schema output.
日本語の概要は準備中です。原文の説明を表示しています。
Validate, lint, audit, or review azure-pipelines.yml — syntax, security, best practices.
日本語の概要は準備中です。原文の説明を表示しています。
Generate/create/scaffold azure-pipelines.yml, stages, jobs, steps, or reusable templates.
日本語の概要は準備中です。原文の説明を表示しています。
Build automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured and unstructured data sources.
日本語の概要は準備中です。原文の説明を表示しています。