'pipelines' 검색 결과

Expert knowledge for Azure Boards development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when managing work items, Agile/Scrum processes, WIQL queries, Excel/Office integrations, or GitHub links, and other Azure Boards related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).

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Expert knowledge for Azure App Testing development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when running Azure Load Testing, Playwright Workspaces, JMeter/Locust tests, private endpoint/VNet tests, or CI/CD runs, and other Azure App Testing related development tasks. Not for Azure Test Plans (use azure-test-plans), Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure App Service (use azure-app-service).

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Expert knowledge for Azure Artifacts development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing feeds, upstream sources, views, .npmrc auth, .artifactignore, or GitHub Actions package publishing, and other Azure Artifacts related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Boards (use azure-boards).

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Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.

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Constructs secure, efficient CI/CD pipelines with supply chain security (SLSA), monorepo optimization, caching strategies, and parallelization patterns for GitHub Actions, GitLab CI, and Argo Workflows. Use when setting up automated testing, building, or deployment workflows.

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Manage Azure DevOps projects, work items, repos, PRs, pipelines, wikis, test plans, security alerts, variable groups, environments/approvals, branch policies, and attachments. Use when user asks to: manage sprints, create/update work items, list repos, create PRs, run pipelines, search code, manage wiki pages, check security alerts, manage variable groups, approve deployments, or configure branch policies. Covers 13 domains with 99 tools via REST API.

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Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

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Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

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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.

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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.

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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.

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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).

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Validate, lint, audit, or review azure-pipelines.yml — syntax, security, best practices.

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Generate/create/scaffold azure-pipelines.yml, stages, jobs, steps, or reusable templates.

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Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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GitLab CLI for managing issues, merge requests, CI/CD pipelines, and repositories. Use when: (1) Creating, reviewing, or merging MRs, (2) Managing GitLab issues, (3) Monitoring or triggering CI/CD pipelines, (4) Working with self-hosted GitLab instances, (5) Automating GitLab workflows from the command line. Requires GITLAB_TOKEN (recommend minimal scopes). The `glab api` command enables arbitrary API calls - use read-only tokens when possible.

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| Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

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Create portable, standards-based bioinformatics pipelines with Common Workflow Language (CWL). Use when building workflows that need maximum portability across execution platforms, sharing pipelines with collaborators using different systems, or contributing to community workflow registries.

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Design CI/CD pipelines that run test suites. Covers GitHub Actions and GitLab CI templates, parallelism and sharding, artifact management, flaky-test quarantine, test-result publishing, coverage quality gates, OIDC keyless deploy, and copy-paste workflows for Playwright, Jest, and multi-stage pipelines. Use when: "CI/CD," "GitHub Actions," "pipeline," "test in CI," "GitLab CI," "continuous integration," "test automation pipeline," "shard tests in CI." Not for: per-test flaky healing at runtime — use test-reliability; go/no-go release decisions and smoke-test checklists — use release-readiness; test-result dashboards and trend reporting — use qa-metrics. Related: playwright-automation, qa-metrics, test-reliability, coverage-analysis, release-readiness.

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Internal — for Boundless team members only. Monitor Boundless deployment pipelines (AWS CodePipeline + CodeBuild) on the ops account. Use when the user wants to track a deployment after merging a PR, check whether a commit has rolled out to staging/prod, diagnose a failed deployment, watch the status of a specific pipeline, or get prompted to approve a production rollout once staging succeeds. Do NOT use for service runtime debugging (use ops-logs-query) or for deploying dev infrastructure (use ops-infra-deploy).

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Data Version Control for ML projects. Track large datasets and models alongside Git, build reproducible ML pipelines, and run experiments with metric comparison. Works with any storage backend including S3, GCS, Azure, and local filesystems.

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Generate and optimize CI/CD pipelines for GitLab CI and CircleCI. Use when a user asks to set up GitLab CI, create a CircleCI pipeline, build a CI pipeline for GitLab, automate deployments with CircleCI, add test automation to GitLab, or configure continuous integration on non-GitHub platforms. For GitHub Actions pipelines, use the github-actions skill instead.

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Run autonomous AI-driven penetration tests on web applications using tools like Shannon, PentAGI, and similar frameworks. Use when tasks involve setting up automated penetration testing pipelines, combining AI agents with security tools (nmap, subfinder, nuclei, sqlmap), building autonomous exploit chains, generating pentest reports with proof-of-concept exploits, or integrating AI pentesting into CI/CD pipelines. Covers the full pentest lifecycle from reconnaissance to reporting using AI orchestration.

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Patterns for building robust, reproducible genomics analysis pipelines. Covers workflow managers, NGS data processing, variant calling, RNA-seq, and common bioinformatics pitfalls. Use when ", " mentioned.

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