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Found 1,316 Skills
Netlify JAMstack deployment platform with serverless functions, forms, and identity
Production incident response procedures for Python/React applications. Use when responding to production outages, investigating error spikes, diagnosing performance degradation, or conducting post-mortems. Covers severity classification (SEV1-SEV4), incident commander role, communication templates, diagnostic commands for FastAPI/ PostgreSQL/Redis, rollback procedures, and blameless post-mortem process. Does NOT cover monitoring setup (use monitoring-setup) or deployment procedures (use deployment-pipeline).
Document deployment processes, infrastructure setup, CI/CD pipelines, and configuration management. Use when creating deployment guides, infrastructure docs, or CI/CD documentation.
Expert knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing ADF pipelines, mapping data flows, SHIR/SSIS IR, SAP CDC, or CI/CD with ARM/DevOps, and other Azure Data Factory related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics), Azure Data Explorer (use azure-data-explorer).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Harden configuration and defaults for safer deployment. Use when a mid-level developer needs to reduce misconfig risks.
Expert guidance on the Google Agent Development Kit (ADK) for Python. Use this skill when the user asks about building agents, using tools, streaming, callbacks, tutorials, deployment, or advanced architecture with the Google ADK in Python.
CI/CD pipeline design, optimization, DevSecOps security scanning, and troubleshooting. Use for creating workflows, debugging pipeline failures, implementing SAST/DAST/SCA, optimizing build performance, implementing caching strategies, setting up deployments, securing pipelines with OIDC/secrets management, and troubleshooting common issues across GitHub Actions, GitLab CI, and other platforms.
This skill should be used when the user requests to add a new third-party API service to the AWS billing/quota monitoring system. It handles the complete onboarding process including adapter creation, Lambda deployment, CloudWatch alarms, Dashboard updates, and verification. Triggers on requests mentioning "add service monitoring", "monitor API balance", "setup quota alerts", "add to billing dashboard", or similar service integration requests.
Salesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use querying-soql), Apex test execution (use running-apex-tests), or metadata deployment (use deploying-metadata).
Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.