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Found 1,660 Skills
Review existing Perses dashboards for quality: fetch via MCP or API, analyze panel layout, query efficiency, variable usage, datasource configuration. Generate improvement report. Optional --fix mode. 4-phase pipeline: FETCH, ANALYZE, REPORT, FIX. Use for "review perses dashboard", "audit dashboard", "perses dashboard quality". Do NOT use for creating new dashboards (use perses-dashboard-create).
Generate a project-specific CLAUDE.md by analyzing the current repository's code, build system, and architecture. 4-phase pipeline: SCAN, DETECT, GENERATE, VALIDATE. Auto-detects language/framework and enriches output with domain-specific conventions (e.g., go-sapcc-conventions for sapcc Go repos). Use for "generate claude.md", "create claude.md", "init claude.md", "bootstrap claude.md", "make claude.md". Do NOT use for improving an existing CLAUDE.md (use claude-md-improver instead).
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 using Azure Load Testing with VNets/private endpoints, JMeter/Locust/Playwright, CI/CD pipelines, or Playwright Workspaces, and other Azure App Testing related development tasks. Not for Azure Test Plans (use azure-test-plans), Playwright Workspaces (use azure-playwright-workspaces), Azure DevOps (use azure-devops), Azure App Service (use azure-app-service).
Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Azure ML pipelines, AutoML, managed online/batch endpoints, prompt flow, or MLflow deployments, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Data Science Virtual Machines (use azure-data-science-vm).
Design the domain model for the Stitch SDK. Use when mapping MCP tools to domain classes and bindings in domain-map.json. This is Stage 2 of the generation pipeline.
CI/CD pipeline patterns for GitHub Actions, GitLab CI, testing strategies, and deployment automation
This skill should be used when the user asks to "fix the issues", "optimize existing content", "create new content for AI visibility", "run Morphiq Build", "generate schema markup", "create an llms.txt file", "run the content lab", or mentions building content fixes, generating schema, rewriting content for AI citations, or creating policy files. Consumes a Prioritized Roadmap (or user prompt, or existing content) and produces build artifacts through a 6-step content lab pipeline.
Sets up GitOps CI/CD pipelines for TrueFoundry using tfy apply. Supports GitHub Actions, GitLab CI, and Bitbucket Pipelines.
AI prompt orchestration CLI using reusable Patterns. Use for YouTube summarization, document analysis, content extraction, code explanation, writing assistance, and any AI task via stdin/stdout piping across 20+ providers.
Instrument, trace, evaluate, and monitor LLM applications and AI agents with LangSmith. Use when setting up observability for LLM pipelines, running offline or online evaluations, managing prompts in the Prompt Hub, creating datasets for regression testing, or deploying agent servers. Triggers on: langsmith, langchain tracing, llm tracing, llm observability, llm evaluation, trace llm calls, @traceable, wrap_openai, langsmith evaluate, langsmith dataset, langsmith feedback, langsmith prompt hub, langsmith project, llm monitoring, llm debugging, llm quality, openevals, langsmith cli, langsmith experiment, annotate llm, llm judge.
Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.
Create, configure, validate, deploy, run, and manage DABs — Declarative Automation Bundles (formerly Databricks Asset Bundles) — for Databricks resources including dashboards, jobs, pipelines, alerts, volumes, and apps