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Found 1,665 Skills
Manage editorial content pipeline through 6 stages: Ideas, Outlined, Drafted, Editing, Ready, Published. Use when user wants to view pipeline status, add ideas, move content between stages, schedule posts, or archive published content. Use for "content calendar", "pipeline status", "add idea", "schedule post", or "move to drafted". Do NOT use for creating Hugo content files, deploying posts, or modifying site configuration.
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).
Figma-to-code design handoff patterns including Figma Variables to design tokens pipeline, component spec extraction, Dev Mode inspection, Auto Layout to CSS Flexbox/Grid mapping, and visual regression with Applitools. Use when converting Figma designs to code, documenting component specs, setting up design-dev workflows, or comparing production UI against Figma designs.
Storybook 10 testing patterns with Vitest integration, ESM-only distribution, CSF3 typesafe factories, play() interaction tests, Chromatic TurboSnap visual regression, module automocking, accessibility addon testing, and autodocs generation. Use when writing component stories, setting up visual regression testing, configuring Storybook CI pipelines, or migrating from Storybook 9.
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).
Use when you need to apply functional programming principles in Java — including writing immutable objects and Records, pure functions, functional interfaces, lambda expressions, Stream API pipelines, Optional for null safety, function composition, higher-order functions, pattern matching for instanceof and switch, sealed classes/interfaces for controlled hierarchies, Stream Gatherers for custom operations, currying/partial application, effect boundary separation, and concurrent-safe functional patterns. Part of the skills-for-java project
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.
Assess data quality with checks for missing values, duplicates, type issues, and inconsistencies. Use for data validation, ETL pipelines, or dataset documentation.
This skill should be used when the user asks to "run a tracking cycle", "measure AI visibility", "check share of voice", "run Morphiq Track", "track citations", "check GEO score", "generate prompts", "run content creation workflow", or mentions monitoring LLM mentions, running content creation workflows, measuring brand visibility, or generating query fanout content. Queries multiple LLM providers, produces delta reports, and maintains MORPHIQ-TRACKER.md as the persistent state file for the entire pipeline.
Use Kotlin idioms safely in Android apps, including nullability, data classes, sealed types, extension functions, and collection pipelines.
Digistore24 integration. Manage Users, Roles, Organizations, Projects, Pipelines, Goals and more. Use when the user wants to interact with Digistore24 data.
Use when the user needs ML pipelines, statistical analysis, data preprocessing, feature engineering, model selection, experiment tracking, or data visualization. Triggers: dataset exploration, model training, feature engineering, hyperparameter tuning, experiment tracking setup, statistical hypothesis testing, visualization creation.