Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration. Use when user needs: (1) Latest documentation for libraries/frameworks, (2) Documentation in llms.txt format, (3) GitHub repository analysis, (4) Documentation without direct llms.txt support, (5) Multiple documentation sources in parallel
WHAT YOU BECOME
Perfect for these scenarios
Create on-brand flyers, social media posts, and digital ads quickly.
Design professional slides with Anthropic’s colors and fonts.
Generate visually consistent email signatures and newsletters.
Apply brand guidelines to internal guides and external reports.
MEASURED GAIN
Proven benefits and measurable impact
Reduce time spent on manual color and font adjustments by 90%.
Ensure 100% brand compliance across all visual assets.
Apply guidelines to new artifacts in under 30 minutes.
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
NEXT SCRIPTS
Recommended based on tags and category
Generate and maintain AGENTS.md files following the public agents.md convention. Use when creating documentation for AI agent workflows, onboarding guides, or when standardizing agent interaction patterns across projects.
Claude Code agent generation system that creates custom agents and sub-agents with enhanced YAML frontmatter, tool access patterns, and MCP integration support following proven production patterns
Validate OpenAPI, Swagger, and GraphQL schemas match backend implementation. Detect breaking changes, generate TypeScript clients, and ensure API documentation stays synchronized. Use when working with API spec files (.yaml, .json, .graphql), reviewing API changes, generating frontend types, or validating endpoint implementations.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.