Skill UI

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

Artifacts Builder scaffolds Claude.ai-ready React+TypeScript projects with Tailwind/shadcn/ui, automating init and Parcel bundling into a shareable single HTML artifact for complex UI experiences.

Prepares codebases for Trail of Bits security reviews by setting goals, running static analysis, expanding test coverage, pruning dead code, documenting build scope/access, and generating flowcharts, user stories, glossaries, and other reviewer-ready artifacts.

Prepares codebases for Trail of Bits security reviews by setting goals, running static analysis, expanding test coverage, pruning dead code, documenting build scope/access, and generating flowcharts, user stories, glossaries, and other reviewer-ready artifacts.

A comprehensive Python SDK for interacting with Azure Container Registry (ACR). This tool allows developers to manage the full container lifecycle, including listing repositories, handling tags, manipulating manifests, downloading artifacts, and managing container images within Azure's cloud environment. Ideal for implementing robust CI/CD pipelines and container automation.

A comprehensive Python SDK for interacting with Azure Container Registry (ACR). This tool allows developers to manage the full container lifecycle, including listing repositories, handling tags, manipulating manifests, downloading artifacts, and managing container images within Azure's cloud environment. Ideal for implementing robust CI/CD pipelines and container automation.

Automates enterprise backlog grooming by following best-practice workflows, generating ready-to-use artifacts, and validating compliance patterns when projects mention the backlog grooming assistant trigger.

Automate CircleCI CI/CD workflows via Composio’s CircleCI toolkit over Rube MCP: trigger pipelines, monitor workflows and jobs, fetch artifacts, and review test metadata while adhering to required tool schemas and authentication flows.

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

This skill simulates a documentation expert, capable of generating comprehensive, maintainable technical documentation directly from source code. It analyzes codebases to produce various artifacts, including API documentation, system architecture diagrams, comprehensive user guides, and technical reference manuals. It is essential for standardizing documentation and building automated documentation pipelines in large, complex projects.

This skill provides comprehensive management for the entire track lifecycle within a Conductor system. It allows users to perform critical operations such as archiving, restoring, deleting, renaming tracks, and cleaning up orphaned artifacts. Use it when managing track status across active, completed, or archived states to ensure data integrity and system organization.

This skill validates the completeness, consistency, and correctness of Conductor project artifacts. It checks for the existence of required directories, verifies essential project documentation (like index.md, product.md, etc.), and confirms adherence to established naming and status patterns. Use this tool after setup, when diagnosing issues, or before implementation to ensure the project context is sound.