Skill UI

Automates the end-to-end training of machine learning models by analyzing datasets, selecting suitable algorithms, tuning parameters, performing cross-validated training, and emitting performance metrics plus a saved model artifact.

TransformerLens is the standard library for performing mechanistic interpretability research on large language models. It provides clean interfaces to inspect and manipulate transformer internals—such as attention patterns, residual streams, and MLP outputs—via activation caching and HookPoints. This tool is essential for reverse-engineering model algorithms, performing causal tracing, and studying the internal circuits responsible for model behavior.

Automates Trello boards, cards, lists, assignments, searches, comments, and attachments through Composio’s Rube MCP, so teams can script workflows like card creation, movement, member assignment, and data retrieval without manual clicks.

Guides Claude to automatically tune machine learning model hyperparameters via grid, random, or Bayesian search, generating runnable Python code, executing validations, and reporting best configurations plus performance metrics for accuracy or other goals.

Configure Turborepo caching for monorepo projects with templates for global and package pipelines, remote cache usage, self-hosted servers, and scoped filters to speed CI/CD builds.

Automates Twelve Data tasks by guiding the Rube MCP workflow from schema discovery through connection checks to multi-tool execution, ensuring compliant inputs before invoking each Composio toolkit operation.

Guide to optimizing TwinMind transcription, balancing accuracy and speed through audio preprocessing, scenario-specific model configs, streaming tweaks, and caching to cut redundant work.

Analyzes tweet drafts through Twitter’s recommendation architecture to rewrite and edit content, explain what the algorithms favor, and surface engagement tactics that align with Real-graph, SimClusters, TwHIN, and Tweepcred signals for broader reach.

Automates user journey mapper tasks in the Visual Content domain by triggering whenever you mention user journey mapper needs, then guiding each step, applying best practices, and generating validated diagrams, charts, and documentation-ready material.

Guides Vast.ai users through caching, batching, Docker optimizations, rsync transfers, and lifecycle scripting to improve API responsiveness, data moves, and GPU training throughput for automated workloads.

Fine-tune Vercel API integrations by measuring latency, introducing caching, batching, and connection pooling, and applying async-friendly patterns to keep throughput high even under heavy request loads.

Vercel Engineering’s authoritative React and Next.js performance guide, offering prioritized rules for waterfalls, bundle size, data fetching, memoization, rendering, caching, and advanced patterns.