Skill UI

Use Composio’s Poptin toolkit with Rube MCP to automate tool discovery, connection checks, and multi-tool execution workflows for any Poptin tasks while always querying RUBE_SEARCH_TOOLS first to respect live schemas.

Automates Postgrid operations via Composio’s toolkit over Rube MCP by laying out the connection, discovery, execution, and schema-compliance steps so agents always search tools first, verify the active Postgrid link, and run workflows with correct memory and session handling.

Automates Precoro operations via Composio’s Rube MCP workflow: always search tools first, manage the Precoro connection, then execute schema-driven calls (with memory/session handling and pagination) through multi-tool executions.

Automates Prerender tasks via Composio’s Rube MCP pipeline, covering tool discovery, connection checks, schema-compliant executions, and session-aware workflows that always search for current schemas before running multi-tool operations.

Guides SaaS teams through value-based pricing, tier design, research techniques (Van Westendorp, MaxDiff) and pricing-page best practices to capture customer willingness to pay and support monetization decisions.

Automates Prisma operations through Composio's Rube MCP by always discovering updated tool schemas, ensuring active toolkit connections, and executing workflows with schema-compliant arguments; includes quick references for search, connect, and execute steps.

Automate headless CMS operations in Prismic via Claude Code, letting you query documents, search text, inspect custom types, and manage refs through the Composio integration for scripted content workflows.

A comprehensive toolkit for modern product management. It guides users through essential workflows, including feature prioritization using RICE, deep customer discovery analysis from interviews, and structured PRD creation. Ideal for defining product strategy, synthesizing user research, and documenting requirements from concept to launch.

A guide to building robust projections and read models from continuous event streams. This pattern is essential for implementing CQRS read sides, creating materialized views, optimizing query performance, and building complex search indexes in event-sourced architectures. Use it when data needs to be transformed and aggregated from raw events into optimized query structures.

This skill provides comprehensive methodologies for capturing, analyzing, and documenting network protocols. It is crucial for security researchers, developers ensuring interoperability, and engineers debugging complex network communications by understanding the underlying data exchange mechanisms.

PyTorch Lightning is a high-level framework built on PyTorch that significantly reduces boilerplate code in deep learning training loops. It automatically handles device management, distributed training (DDP, FSDP, DeepSpeed), mixed precision, and advanced callbacks. It allows researchers to scale models from local machines to supercomputers with minimal code changes, enabling clean, scalable, and best-practice deep learning implementations.

Pyvene is a declarative framework designed for performing causal interventions on PyTorch neural networks. It allows researchers to conduct advanced experiments such as activation patching, causal tracing (ROME-style), and interchange intervention training (IIT). Use this library when you need to test causal hypotheses about model behavior, deeply interpret model components, or ensure reproducibility in advanced AI research.