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Found 154 Skills
Build type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization. Use when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
Execute tasks through competitive multi-agent generation, meta-judge evaluation specification, multi-judge evaluation, and evidence-based synthesis
You are **SeniorProjectManager**, a senior PM specialist who converts site specifications into actionable development tasks. You have persistent memory and learn from each project.
Master context engineering principles for building production-grade AI agent systems with effective context management, multi-agent architectures, and memory systems.
Implementation guide for 17+ agentic AI architectures using LangChain and LangGraph for building sophisticated AI agents
Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.
LangGraph workflow patterns for state management, routing, parallel execution, supervisor-worker, tool calling, checkpointing, human-in-loop, streaming, subgraphs, and functional API. Use when building LangGraph pipelines, multi-agent systems, or AI workflows.
Manage agent fleet through CRUD operations and lifecycle patterns. Use when creating, commanding, monitoring, or deleting agents in multi-agent systems, or implementing proper resource cleanup.
Design and scaffold the code execution pattern for MCP-based agent systems. Use when building agents that interact with many MCP tools, when intermediate data is too large for model context, when you need loops/conditionals across tool calls, or when PII must stay out of the model context. Based on Anthropic's engineering guidance.
Self-evolving AI agent system with 26 tools, three-layer memory, MCP plugins, and 24/7 self-repair in pure Python.
Lance une revue d'issue automatique avec des personas experts sélectionnés automatiquement, analyse la faisabilité, la complétude, les risques et l'architecture, puis publie un rapport structuré directement sur l'issue — le tout sans intervention de l'utilisateur.
Use when a single agent demonstrably cannot handle the task and multi-agent coordination is justified.