LangSmith Agent Builder - No-code platform for creating AI agents with built-in tools (Gmail, Slack, GitHub, Linear), OAuth integrations, MCP server support, Slack deployment, and programmatic invocation via LangGraph SDK
Create or update agent skills following the agent skill specification. Use when user asks to create a new skill or update an existing skill.
Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".
This skill provides reusable implementation patterns extracted from the better-chatbot project for custom AI chatbot deployments. Use this skill when building AI chatbots with server action validators, tool abstraction systems, workflow execution, or multi-AI provider integration in your own projects (not contributing to better-chatbot itself). Use when: building AI chatbot features, implementing server action validators, creating tool abstraction layers, setting up multi-AI provider support, building workflow execution systems, adapting better-chatbot patterns to custom projects Keywords: AI chatbot patterns, server action validators, tool abstraction, multi-AI providers, workflow execution, MCP integration, validated actions, tool type checking, Vercel AI SDK patterns, chatbot architecture
Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (AI-powered intent analysis, keyword matching), enforcement levels (validation only), hook mechanisms (UserPromptSubmit with auto-injection), session tracking, and the 500-line rule.
Implement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external data. Covers embedding strategy, vector stores, retrieval pipelines, and evaluation.
Decision guide for when to query Bluera Knowledge stores vs using Grep/Read on current project. Query BK for library/dependency questions and reference material. Use Grep/Read for current project code, debugging, and implementation details. Includes setup instructions and mental model.
Expert C4 Container-level documentation specialist. Synthesizes Component-level documentation into Container-level architecture, mapping components to deployment units, documenting container interfaces as APIs, and creating container diagrams. Use when synthesizing components into deployment containers and documenting system deployment architecture.
Open protocol for AI agent interoperability enabling standardized communication between agents, applications, and humans across different frameworks
Cloudflare Agents SDK - Build and deploy AI-powered agents on Cloudflare's edge with real-time WebSocket communication, persistent state, SQL storage, task scheduling, MCP integration, and human-in-the-loop workflows.
Guide on optimizing Bluera Knowledge search results through proper intent selection, detail level strategies, result limiting, and store filtering. Teaches when to use minimal vs contextual vs full detail, and how to choose the right search intent for different query types.
Model Context Protocol (MCP) - Open standard for connecting AI applications to external data sources, tools, and systems. Use for building MCP servers (tools, resources, prompts), clients, understanding protocol architecture, and implementing AI integrations.
Comprehensive toolkit for managing large Claude Code skill collections including bulk downloading from GitHub, organizing into categories, detecting and removing duplicates, consolidating skills, and maintaining clean skill repositories with 100+ skills.
Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, and integrating with deep RL workflows. This skill should be used when users request RL algorithm implementation, agent training, environment design, or RL experimentation.
Compares old vs new prompts across test cases with diff summaries, stability metrics, breakage analysis, and fix suggestions. Use for "prompt testing", "A/B testing prompts", "prompt versioning", or "quality regression".
This skill provides complete coverage of Google Gemini embeddings API (gemini-embedding-001) for building RAG systems, semantic search, document clustering, and similarity matching. Use when implementing vector search with Google's embedding models, integrating with Cloudflare Vectorize, or building retrieval-augmented generation systems. Covers SDK usage (@google/genai), fetch-based Workers implementation, batch processing, 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY, etc.), dimension optimization (128-3072), and cosine similarity calculations. Prevents 8+ embedding-specific errors including dimension mismatches, incorrect task types, rate limiting issues (100 RPM free tier), vector normalization mistakes, text truncation (2,048 token limit), and model version confusion. Includes production-ready RAG patterns with Cloudflare Vectorize integration, chunking strategies, and caching patterns. Token savings: ~60%. Production tested. Keywords: gemini embeddings, gemini-embedding-001, g
Proactive recovery using plan mode and subagents. After 1-2 failed attempts, STOP trying variations. Enter plan mode and launch parallel Explore/Plan agents to find idiomatic solutions instead of spinning wheels.
Comprehensive Twilio Voice API assistance with AI integration patterns
Multi-LLM collaborative brainstorming and planning. Use when user explicitly requests consultation with multiple AI models (ChatGPT, Gemini, other LLMs) before presenting an implementation plan, or asks to "consult the council", "ask other models", or "get perspectives from other AIs". Queries external LLM APIs, synthesizes their perspectives, and presents an adapted implementation plan.
