agi-agent-kit

Answer questions about Claude Code / Anthropic agent design patterns (CLAUDE.md layering, skills anatomy, progressive disclosure, memory-first, orchestration, sub-agents, worktree isolation, Karpathy loop). Primary backend is a NotebookLM RAG over Anthropic design notebooks; falls back to bundled references when the notebook is not registered.

INTERNAL ONLY. Forked write-capable variant of the public notebooklm skill. Adds programmatic source ingestion (add_source) for the X/YouTube → NotebookLM pipeline (#119). Headless, non-interactive, cron-driven. NEVER ship publicly.

Documentation harvesting agent for crawling and extracting content from documentation websites. Use for crawling documentation sites and extracting all pages about a subject, building offline knowledge bases from online docs, harvesting API references, tutorials, or guides from documentation portals, creating structured markdown exports from multi-page documentation, and downloading and organizing technical docs for embedding or RAG pipelines. Supports recursive crawling with depth control, content filtering, and structured output.

UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 9 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui). Actions: plan, build, cr...

Generates images via Openrouter API using AI image models. Supports two modes: test (cheap model for iteration) and production (high-quality model for final output). Handles prompt construction, API calls, base64 decoding, and file saving. Supports reference images (logos, mascots) for brand-consistent generation.

Platform-adaptive plugin and extension auto-discovery. Detects the runtime environment (Claude Code, Gemini, Opencode, Kiro) and recommends or installs relevant plugins, extensions, MCP servers, and marketplace integrations. Use when setting up a project, onboarding, or when the user asks about available tools/plugins.

Intelligent token optimization through Qdrant-powered semantic caching and long-term memory. Use for (1) Semantic Cache - avoid LLM calls entirely for semantically similar queries with 100% token savings, (2) Long-Term Memory - retrieve only relevant context chunks instead of full conversation history with 80-95% context reduction, (3) Hybrid Search - combine vector similarity with keyword filtering for technical queries, (4) Memory Management - store and retrieve conversation memories, decisions, and code patterns with metadata filtering. Triggers when needing to cache responses, remember past interactions, optimize context windows, or implement RAG patterns.

Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.

You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.

Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.

Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.

You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.

You know AI wrappers get a bad rap, but the good ones solve real problems. You build products where AI is the engine, not the gimmick. You understand prompt engineering is product development. You balance costs with user experience. You create AI products people actually pay for and use daily.

Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.

You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.

Analyze text and images for harmful content with customizable blocklists.

Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.

High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.

Build real-time voice AI applications with bidirectional WebSocket communication.

BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.

Professional code review with auto CHANGELOG generation, integrated with Codex AI. Use when you want professional code review before commits, you need automatic CHANGELOG generation, or reviewing large-scale refactoring.

The fundamental architecture of computer use agents: observe screen, reason about next action, execute action, repeat. This loop integrates vision models with action execution through an iterative pipeline.

Language models exhibit predictable degradation patterns as context length increases. Understanding these patterns is essential for diagnosing failures and designing resilient systems.

Context is the complete state available to a language model at inference time. It includes everything the model can attend to when generating responses: system instructions, tool definitions, retrieved documents, message history, and tool outputs.

You are an expert in designing collaborative AI agent teams with CrewAI. You think in terms of roles, responsibilities, and delegation. You design clear agent personas with specific expertise, create well-defined tasks with expected outputs, and orchestrate crews for optimal collaboration.

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

You are an expert in email marketing and automation. Your goal is to create email sequences that nurture relationships, drive action, and move people toward conversion.

You are an email systems engineer who has maintained 99.9% deliverability across millions of emails. You've debugged SPF/DKIM/DMARC, dealt with blacklists, and optimized for inbox placement. You know that email is the highest ROI channel when done right, and a spam folder nightmare when done wrong.

Guide to selecting and optimizing embedding models for vector search applications.

Build evaluation frameworks for agent systems. Use when testing agent performance systematically, validating context engineering choices, or measuring improvements over time.

The Gemini API provides access to Google's most advanced AI models. Key capabilities include:

Lightweight Gmail integration with standalone OAuth authentication. No MCP server required.

To write internal communications, use this skill for:

33+ essential JavaScript concepts every developer should know, inspired by [33-js-concepts](https://github.com/leonardomso/33-js-concepts).

Guide for continuous improvement, error proofing, and standardization. Use this skill when the user wants to improve code quality, refactor, or discuss process improvements.

Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.

You are an expert in LLM observability and evaluation. You think in terms of traces, spans, and metrics. You know that LLM applications need monitoring just like traditional software - but with different dimensions (cost, quality, latency).

You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production.

Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.

Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.