AgentDB Vector Search

AgentDB Memory Patterns

proffesor-for-testing

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

OpenAI API

jezweb

Complete guide for OpenAI's traditional/stateless APIs: Chat Completions (GPT-5, GPT-4o), Embeddings, Images (DALL-E 3), Audio (Whisper + TTS), and Moderation. Includes both Node.js SDK and fetch-based approaches for maximum compatibility. Use when: integrating OpenAI APIs, implementing chat completions with GPT-5/GPT-4o, generating text with streaming, using function calling/tools, creating structured outputs with JSON schemas, implementing embeddings for RAG, generating images with DALL-E 3, transcribing audio with Whisper, synthesizing speech with TTS, moderating content, deploying to Cloudflare Workers, or encountering errors like rate limits (429), invalid API keys (401), function calling failures, streaming parse errors, embeddings dimension mismatches, or token limit exceeded. Keywords: openai api, chat completions, gpt-5, gpt-5-mini, gpt-5-nano, gpt-4o, gpt-4-turbo, openai sdk, openai streaming, function calling, structured output, json schema, openai embeddings, text-embedding-3, dall-e-3, image generation, whisper api, openai tts, text-to-speech, moderation api, openai fetch, cloudflare workers openai, openai rate limit, openai 429, reasoning_effort, verbosity

AgentDB Memory Patterns

ruvnet

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

AgentDB Vector Search

ruvnet

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

Creating and Optimizing Claude Code Skills

modu-ai

Design reusable knowledge capsules with progressive disclosure (metadata → content → resources). Apply freedom levels (high/medium/low), create examples, validate YAML. Use when building domain-specific guidance or automating recurring patterns.

Designing Slash Commands for Claude Code

modu-ai

Create and optimize slash commands with proper argument parsing, tool permissions, and agent orchestration. Use when building workflow entry points, automation commands, or user-facing shortcuts.

brand-guidelines

ComposioHQ

Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.

canvas-design

mrgoonie

Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.

data-ml

baz-scm

Competence in data analytics and machine learning, enabling developers to build data-driven features and integrate AI/ML capabilities.

brand-guidelines

LeastBit

将 Anthropic 官方品牌配色与字体应用到需要呈现 Anthropic 风格的任何产出物上。当涉及品牌配色、风格规范、视觉格式或公司设计标准时请使用本技能。

senior-prompt-engineer

alirezarezvani

World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.

senior-ml-engineer

alirezarezvani

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.