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Everything your AI needs, in one place.
Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.
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A service that does a whole job for you - research, coding, support - and reports back.
Step-by-step instructions an AI follows for one kind of task. Install once, reuse everywhere.
A voice and set of rules layered onto any model: tone, audience, do's and don'ts.
A ready-to-use prompt with fill-in-the-blank variables and notes on when it works best.
A complete routing setup - models, rules and settings - in one file you can apply in a minute.
A single function an AI can call: a calculator, a search, a database lookup.
A language model endpoint with its price, speed and quality declared so the router can compare it.
vector-search-patterns
Implement vector similarity search with embedding generation, index selection, and hybrid retrieval strategies. Covers ChromaDB, pgvector, FAISS, and RAG pipeline design. Triggers on vector search, em
ai-engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for
automatic-stateful-prompt-improver
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimi
clip-aware-embeddings
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car mo
agent-memory
Add persistent memory to AI coding agents — file-based, vector, and semantic search memory systems that survive between sessions. Use when a user asks to "remember this", "add memory to my agent", "pe
chromadb
Assists with storing, searching, and managing vector embeddings using ChromaDB. Use when building RAG pipelines, semantic search engines, or recommendation systems. Trigger words: chromadb, chroma, ve
cloudflare-ai
You are an expert in Cloudflare Workers AI, the serverless AI inference platform running on Cloudflare's global network. You help developers run LLMs, embedding models, image generation, speech-to-tex
cohere-api
Cohere API for enterprise NLP — embeddings, reranking, RAG, and text generation. Use when building RAG pipelines, semantic search, document reranking, or enterprise NLP applications. Command R+ excels
openai-sdk
Integrate OpenAI APIs into applications. Use when a user asks to add GPT or ChatGPT to an app, generate text with OpenAI, build a chatbot, use GPT-4 or o1 models, generate embeddings, use function cal
pgvector
Store and search vector embeddings in PostgreSQL with pgvector — no separate vector database needed. Use when someone asks to "vector search in Postgres", "store embeddings", "pgvector", "similarity s
pinecone
Pinecone is a managed vector database for AI and machine learning applications. Learn to create indexes, upsert embeddings, query by similarity, use namespaces and metadata filtering for semantic sear
qdrant
You are an expert in Qdrant, the high-performance vector search engine written in Rust. You help developers build semantic search, RAG retrieval, recommendation systems, and anomaly detection with bil
together-ai
Cloud platform for running open-source AI models. Provides inference APIs for LLMs, image models, and embedding models. Supports fine-tuning on custom data, OpenAI-compatible API format, and competiti
memory-qdrant
Local semantic memory with Qdrant and Transformers.js. Store, search, and recall conversation context using vector embeddings (fully local, no API keys).
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production c
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable
rag-architect
Design and optimize RAG pipelines. Document chunking strategies, embedding model selection, vector database choices, retrieval strategies, query transformation, context optimization, evaluation framew
llm-ops
LLM-OPS -- IA de Producao workflow skill. Use this skill when the user needs LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qual
fair-esm
Run the FAIR `fair-esm` package — the original Meta Fundamental AI Research reference implementation of the ESM family of protein language and structure models. Use this skill when: (1) Extracting per
nlp-engineering
Use this skill when building NLP pipelines, implementing text classification, semantic search, embeddings, or summarization. Triggers on text preprocessing, tokenization, embeddings, vector search, na
milvus
Operate Milvus vector database with pymilvus — collections, vector search, hybrid search, indexes, RBAC, partitions, and more via Python code.
langchain
Build production-ready LLM applications with chains, agents, memory, tools, and RAG pipelines using the LangChain framework
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Install
One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.
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Prefer the terminal? osr stack apply registry://starter installs the starter template.