Volo
AdyTech99
An F/OSS solution combining AI with Wikipedia knowledge via a RAG pipeline
OPENAGENTSKILL / DIRECTORY
Find a skill for your next task. Explore tools for Codex, Claude Code, Cursor and more.
Find a skill for your next task. Preview examples where available.
Page 34 · 16 shown · 567 public entries
Results: 567
AdyTech99
An F/OSS solution combining AI with Wikipedia knowledge via a RAG pipeline
RihanArfan
Chat with PDF lets you ask questions to PDF documents. Built and deployed with NuxtHub, and powered by Cloudflare Workers AI and Vectorize.
anakin87
Mistral + Haystack: build RAG pipelines that rock 🤘
Aavache
A Web Crawler based on LLMs implemented with Ray and Huggingface. The embeddings are saved into a vector database for fast clustering and retrieval. Use it for your RAG.
lixx21
A Retrieval-Augmented Generation (RAG) application for querying legal documents. It uses PostgreSQL, Elasticsearch, and LLM to provide summaries and suggestions based on…
quanta-quest
AI-powered universal search for all your personal data, tailored just for you. Goal:The world's first product with "edge-side LLMs + consumer data localization" as its c…
Wannabeasmartguy
Create your own GPT intelligent assistants using Azure OpenAI, Ollama, and local models, build and manage local knowledge bases, and expand your horizons with AI search…
OpenDCAI
Use DataMind from Codex to ingest local files, query RAG or graph knowledge, store facts, and inspect profiles.
atukunare
Turn chat-pasted text/links into a verified, translated, searchable wiki knowledge base. When a user pastes a link or text (in ANY channel — Discord, Slack, CLI, etc.),…
roedyrustam
Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents…
kissgyorgy
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, e…
get-convex
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
gmh5225
Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering.
dhicoc
从宋·刘温舒《素问入式运气论奥》(元符己卯 / 1099,已入《正统道藏》太玄部,公有领域)蒸馏出的运气学「专论·机制纵深层」框架——卷上象数纵深(纳音/六化/五行本义/日刻/标本)+ 卷下机制纵深(胜复·五郁·六病·六脉·治法·五行胜复论=亢害承制机制化),逐字照明本底本。用于「专论机制回溯」与「亢害承制/胜复/五郁/六病桥梁」补足,…
dhicoc
从明·张介宾(张景岳)《类经图翼》卷一「运气上」、卷二「运气下」蒸馏出的运气学「图翼·象数基础」层框架——太极—阴阳—五行生成数—气数—五运(五天五运/五音建运太少相生/主客运)—六气(正化对化/主客气/司天在泉)—天符岁会—南北政脉不应,逐字照明本底本。用于「象数基础研读」与「图翼推算细节补足」,非临床指南。
从《医学穷源集·卷二》(明·王肯堂 撰,殷宅心 辑释;herbtcm.com 逐字转录本,公有领域)蒸馏出的运气「专论·灾变纵深层」框架——太乙移宫九宫八风、左右升降不前/司天不迁正不退位、刚柔失守三年化疫(甲子/丙寅/庚辰/壬午/戊申五年详例)、疫由人事论(人定胜天)、运气总论(太过不及平气胜复郁发)、化数生成说、流年灾宫说、方月图说…