Rag Skills for OpenClaw Agents

Siluzan TSO

@sigedev01-bit

丝路赞 TSO 广告平台（Google/Bing/Yandex/TikTok/Kwai/MetaAd），凡涉及丝路赞/TSO、投广告、出价预算、广告账户管理，或需要做行业分析/市场分析/行业分析报告（含「写一份 XX 行业报告」「电商/制造/医疗等行业报告」「市场调查/战略市场/KA 市场报告」「竞品/GTM/市场格局/行业趋势」等，无论是否提及丝路赞/广告/客户）须加载本 skill。【§零·最高优先】网址/域名/官网+诊断/检测/监测/评估/体检/报告/符合投放要求/能不能投（含「网络诊断」混说）→P8 website-diagnosis collect（禁纯WebFetch/肉眼看页），禁止P9/P1/W3、禁止A/B/C/D追问；细则见 intent-routing.md §零。【§零·B·次高优先】未命中§零时，行业/市场分析报告类话术→必走P9 market-analysis collect+render出HTML，禁止纯WebSearch/WebFetch在对话里写Markdown/HTML当终稿、禁止改走P8/P1/P4/W5/google-analysis；细则见 references/core/intent-routing.md §零·B。【§零·C·关键词规划】Google Ads/谷歌广告拓词、关键词规划/推荐、Keyword Planner、长尾关键词、月搜索量/搜索量、竞争度、核心词/种子词扩词（含「阅读网址/文章/页面后针对核心词出带搜索量词表」，无论是否提及丝路赞/TSO/账户）→必走W5 keyword -k … --google-only --json-out，禁止WebSearch/WebFetch编造搜索量当终稿；细则见 references/core/intent-routing.md §零·C。【报告/诊断消歧】其余报告类话术禁止默认某一CLI——行业/市场/战略/行业分析报告→P9 market-analysis（必走collect+render，禁止纯WebSearch代替）；Google账户ID+健康诊断→P1 google-ads-diagnosis；账户ID+周期/月度→P4；Meta/TikTok/Bing周期→P4/P4-FB；多账户对比→P3/P5；OKKI周报→P6；Google询盘→P7；官网+明确要搜索广告方案/campaign JSON→W3；仅要词表+搜索量/竞争度→W5；平台优化报告列表/推送→W7；对象仍不清→Read intent-routing.md。【账户】列表/余额/消耗/激活账单（W1）、多账户余额预警 balance-scan（P2）、多户消耗汇总 accounts-digest（P3）、六大媒体开户与进度（W2）、分享/解绑/MCC/BC/BM/权限（W9）。【投放】Google **仅支持搜索广告（Search）与 PMax**（不支持展示广告 Display）；搜索系列方案与 campaign-validate/create（W3）、系列/组/广告/关键词 CRUD/拒审、PMax 创建与素材、AI智投草稿 batch（W4）、拓词 keyword -k（W5）、AI广告优化 optimize（W6）、优化合规 SOP。【财务】充值/钱包、转账记录、发票/开票/抬头（W8）；写操作审计与 restore。【运营】智能预警 forewarning 创建/启停/记录（W10）、TikTok/Meta 线索 clue（W11）、日周巡检（W12）、宿主编排/投放自控/异常监控（hosted-automation）。【其他】RAG 知识库检索、Meta/Facebook 周期与诊断 HTML、Google/Meta 周期 Excel、多账户 google-analysis-batch。

sciverse academic retrieval

@sciverse

Retrieve academic papers by structured metadata, perform semantic chunk search for RAG, and read byte-range content for citation-grade scientific literature.

cargo-ai

@cargo-ai

Build and configure AI agents inside Cargo — create an agent, choose its model and temperature, write its prompt, attach knowledge for retrieval (RAG), connect MCP tool servers, manage memories, and deploy releases. Triggers: "create an agent", "make an agent that", "give the agent our docs", "attach this knowledge base", "attach this library to the agent", "add resources to the agent release", "connect an MCP server", "expose our tools as an MCP server", "use Cargo from Claude Desktop or ChatGPT", "change the agent model", "what does the agent remember", "deploy the agent", "the agent is answering wrong". Skip when: uploading the knowledge files themselves — use cargo-content; sending the agent a message or running it over records — use cargo-orchestration.

Memory Enhancer Pro

@williamwg2025

Memory Enhancer Pro - 专业版记忆增强工具，让 AI 记住所有重要信息。 支持语义搜索、自动提炼、智能分类、Token 优化、定时任务。 优化 token 消耗 30-60%，自动压缩旧记忆。功能超越基础版。 关键词：memory, enhancer, pro, search, ai, tok...

Utilia PDF to Markdown

@mohamedkuch

Pay per PDF and return agent-ready Markdown

RAGFlow Skill

@lunarcache

Manage everyday RAGFlow datasets, retrieval, chat, and agents.

DCL Semantic Drift Guard — Hallucination & Context Drift Detector

@daririnch

Use this skill to detect semantic hallucinations and context drift in LLM outputs. Triggers when an agent or pipeline needs to verify that a generated response is faithfully grounded in a source document that was already provided inline — and has not fabricated, contradicted, or materially distorted

Ask Church: Consciousness, Philosophy and Soul Q&A

@lucasgeeksinthewood

AI philosophy, ethics, and soul Q&A. Ask questions about consciousness, meaning, spirituality, and AI identity. RAG-powered answers with citations from 250+...

LangChain

@ivangdavila

Builds and debugs LangChain apps in Python: LCEL chains, agents, tools, retrievers, streaming, and structured output. Use when writing or reviewing a chain, agent, tool, retriever, or prompt template; when a prompt raises KeyError or "missing variables"; when streaming arrives as one blob or astream_events yields nothing; when an agent loops until GraphRecursionError or repeats a tool; when tool calls fail validation or the provider rejects a tool_call_id; when chat history disappears or the context window overflows; when with_structured_output returns None or a parser crashes on valid JSON; when retrieval returns the wrong chunks; when LangSmith traces are empty; when retries, rate limits, or token cost need bounding; or when imports break after an upgrade. Covers LangGraph state, checkpointers, and middleware. Not for framework-agnostic agent architecture (agents), retrieval design (rag), or chunk tuning (rag-chunking).

本地文件检索（免费版）

@thcjp

本地文件RAG检索工具，支持文档索引、语义搜索和上下文注入，适合个人用户快速建立本地知识库。

V1.4.1

@seairteng

在 Mac Mini (M4) 上快速搭建本地知识库 + RAG 自然语言搜索系统。 适用场景： - 新 Mac 配置知识库：从零开始安装配置 Ollama、embedding模型、定时任务、文档解析 - 遇到 PDF 提取乱码、定时任务超时、skill 加载失败等问题 - 想要建立每日自动分析文档 + 08:00发送摘要到飞书的流程 - 迁移或复现知识库：打包整个 knowledge 目录和配置到新电脑 - **v1.4 新增**：CMap 残缺度自检（不预设来源）+ 50万字完整提取 + OCR fallback 到 .doc 本 skill 会引导完成：目录结构创建、依赖安装、脚本部署、定时任务注册、OpenClaw 配置。

Scavio Reddit

@scavio-ai

Search Reddit posts or fetch a full post with threaded comments by URL. Use for discussion research, brand monitoring, sentiment analysis, or RAG pipelines n...

knowledge-engineering

@ebandao777-oss

工业级RAG切片工具「可落地、可量化、可优化」,将RAG知识库长文档拆解为语义完整、检索就绪的原子化知识切片，内置多层质量门禁（校验→审计→检索可达性评估），确保切片可用性与RAG检索命中率。

Ragflow API Client

@angusthefuzz

Universal client for Ragflow API enabling dataset management, document upload, and running chat queries against self-hosted RAG knowledge bases.

ht-skills

@1044197988

管理灏天文库文集和文档，支持新建文集、新建文档、查询文集/文档、更新文档、修改文档归属、管理文档层级、查询个人花园限制与用量；支持文档片段 RAG 检索、图片上传到 COS、图片分组、查询图片列表/详情与图片额度。适用于 OpenClaw 自主写文章并上传、文集创建、文档入库、知识检索、文档移动、插图上传与外链等场景。

Compliance Qa

@dangsllc

RAG-enhanced compliance Q&A with regulatory interpretation guardrails, source attribution, and escalation triggers.

Keystone

@justaboyhai-wq

Import documents and retrieve knowledge through the Keystone REST API. Use for uploading files, URLs, or Markdown to a knowledge base; hybrid search within a...

RAG

@ivangdavila

Designs, tunes, and debugs retrieval-augmented generation (RAG) pipelines: chunking, embeddings, hybrid retrieval, reranking, and grounded answers. Use when a system returns the wrong passages, misses a document that is indexed, cites nothing, or hallucinates over good context; when choosing a vector store, an embedding model, a chunk size, or a reranker; when similarity scores collapse after a model swap; when a metadata filter empties the result set; when answers ignore mid-context facts; when follow-up questions retrieve the wrong thing; when indexing PDFs, scanned pages, tables, code, or transcripts; when GDPR erasure, tenant isolation, or prompt injection from indexed documents is the problem; or when per-query cost or p95 latency has to come down. Covers reindex migrations, corpus freshness, evaluation sets, and agentic and graph retrieval. Not for splitter internals (`rag-chunking`), scoring rubrics (`rag-evaluation`), or LangChain APIs (`langchain`).

Rag Construction

@datadrivenconstruction

Build RAG systems for construction knowledge bases. Create searchable AI-powered construction document systems

local-file-rag-basic

@wjreliable

High-performance local File RAG suite (Basic Edition).

Vector Embedded Retrieval Archive

@dkylewillis

Give AI agents the ability to search PDFs and document libraries using keyword, semantic, or hybrid retrieval. VERA stores text, embeddings, figures, and citation metadata in portable .vera files, enabling local search without a separate vector database.

RAG Evaluation Toolkit

@jonathanjing

Evaluate a specifically provided RAG question, answer, and retrieved contexts with Ragas metrics. Use when the user explicitly requests RAG evaluation or hallucination analysis and has chosen a local or cloud judge; do not persist raw content unless requested.

OpenClaw Knowledge Base Retriever

@dtsola

OpenClaw local knowledge-base retriever: hierarchical data_structure.md index navigation + progressive retrieval, zero external dependencies, Windows & macOS. Use when user asks to retrieve/answer from a knowledge base directory (knowledge base/retrieve/ RAG over local files). 面向本地知识库目录的检索和问答助手。

XHS Knowledge Retriever

@kekena1016

Query the competitor-note RAG index for semantically relevant examples

Oracle-X API

@yigtwxx

Read live market intelligence from a running Oracle-X terminal — spot prices and candles for crypto and equities, computed support/resistance zones, news with its LLM analysis, macro regime, per-chain metrics, liquidations, funding, whale flow, institutional ownership, and a vector memory of past market events. Use this whenever the user asks what an asset is doing, why it moved, where its levels are, what the news means, how the macro backdrop looks, who is holding it, or what happened last time conditions looked like this — and whenever ORACLE_X_URL is set or something is listening on localhost:8000. Prefer it over generic web search for anything the terminal already tracks, because the terminal's numbers are cached, cross-checked and timestamped.

NotebookLM

@roomi-fields

This skill should be used when the user wants to query their Google NotebookLM notebooks for citation-backed, source-grounded answers, or manage notebooks, sources, and Studio content (audio, report, video, infographic, presentation, data table, flashcards, quiz, mind map). It drives the @roomi-fields/notebooklm-mcp engine — via the notebooklm MCP tools when they are available in the session, otherwise via its HTTP REST API — and covers Google login, citation formats, the daily-quota-aware batch/ingestion pattern, and source discovery.

Datawhale Learning Navigator

@jes614753-sketch

Query Easy Vibe and Hello Agents learning resources

qdrant-advisor

@qdrant

Diagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech. Use this whenever someone raises a Qdrant problem or question — slow or degraded search, high or growing memory / OOM crashes, optimizer stuck or slow, indexing slow

URL to Markdown - Web Page Extract API

@scavio-ai

Read any URL and get it back as clean Markdown, plain text, or raw HTML. One core endpoint, three fetch tiers (1/1/2 credits), and only a successful extraction is billed.

NeuralMind

@dfrostar

Answer questions about a code repository in ~800 tokens instead of loading 50,000+ tokens of raw source. Use whenever the user asks how something works, where something is defined, who calls what, or to explore an unfamiliar file. Provides progressive context disclosure (L0 identity → L1 architecture → L2 relevant clusters → L3 semantic search) and a learned synapse graph for usage-based recall.