233 Claude Code skills tagged Rag. Browse all AI provider, model, or runtime-related skills in the open ClaudSkills registry — free to install, one-click via the desktop app.
Showing top 200 of 233 skills, ranked by quality score.
Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking
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Batch embedding generation with caching, rate limiting, and multiple provider support
engineering
RAG 시스템 품질 평가 및 개선을 위한 스킬입니다. RAGAS 기반 LLM-as-Judge 평가, 사용자 페르소나 시뮬레이션, 합성 데이터 생성, 평가 결과 저장 및 분석 기능을 제공합니다. — from LucasSantana-Dev/sharekit
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RAG 시스템 품질 평가 및 개선을 위한 스킬입니다. RAGAS 기반 LLM-as-Judge 평가, 사용자 페르소나 시뮬레이션, 합성 데이터 생성, 평가 결과 저장 및 분석 기능을 제공합니다. — from majiayu000/claude-skill-registry
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Consulta ao corpus RAG do projeto. Busca conhecimento em decisoes, documentacao, learnings e padroes armazenados. Use quando: buscar decisoes passadas, encontrar document — from ma
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Consulta ao corpus RAG do projeto. Busca conhecimento em decisoes, documentacao, learnings e padroes armazenados. Use quando: buscar decisoes passadas, encontrar document — from ma
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RAG systems analyst and architect skill. Collects and clarifies requirements through structured dialogue, then transforms unstructured business or developer descriptions into forma
engineering
Cross-encoder reranking and MMR diversity filtering for improved retrieval quality
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Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, ag
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Vector database operations for embeddings and semantic search. Activate for Pinecone, Weaviate, Chroma, pgvector, RAG, and similarity search.
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NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug,
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LlamaIndex agent and query engine setup for RAG-powered agents
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Query expansion, HyDE, and multi-query generation for improved retrieval
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Medical AI paper optimization for AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace) and RAG-based literature tools. Applies when drafting or reviewing titles
science
Add Qdrant embedding support to v3 WordPress components for RAG chatbot. Implements component-level content chunking for searchable, structured embeddings. Use when adding embeddin
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Composite skill — query, capture, improve, and persist knowledge in one workflow. Chains recall (RAG query) → sync-memories (write durable note) → rag-curate (improve weak retrieva
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Audit corpus distribution by source type and repo; identify coverage gaps and underindexed topics
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Manually improve corpus quality by adding missing docs and filling retrieval gaps
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Detect and fix stale chunks (files that changed or were deleted since last indexing)
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Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching
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Erstellt eine portable KI-Arbeitsumgebung auf einem USB-Stick oder beliebigem Laufwerk. RAG-Pipeline mit lokalen LLM-Modellen (Ollama), Vektordatenbank (ChromaDB) und vorkonfigurie
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Use when designing or auditing a Salesforce data architecture to support AI features — Einstein, Agentforce, Data Cloud, or custom ML models. Covers field-level data quality requir
engineering
Implement AI Coaching best practices on AnalyticDB for PostgreSQL (ADBPG): Leverage Supabase projects (training data management) + ADBPG instances with vector optimization to build
engineering
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embed — from ge
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Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing — from col
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Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing — from col
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AI philosophy, ethics, and soul Q&A. Ask questions about consciousness, meaning, spirituality, and AI identity. RAG-powered answers with citations from 250+ documents on meditation
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Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
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Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy — from cu
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Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy — from ma
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Use when running multi-skill pipelines — new-feature, marketing-launch, design-to-code, web-creation, full-audit, rag-setup workflows
growth
Scaffold, structure, and deploy the Physical AI textbook in Docusaurus with book-aware content and RAG-ready exports. Use when creating or updating the Docusaurus site, adding chap
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Spezialfall RAG-Architekturen mit Mandantenakten: Embedding-Speicher, Vektor-DB im EU-Hosting, Loeschkonzept Embedding bei Mandantenwiderruf, Trennung pro Mandat. Prüfras — from Kl
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Spezialfall RAG-Architekturen mit Mandantenakten: Embedding-Speicher, Vektor-DB im EU-Hosting, Loeschkonzept Embedding bei Mandantenwiderruf, Trennung pro Mandat. Pruefra — from Kl
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An official MCP server for the Chroma open-source embedding database. Enables AI agents to create collections, add documents, perform vector search, full-text search, and metadata
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Use OpenRAG's built-in MCP endpoint so agents can ingest documents, search a knowledge base, create filters, and run RAG-backed chat against a deployed OpenRAG instance.
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Builds a portable, embedding-free knowledgebase from a set of files and delivers it as a self-contained `.skill` bundle (BM25 index + bundled searcher + query protocol). Use when a
engineering
Use when you have paired scATAC-seq peak matrices and scRNA-seq gene expression matrices from the same cells (multiome data) and need to perform joint clustering, visualization, or
product
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying re — from an
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Use to DESIGN or REVIEW software, docs, SDKs, and repos so AI agents can consume them as a first-class audience — agent experience (AX), the agent-facing analog of UX and DX. Cover
product
Dokumente in ueberlappende Token-Chunks aufteilen fuer RAG-Pipelines und LLM-Kontextfenster. Zero Dependencies.
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PDF/DOCX/XLSX/PPTX generation and parsing on Cloudflare Workers. Covers CF Browser Rendering → PDF, pdf-lib Worker-native generation, docx/exceljs output, pptxgenjs slides, and RAG
engineering
Compute and analyze embeddings for dataset quality, distribution comparison, semantic deduplication, diversity measurement, and similarity-based filtering. Covers sentence-transfor
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Embed an idea title+summary via HF inference; check cosine similarity vs the embeddings store.
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Embedding backends (InsightFace/PyTorch+ONNXRuntime vs TensorRT). Use when optimizing embedding throughput or debugging drift/fallbacks.
engineering
Use when designing embedding strategies that fuse semantic and structural information for knowledge graphs. Invoke when user mentions node embeddings, structural embeddings, semant
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Generate and manage text embeddings for semantic search, clustering, and similarity tasks
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Tune a vector index — HNSW graph parameters and quantization — to hit a recall target at the lowest latency and memory, by sweeping settings against a fixed query set instead of tr
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Embedding Models — выбор, настройка и отладка моделей эмбеддингов. ИСПОЛЬЗУЙ когда выбираешь модель embeddings (E5, BGE, Jina, Giga), настраиваешь ONNX backend, исправляешь dimensi
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Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or doc
engineering
Implement reusable embedding functions using Gemini embedding models via LangChain with proper error handling and batching for sitemap-crawled content.
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Diagnose the health of an embedding set before blaming the retriever — checking normalization, dimensionality, near-duplicates, degenerate vectors, and corpus/query distribution mi
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Use when after a CNN model has generated predicted molecular embeddings from mass spectrometry data, and you need to identify the most likely candidate molecules from a r — from Ho
science
Use when you have pairs or triplets of MS/MS spectra with associated metadata (compound structural information, Tanimoto similarity scores) and want to learn embeddings that simult
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Guide to selecting and optimizing embedding models for vector search applications. — from whatamelon/AGENT-HARNESS-BASELINE
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embeddi — from bg
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embeddi — from ma
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embeddi — from ma
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embeddi — from ma
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embeddi — from ma
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embeddi — from ma
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embeddi — from ma
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embeddi — from ge
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Guide to selecting and optimizing embedding models for vector search applications. — from majiayu000/claude-skill-registry
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embeddi — from ma
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[STUB - Not implemented] Asymmetric embedding strategy with RETRIEVAL_DOCUMENT for ingestion and RETRIEVAL_QUERY for queries. PROACTIVELY activate for: [TODO: Define on implementat
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Embed and execute external binaries (sidecars) in Tauri apps: configuration, cross-platform executable naming, and Rust/JavaScript spawn APIs. USE WHEN bundling a CLI or server bin
engineering
Use when after instantiating and invoking a sinusoidal formula embedding layer (such as SCARF embeddings in MIST-CF) on chemical formula inputs, validate that the output embeddings
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Forensische Prüfung Prompt-Injection-Risiko: Indirekte Prompt-Injection ueber hochgeladene Dokumente, RAG-Vergiftung, Datenexfiltration. Prüfraster für Reviewer-Audit, Sandbox-Test
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Forensische Pruefung Prompt-Injection-Risiko: Indirekte Prompt-Injection ueber hochgeladene Dokumente, RAG-Vergiftung, Datenexfiltration. Pruefraster fuer Reviewer-Audit, Sandbox-T
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Citation discipline for AI-generated outputs. When to cite, what counts as a verifiable citation, URL + quote + file-path verification, marking inference vs fact, what NOT to cite.
science
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents — from maj
engineering
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents — from goo
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Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, te
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Use when when building a Graph Transformer model for continuous property prediction on molecules with associated experimental or instrumental metadata (e.g., retention time predict
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Image embedding size limits in markdown — base64 bloats 33%, use file references for images over 50KB
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Build the internal link graph for a site, run PageRank-style authority distribution, detect orphan pages, and recommend new internal links via embedding-based semantic similarity (
engineering
Use when you have 512-dimensional representation vectors output from ResNet18 encoders processing paired augmented ion images, and you need to prevent trivial solutions (representa
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Composite skill — query, capture, improve, persist knowledge in one workflow. Chains recall (semantic lookup) → sync-memories (write note) → rag-curate (improve weak retrievals) →
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Build and query vector stores with LangChain 1.0 without getting burned by flipped score semantics, embedding-dim mismatches, reranker quirks, and chunk-splitter bugs. Use when bui
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Use when you need to reduce LLM API spend, control token usage, route between models by cost/quality, implement prompt caching, or build cost observability for AI features. Trigger
engineering
Use when when you have preprocessed MS/MS spectral data (normalized peak intensities and m/z values) and need to convert individual spectra into fixed-dimensional vector — from Hol
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Use when you have pre-processed MS/MS spectra and need to prepare them for word-embedding-based similarity methods (e.g., Spec2Vec).
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RAG-indexed registry of configured MCP servers, available tools, and dispatch rules for Godot development workflows.
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Design and implement ML operations — model registry, serving patterns, deployment strategies (shadow/canary/blue-green), drift detection, feature stores, retraining triggers, and p
engineering
Generate cross-modal embeddings with CLIP, SigLIP, and ImageBind for text-image-audio search. Activate on: multimodal search, text-to-image search, cross-modal embeddings, CLIP emb
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Use when when you have paired or unpaired MS/MS spectra and need to compute structural similarity scores without explicit molecular fingerprint computation, or when you want to gen
science
Use when when building Word2Vec or embedding-based spectral similarity models where you need to capture fragmentation patterns beyond individual peak positions.
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Queries Notion databases and pages via the Notion API v1, then renders content blocks into PDF via WeasyPrint. Extracts text, tables, and inline images and preserves heading hierar
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Embeddings via OmniRoute using OpenAI /v1/embeddings format with auto-fallback across text-embedding-3-large, Voyage, Cohere, Gemini embeddings, Jina. Use when the user needs vecto
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Use local Codex/Claude JSONL logs as evidence, then produce a human-written daily diary summary and write it into Obsidian. Keep directory filtering (for example rag-flow/rag-recal
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pgvector performance optimization: HNSW vs IVFFlat index selection and tuning, ef_search / m / ef_construction parameters, iterative scanning for filtered queries, scalar and binar
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Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt regressions, or creating eval pipe
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Qdrant Operations — управление коллекциями Qdrant, sparse vectors, snapshots. ИСПОЛЬЗУЙ когда создаёшь/настраиваешь коллекции Qdrant, мигрируешь с ChromaDB, настраиваешь named vect
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Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search. — from majiayu000/claude-skill-registry
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Optimize accuracy for RAG (Retrieval-Augmented Generation) systems. Covers: DB schema design, chunking strategies, retrieval optimization, accuracy testing, and anti-hallucination
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Add an indexing source — add it to settings.json `sources` and immediately do a partial index. Utterance examples "rag add source docs/", "rag-add-source notes/", "register wiki/ i
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Use when building RAG systems, implementing semantic/hybrid search, selecting vector databases, tuning retrieval quality, or choosing chunking and embedding strategies.
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Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems. — from DROOdo
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Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation. — from majiayu000/cla
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Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation. — from majiayu000/cla
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Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems. — from majiay
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Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation. — from majiayu000/cla
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Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying re — from ma
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Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents — from vu
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Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, im
engineering
Review an existing Retrieval-Augmented Generation system and find why it underperforms. Use when asked to review or audit a RAG pipeline, diagnose wrong/ungrounded answers from a '
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Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate). Triggers on: "audit RAG pipeline", "RAG quality", "hallu
engineering
Guide an agent through property search, buyer/renter preference capture, and evidence-backed shortlist notes from structured listing data.
science
RAG pipeline design — document chunking, embedding strategies, retrieval optimization, and answer generation
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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 chr
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Destructive reset/wipe that clears the rag index·graph·cache. settings.json is preserved (optionally delete it if desired). Example utterances "clear rag", "reset rag", "delete rag
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RAG-grounded code generation with source citations. Triggers on: grounded code, ground this, cite sources, show me with sources, how do I with attune, reference attune docs, verify
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Build RAG systems for construction knowledge bases. Create searchable AI-powered construction document systems
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Improve RAG corpus quality by adding missing docs, rewriting weak chunks, and filling retrieval gaps. Use after a diagnostic skill (adt-rag-inspect, adt-rag-quality, adt-rag-covera
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Curador do corpus RAG. Gerencia adição, organização e manutenção do conhecimento do projeto. Garante qualidade e acessibilidade. — from majiayu000/claude-skill-registry
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Curador do corpus RAG. Gerencia adição, organização e manutenção do conhecimento do projeto. Garante qualidade e acessibilidade. — from majiayu000/claude-skill-registry
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Production-grade RAG (Retrieval-Augmented Generation) system design patterns from OpenClaw. Use when implementing semantic search, vector retrieval, memory systems, or knowledge ba
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Design a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your docs' feature, a knowledge assistant, or to debug why a RAG sy
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Automatically applies when building RAG (Retrieval Augmented Generation) systems. Ensures proper chunking strategies, vector database patterns, embedding management, reranking, and
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Build production document ingestion pipelines with chunking, embedding, and vector DB storage. Activate on: document ingestion, chunking strategy, embedding pipeline, vector DB ing
engineering
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from pl
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Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from ma
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Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from ma
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Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from ma
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Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo
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Batch-evaluate rag index quality with pre-registered probes — search results + the active AI session's diagnosis and tuning suggestions. Example utterances "evaluate rag", "run the
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Retrieval-Augmented Generation patterns on Oracle Cloud Infrastructure — embeddings, vector stores, hybrid search, reranking, and production RAG architecture
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N-hop graph exploration around a specific entity — reports what is connected to what as a visual/table. Example utterances "explore around entity X", "rag-explore Riverpod", "explo
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Create a rag index snapshot tarball (vec.db + graph/ + manifest + settings + compatibility header). Example utterances "make a rag snapshot", "rag-export ~/Downloads/foo.tar.gz", "
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Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HN — from fa
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Record 👍/👎 feedback on the most recent search results to improve the quality of future searches. Example utterances "this result was good", "rag feedback positive", "that last sear
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Aggregates accumulated search feedback to report what is searched most, which sources match well, and weak spots (sources with many 👎), and proposes improvements. Example utterance
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Scrape external documents (Confluence, Google Drive, Notion, etc.) via installed official connectors and put them into the local index. rag does not hold external keys/connectors —
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Full usage guide for the rag plugin — one-line concept, quick start, index of 17 skills, FAQ, troubleshooting, Windows/macOS install. Example utterances "rag usage", "rag help", "h
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Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
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Design and implement Retrieval-Augmented Generation systems — chunking strategy, embedding selection, vector store setup, retrieval pipeline, re-ranking, and evaluation
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RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization. — from whatamelon
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from bg
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from ma
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from ma
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Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embed — from ma
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from ma
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RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization. Use whe — from ma
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from ma
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from ma
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from ma
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from ma
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from ma
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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building d — from ma
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Implement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external data
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Apply a snapshot tarball sent by a colleague to the current project's `.noory/rag/`. Auto-verifies compatibility. Example utterances "load the rag snapshot …", "rag-import ~/foo.ta
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Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking,
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Интеграция RAG (Retrieval Augmented Generation) с xAI Grok Collections и Google Gemini. Используй этот skill когда нужно добавить AI-чат с базой знаний, настроить RAG систему, инте
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Build RAG (Retrieval-Augmented Generation) knowledge bases for businesses — turn documents, SOPs, policies, product manuals into AI assistants that answer questions accurately. Use
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Composite RAG maintenance skill — runs a full retrieval index audit end-to-end: measure quality, find corpus gaps, detect stale chunks, and curate (add missing docs, rewrite weak c
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RAG architecture: embeddings, chunking strategies, hybrid search (BM25 + vector), reranking, CRAG/self-correcting, multi-hop reasoning, evaluation metrics. Triggers: RAG, embedding
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Vertex AI RAG Engine integration patterns for grounding agent responses in private data sources including corpus management, retrieval tool creation, and citation extraction. PROAC
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Chunking strategies, embedding model selection, hybrid search, reranking, eval metrics
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Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eva
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Transform textbook content based on the 10-dimension user profile to provide personalized learning experiences. Agent: AIEngineer
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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency — from Orc
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Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search. — from param087/agent-ml-skills
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Design and architect RAG (Retrieval-Augmented Generation) pipelines. Covers vector DB selection, chunking strategies, hybrid retrieval (vector + knowledge graph), semantic caching,
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Build retrieval-augmented generation systems that ground LLM responses in your data — from eddiebelaval/squire
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Build retrieval-augmented generation systems that ground LLM responses in your data — from majiayu000/claude-skill-registry
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Conception de pipelines RAG (Retrieval-Augmented Generation). Se déclenche avec "RAG", "retrieval augmented", "vector database", "embeddings", "knowledge base", "Pinecone — from Ja
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트리거: "RAG 파이프라인", "벡터 검색", "문서 임베딩", "RAG 만들어줘", "retrieval augmented generation" 수행: 문서 청킹 전략 설계 → 임베딩 → 벡터 DB 저장 → 검색 파이프라인 코드 생성 출력: 완전한 RAG 파이프라인 코드 (LangChain 또는 LlamaIndex 기반
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Add a rag evaluation probe (pre-registered question) — register a frequently asked question in `.noory/rag/probes.json`. Utterance examples "add a probe", "register a rag evaluatio
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List the registered rag evaluation questions (probes) — print every entry in `.noory/rag/probes.json` as a table. Utterance examples "probe list", "show the rag evaluation question
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Remove a rag evaluation question (probe) — delete an item by ID from `.noory/rag/probes.json`. Example utterances "remove probe", "delete the auth-flow probe", "drop this question"
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RAG-specific prompt engineering techniques and best practices. RAG 專屬提示工程技術與最佳實踐。 Use when: building retrieval-augmented generation pipelines, grounding LLM answers in documents, h
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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid sear — from qd
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Index quality cleanup — alias merge · community re-detection · community summary generation. Utterance examples "rag rebalancing", "rag-rebalance", "graph cleanup", "entity duplica
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Re-chunk and re-extract entities from only the changed files, then load them into the vector/graph. Example utterances "rag reindex", "rag update", "rag-reindex", "reflect the chan
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Remove an indexing source — take it out of settings and clean up the chunks/entity mentions belonging to that source. Example utterances "rag remove source docs/legacy", "rag-remov
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Use when designing or fixing the retrieval side of a RAG system, choosing chunking strategy (fixed-size / recursive / semantic), implementing hybrid search (BM25 + dense) with RRF
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Search RAG database for relevant content. Use for semantic queries over processed documents, code, or papers.
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Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multil — from UK
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Guide to the three patterns for sharing a rag index/materials with a team. Example utterances "how to share rag", "share the index with my team", "how do I give it to someone else?
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RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
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Build Retrieval Augmented Generation (RAG) pipelines with vector databases, embeddings, and context-aware responses. Adapted from Anthropic's Claude Cookbooks.
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Report the current rag index status — file/chunk/entity/relation/community counts, embedding model, disk usage. Example utterances "rag status", "rag-status", "rag stats", "index s
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Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, o
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Build Retrieval-Augmented Generation systems to enhance LLMs with external knowledge. Use for question answering, document search, knowledge bases, reducing hallucinations, and gro
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Patterns for wrapping any agent with RAG context from Qdrant. Use to add persistent memory to imported or external agents.
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Design know-how for read-only instruments (計器) — aggregate readings over stored state (distributions, compositions, cluster structure) that inform the operator before an interventi
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Research GitHub, GitLab, and Bitbucket repositories using DeepWiki MCP server. Use when exploring unfamiliar codebases, understanding project architecture, or asking ques — from sc
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Research GitHub, GitLab, and Bitbucket repositories using DeepWiki MCP server. Use when exploring unfamiliar codebases, understanding project architecture, or asking ques — from ma
science
Research GitHub, GitLab, and Bitbucket repositories using DeepWiki MCP server. Use when exploring unfamiliar codebases, understanding project architecture, or asking ques — from ma
science
Use when you have preprocessed MS/MS spectra binned into 10,000 equally-sized m/z bins (10–1000 m/z range) with square-root-transformed intensities, and you need to generate 200-di
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Use when building a transformer-based neural network for chemical formula ranking or classification from mass spectrometry spectra, and you need to encode categorical chemical form
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Use when after generating a tile matrix or feature count matrix from single-cell ATAC-seq, RNA-seq, Hi-C, or methylation data, before clustering or UMAP visualization, when you nee
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Use when you have a set of preprocessed MS/MS spectra (binned to 10,000 m/z bins with intensities square-root transformed) and a trained MS2DeepScore Siamese neural network model,
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Use when you have a collection of pre-processed MS/MS spectra (binned, intensity-normalized) and a trained MS2DeepScore base network, and you need to compute structural similarity
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Use when after training a transformer-encoder-based mass spectrometry embedding model (e.
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Use when you have a large single-cell count matrix (≥10 million cells) in CSR format and need to verify whether the matrix-free spectral embedding in SnapATAC2 achieves its documen
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Use when you have MS/MS spectra in MGF or similar format and a reference library of molecular structures (SMILES or SDF), and your goal is to retrieve the most likely structures fo
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Use when when you have variable-length MS/MS peak lists (m/z and intensity arrays) that must be fed into a transformer-based model for spectra analysis, and you need deterministic,
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Use when when you have MS/MS spectra (LC-MS or equivalent positive ionization mode data) that you intend to embed using Word2Vec or similar distributional semantic models, or when
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