Packs

12 packs

Azure Data Analytics

For data engineers to query and manage big data on Azure with Kusto and Data Lake.

4 skills · pack

Data from om-scogo/skillsh-scraper.

100 skills · pack

Privacy Data Protection Skills

Privacy Data Protection Skills from mukul975/Privacy-Data-Protection-Skills.

100 skills · pack

Data Analysis from nivkazdan/skills-agents-catalog.

6 skills · pack

SQL, analytics, datasets, models and machine-learning workflows.

29 skills · pack

Data analytics skills for PMs: SQL query generation and cohort analysis. Analyze user data, generate queries, and identify retention patterns.

3 skills · pack

Python Data Visualization

For data scientists to create static and interactive plots using Python libraries.

12 skills · pack

Deploy Azure Infrastructure

Creates databases, caches, and configures authentication, monitoring, and backup.

3 skills · pack

Social Media Scraping

Extract structured data from social media platforms via browser automation.

12 skills · pack

Azure services skills covering 200+ cloud services, IoT, AI, data, networking, and more

78 skills · pack

Build GraphQL API

Design a GraphQL schema, implement resolvers with DataLoader, and integrate with Apollo.

4 skills · pack

Redpanda Data Skills

Agent Skills for Redpanda's five products — Streaming (Kafka-compatible engine), SQL (Oxla), Connect (incl. CDC connectors), Cloud (Serverless, BYOC, Dedicated), and the Agentic Data Plane — plus the rpk CLI. Grounded in Redpanda source, docs, and APIs.

32 skills · pack

Results for “data”

36 skills

Manage Arize datasets and examples using the ax CLI: create, list, get, export, and append datasets for evaluation and experimentation.

36.2k · bundle

Search, query, inspect, create, and import data into Weaviate vector database collections using official scripts and references.

42.4k · bundle

Installs and configures ktx, the open-source context layer for data agents, including database connections, embeddings, agent integration, and context ingestion.

Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.

Manage Hugging Face Hub resources: download/upload models, datasets, spaces; manage repos, buckets, collections, discussions, and cache; run SQL queries on datasets; authenticate and manage tokens.

Builds a configurable scraping agent that collects data from APIs, HTML, or RSS, enriches it with Gemini AI scoring, and stores results in Notion, Google Sheets, Supabase, or local files.

More results

bgpt-paper-search

Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server, returning 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

Provides guidance on using the ZVec in-process vector database for efficient similarity search and embedding storage in agent memory systems.

orchestra-research

Connects LLMs with user data for RAG applications, document Q&A, and knowledge retrieval using 300+ data connectors and vector indices.

10.4k · bundle

Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.

nimoqup046-collab

Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.

earth2studio-discover

Find Earth2Studio models, data sources, and examples for weather/climate use cases by consulting live documentation and verifying compatibility via the lexicon system.

qdrant-scaling-query-volume

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

orchestra-research

Provides code examples and best practices for using Pinecone, a managed vector database for production RAG, recommendation, and semantic search applications.

10.4k · bundle

lucaspmarie-a11y

Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.

orchestra-research

Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.

10.4k · bundle

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.

orchestra-research

qdrant-vector-search

Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.

10.4k · bundle

Search scientific papers via the BGPT MCP server and retrieve structured experimental data — methods, results, conclusions, quality scores, and 25+ metadata fields per paper.

Manage vector database operations across Pinecone, Weaviate, Qdrant, and ChromaDB, including embedding generation, index creation, metadata filtering, hybrid search, and production deployment for RAG and similarity search.

Access AI-ready drug discovery datasets and benchmarks from Therapeutics Data Commons, covering ADME, toxicity, drug-target interactions, and molecular generation with standardized splits and evaluation metrics.

30.2k · bundle

Index and search code repositories, documentation, research papers, HuggingFace datasets, local folders, and packages via the Nia API, with AI-powered research and code advisor capabilities.

Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.

20.4k · bundle

assessing-vector-and-embedding-weaknesses

Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.

24.6k · bundle

Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.

Generates and manages vector embeddings for semantic search and RAG retrieval across knowledge bases, with self-learning capabilities.

Builds a Streamlit and FastAPI RAG application that lets users upload documents and query them with natural language through LM Studio.

Store and query embeddings with metadata, vector and full-text search, and filtering. Integrates with LangChain and LlamaIndex for RAG and semantic search applications.

azure-ai-document-intelligence-ts

Extract text, tables, and structured data from documents using Azure Document Intelligence. Process invoices, receipts, IDs, forms, or build custom document models.

Chat with your agent about projects, recommendations, and canonical papers in Paperzilla. Fetch feeds, read papers, leave feedback, and export data using the pz CLI.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

10.4k · bundle

azure-ai-document-intelligence-dotnet

Extract text, tables, and structured data from documents using Azure AI Document Intelligence SDK for .NET, with support for prebuilt and custom models.

Navigate Oracle Cloud Infrastructure's Enterprise AI services: choose models, build agents with RAG and tools, estimate costs, secure access, and integrate with Oracle Database, APEX, and other platform services.