basic-usage
Essential guide to llmemory for document storage and search: installation, database setup with pgvector, document ingestion, hybrid/semantic retrieval, and building RAG systems with multi-tenant support.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
134 skills found
Essential guide to llmemory for document storage and search: installation, database setup with pgvector, document ingestion, hybrid/semantic retrieval, and building RAG systems with multi-tenant support.
Map stakeholders on a Power/Interest grid, define tailored communication strategies, and generate a comprehensive engagement plan for product initiatives and team alignment.
Comprehensive SEO and GEO optimization suite. Use to analyze domains, find keyword gaps, research backlinks, and generate autocomplete search suggestions using DataForSEO.
Design and implement robust, scalable event stores for event-sourced systems, covering architectural patterns, technology selection, and persistence strategies.
Manage database orchestration sessions, state snapshots, and system-level operations for the BAZINGA-DB core engine.
Proven patterns for extracting, caching, and processing analytics data from GA4 and GSC using MCP servers.
High-performance document intelligence library for extracting text, tables, code, and metadata from 91+ file formats, with OCR and LLM-ready output.
An all-in-one Chinese daily utility toolkit: weather, currency exchange, news, and package tracking. Zero configuration, no API keys required.
Standardized detective skill integration for agent roles. Maps agents to code-analysis skills and enforces claudemem usage for memory-indexed code investigation.
Optimize Apache Spark jobs with partitioning strategies, memory management, shuffle tuning, and data skew mitigation for high-performance data processing pipelines.
Generates data cleaning pipelines for pandas/polars/PySpark, handling missing values, duplicates, outliers, type conversions, and validation.
Upstash Vector DB setup, semantic search, namespaces, and embedding models. Ideal for building high-performance vector search features in Next.js 16/Vercel projects.