KX Systems
Official@kxsystems · United States
Open-source code for use with kdb
Agent Skills by KX Systems
Showing 7 vetted skills indexed across 1 GitHub repositories.
KxSystemspykx
Integrate Python workflows with kdb+ via a Python-first interface.
KxSystemskdbai
Enable KDB.AI vector search workflows for similarity, hybrid search, and time-series patterns.
KxSystemsqlint-snippet
Lint q and qSQL code snippets with KX qlint via stdin or argument.
KxSystemsq
Provide q/kdb+ idioms, debugging guidance, and translation tips for .q scripts.
KxSystemskdbx
Orchestrate KDB-X modules and AI libraries for analytics workloads.
KxSystemskxmeta-author
Write aimeta annotations with mandatory @kind and @name markers for q codebases.
KxSystemskxmeta-discover
Probes kdb+ process discovery metadata via /meta HTTP route and qIPC fallbacks.
Frequently Asked Questions About KX Systems
FAQPage SchemaWhat specific data tasks does KX Systems enable?▼
KX Systems enables high-speed time-series analysis, vector-based similarity searching, and hybrid search operations. It provides specialized utilities for linting qSQL code, managing metadata annotations, and facilitating process discovery within distributed database environments.
Which technical personas benefit from these capabilities?▼
These capabilities are designed for quantitative developers, database engineers, and data architects working within high-frequency trading or real-time analytics environments. Professionals managing large-scale kdb+ deployments utilize these resources to maintain code integrity and optimize data retrieval performance.
What are the prerequisites for implementing these modules?▼
Implementation requires an existing kdb+ environment and familiarity with the q programming language. Users must ensure their infrastructure supports qIPC communication protocols and that target codebases are structured to accommodate standard metadata markers for discovery and linting processes.