Index and query semantic context from codebases via viking:// resources.
npx skills add https://github.com/mkrlabs/specflow --skill openviking-mkrlabs
Please help me install this Agent Skill. Skill: openviking Source: https://github.com/mkrlabs/specflow/tree/main/examples/.claude/skills/openviking Command: npx skills add https://github.com/mkrlabs/specflow --skill openviking-mkrlabs
OpenViking provides a native context database for AI agents, enabling persistent, semantic memory that can be indexed and queried across codebases and resources.
Start the OpenViking server and index your project into the Viking memory.
High-intent search queries and answers about installing and using this skill.
Persistent semantic memory for AI agents is set up by starting a Viking context database server and indexing your project codebases and resources into it for retrieval.
Indexing a codebase for semantic search is done by starting the OpenViking server and running native CLI commands to index project files into the Viking memory.
Retrieving code snippets and documentation from an AI agent memory database is performed by querying viking:// resources via native CLI commands.
OpenViking does require a server-backed memory to store indexed codebases and process semantic context queries for AI agents.
Searching for architecture rules and functions across indexed projects is supported by performing semantic searches against the Viking memory database.
The best way to provide long-term context to an AI agent is by indexing codebases and documents into a native context database for persistent semantic retrieval.
SukbeomHVerify prerequisites, initialize memory directories, and analyze the codebase.
gplv2Translate natural-language code questions into semantic search queries and retrieve relevant code snippets.
drunkcodingUnify AI context into a searchable graph with hierarchical L0/L1/L2 loading and Viking URIs.
davpatel605-beepManage scalable vector database storage and retrieval for AI applications.
