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Tugoukezhang
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byterover

Store and retrieve project patterns and architectural rules via the brv CLI.

8|1|Updated Apr 13, 2026
Search Tags:#cli#memory#ai-agent#knowledge-management#context-tree#byterover#brv
One-click install
npx skills add https://github.com/Tugoukezhang/workbuddy-skills --skill byterover-tugoukezhang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: byterover
Source: https://github.com/Tugoukezhang/workbuddy-skills/tree/main/skills/byterover
Command: npx skills add https://github.com/Tugoukezhang/workbuddy-skills --skill byterover-tugoukezhang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ByteRover provides an AI agent knowledge management system that uses brv to store and retrieve project patterns, decisions, and architectural rules in .brv/context-tree. This enables consistent reasoning and long-term memory across tasks.

Core Features & Use Cases

  • Centralized memory: stores project patterns, decisions, and architectural rules in .brv/context-tree.
  • Quick recall: brv query to fetch relevant context before performing actions.
  • Curate & evolve: brv curate to persist new knowledge after implementation.

Quick Start

Run brv query to retrieve relevant context before taking action, and brv curate to save new insights after implementation.

Frequently Asked Questions about byterover

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I give an AI agent persistent memory across project sessions?▼

To give an AI agent persistent memory, you store project patterns and architectural rules in a structured knowledge base. ByteRover uses the brv CLI to save context locally in a .brv/context-tree directory, enabling consistent recall across tasks.

What is the best way to retrieve architectural rules before an AI agent takes action?▼

The best way to retrieve architectural rules before an AI agent takes action is to query the knowledge base. Using the brv query command fetches relevant context from the stored .brv/context-tree, ensuring the agent has the necessary background information.

How do I save new project patterns and decisions after implementation?▼

You save new project patterns and decisions after implementation by curating the knowledge base. Running the brv curate command persists new insights into the .brv/context-tree, allowing your AI agent's knowledge to evolve over time.

Do I need the brv CLI to manage an AI agent's knowledge base?▼

Yes, you need the brv CLI to manage an AI agent's knowledge base with this system. The brv CLI handles local query and curate operations for the .brv/context-tree, and also supports optional cloud sync via brv push and pull commands.

Can I sync my AI agent's context tree across different development environments?▼

You can sync your AI agent's context tree across different development environments using cloud synchronization. The brv CLI supports optional cloud sync via the brv push and brv pull commands to keep your knowledge base updated across projects.

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