engaku 0.2.0

AI persistent memory layer for VS Code Copilot

engaku

AI persistent memory layer for VS Code Copilot — keeps project context, rules, and active tasks in front of the agent at every turn through VS Code Agent Hooks.

What it does

engaku gives VS Code Copilot durable project memory stored in .ai/ Markdown files. Agent Hooks automatically inject current context into every conversation, surface active-task steps on each prompt, and remind the agent when a task plan is complete and ready for review.

Installation

pip install engaku

Or install directly from source:

pip install git+https://github.com/JorgenLiu/engaku.git

Quick Start

# Bootstrap .ai/ and .github/ structure in your repo
engaku init

After running init, VS Code Agent Hooks are active. The @dev, @planner, @reviewer, and @scanner agents are available via .github/agents/. No further manual steps are needed — hooks fire automatically on SessionStart, UserPromptSubmit, Stop, and PreCompact.

What engaku init creates

.ai/
  overview.md       — project description, constraints, tech stack
  tasks/            — planner-managed task plans
  decisions/        — architecture decision records
.github/
  copilot-instructions.md   — global agent rules
  agents/           — dev, planner, reviewer, scanner agent definitions
  instructions/     — .instructions.md stubs for hooks, templates, tests
  skills/           — bundled skills (systematic-debugging, verification-before-completion, frontend-design)

Subcommands

Command	Purpose
init	Bootstrap .ai/, .github/ structure and install VS Code Agent Hooks
inject	Inject .ai/overview.md + active-task context (SessionStart / PreCompact hook)
prompt-check	Detect rule/constraint in user prompt and inject active-task steps (UserPromptSubmit hook)
task-review	Detect completed task plans and emit handoff reminder (Stop hook)
apply	Apply .ai/engaku.json model config to .github/agents/ frontmatter

How it works

After engaku init, four Agent Hooks fire automatically:

• SessionStart → engaku inject: injects overview.md and the current active-task context at the start of every session.
• PreCompact → engaku inject: re-injects context before conversation compaction so the agent doesn't lose project memory.
• UserPromptSubmit → engaku prompt-check: scans each user prompt for new rules or constraints and injects the active-task's unchecked steps as a system message so the agent always knows what to do next.
• Stop → engaku task-review: after each agent turn, checks whether all steps in an in-progress task plan are ticked and emits a handoff reminder if so.

Requirements

• Python ≥ 3.8 (stdlib only, no third-party dependencies)
• VS Code with GitHub Copilot