Metadata-Version: 2.4
Name: engaku
Version: 0.5.0
Summary: AI persistent memory layer for VS Code Copilot
License: MIT
Project-URL: Homepage, https://github.com/JorgenLiu/engaku
Project-URL: Repository, https://github.com/JorgenLiu/engaku
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Quality Assurance
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"

# 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

```bash
pip install engaku
```

Or install directly from source:

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

## Quick Start

```bash
# 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
