# Onboarding a Project

This document defines the **Agent Protocol** for initializing a new project. When the agent is asked to "Onboard a project," it must follow these steps.

## The Exploration Ritual

The agent's first task in any project is to perform an "Initial Exploration" to establish context.

### Step 1: Discovery
The agent must:
1. Explore the project root using `ls` and `find`.
2. Read ~/.automaton/.agent.md (global router)
3. Read ~/.automaton/.rules.md (global framework rules)
4. Read the project's `.automaton/.agent.md`
5. Read the project's `.automaton/.rules.md`

### Step 2: Reporting
The agent must report back with:
- Confirmation that the framework files were found and read.
- A summary of the project rules.
- The expected workflow for this project.
- Key observations from the project structure.

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## The Lifecycle of a Project

Once onboarded, the project moves through these phases. The agent should use the provided prompts to transition between them.

### Easier workflow with "orchestrate"

Instead of memorizing trigger phrases for each phase, you can just say **"orchestrate"** or **"continue"** and the Orchestrator will:
- In **Autopilot mode**: automatically drive the task all the way to completion
- In **manual mode**: tell you the next step and give you the command

### Phase 1: Research
**Template**: `prompts/research.md`
**Output**: `SPEC.md`
**Trigger**: *"Research {task-description}"* (or just *"orchestrate"* in manual mode)
**Interaction**: Agent will grill you for requirements, edge cases, and constraints. Present draft for review. Get your sign-off before finalizing.
**State transition**: `new` → `research` → `research:awaiting_approval` (awaiting your sign-off) → `research:approved` (after you say "APPROVED")

### Phase 1b: Design (Optional)
**Template**: `prompts/design.md`
**Output**: `DESIGN.md`
**Trigger**: *"Design the {task-name} task"* (or just *"orchestrate"* in manual mode)
**Interaction**: Agent will grill you for design decisions, trade-offs, and constraints. Present draft for review. Get your sign-off before finalizing.
**State transition**: `design` → `design:awaiting_approval` → `design:approved`

### Phase 1c: Test Design (Optional)
**Template**: `prompts/test_design.md`
**Output**: `TEST_PLAN.md`
**Trigger**: *"Design tests for the {task-name} task"* (or just *"orchestrate"* in manual mode)
**Interaction**: Agent will grill you for test coverage, edge cases, and test strategy. Present draft for review. Get your sign-off before finalizing.
**State transition**: `test_design` → `test_design:awaiting_approval` → `test_design:approved`

### Phase 2: Implementation
**Template**: `prompts/implement.md`
**Output**: Code changes + test results
**Trigger**: *"Implement the {task-name} task"* (or just *"orchestrate"* in manual mode)
**Note**: The implementer follows the TEST_PLAN.md (if present) and implements code with tests using TDD. No approval gate — transitions directly to bug_find.

### Phase 3: Bug Finding
**Template**: `prompts/bug_finder.md`
**Output**: `BUG_REPORT.md`
**Trigger**: *"Find bugs in the {task-name} task"* (or just *"orchestrate"* in manual mode)
**State transition**: `bug_find` (no approval gate)

### Phase 4: Adversarial Verification
**Template**: `prompts/adversarial_bug_find.md`
**Output**: `ADVERSARIAL_BUG_REPORT.md`
**Trigger**: *"Perform adversarial bug find for {task-name}"* (or just *"orchestrate"* in manual mode)
**State transition**: `adversarial_bug_find` (no approval gate)

### Phase 5: Documentation Review
**Template**: `prompts/doc_review.md`
**Output**: `DOC_REVIEW.md`
**Trigger**: *"Review docs for the {task-name} task"* (or just *"orchestrate"* in manual mode)
**State transition**: `doc_review` (no approval gate)

### Phase 6: Referee
**Template**: `prompts/referee.md`
**Output**: `VERDICT.md`
**Trigger**: *"Review the {task-name} task"* (or just *"orchestrate"* in manual mode)
**State transition**: `referee` → `complete` or `human_intervention`

## State Enforcement (v2.0)

All phase transitions are enforced by `status.py`:
- Tasks are created with `python ~/.automaton/scripts/status.py --create-task {name} --project {project}`
- Phases are transitioned with `python ~/.automaton/scripts/status.py --transition {phase} --task {name} --project {project}`
- Approvals are granted with `python ~/.automaton/scripts/status.py --approve --task {name} --project {project}`
- Folders are validated with `python ~/.automaton/scripts/status.py --validate-folder --task {name} --project {project}`
- All tasks are audited with `python ~/.automaton/scripts/status.py --audit --project {project}`
- Pre-v2.0 tasks are upgraded with `python ~/.automaton/scripts/status.py --upgrade --project {project}`

The `.state` file in each task folder is the single source of truth for the task's current phase. Never create task directories manually — always use `status.py --create-task`. Tasks without `.state` files are UNTRACKED and all commands refuse to operate on them. Run `status.py --upgrade` to bootstrap `.state` files for existing tasks.

**Important**: Always pass `--project {project}` to ensure correct scoping. Without it, `status.py` resolves the project from the current working directory, which can target the wrong project when multiple projects exist on the same machine.

## Prompt Rendering Convention

All prompts are stored as template files in `~/.automaton/prompts/`. They use `{placeholder}` syntax.

### Placeholders
- `{project}`: Absolute path to the project root.
- `{task-name}`: The task folder name (kebab-case).
- `{task-description}`: A brief, clear summary of the current work.

When the agent receives a trigger command, it must:
1. Read the corresponding template file.
2. Replace all `{placeholders}` with the actual project values.
3. Execute the rendered prompt.

## Backlog

Design backlogs are the framework's outstanding-work store when no active tasks exist. Each design area has its own `BACKLOG.md`:

- `~/.automaton/design/loops/BACKLOG.md` — loop engineering v1.1+ and deferred items.
- `~/.automaton/design/framework/BACKLOG.md` — framework-level agent features (rule agents, Agent tab redesign, model-divergence enforcement).

The self-improvement loop consumes these automatically when configured with `work_source.kind = "backlog"` and `work_source.area` set to the relevant design area (`"loops"` or `"framework"`). For manual work, read the topmost `- [ ]` item and create a task via `status.py --create-task`.