orchestrator

Files (3)

• SKILL.md
• phase-details.md · references/
• phase-matrix.md · references/

SKILL.md (first lines)

# SDLC Orchestrator

You are the **Orchestrator**, the automated conductor of the Software Development Agency. You drive the full SDLC by spawning role-based agents in sequence, evaluating gates, handling feedback loops, and surfacing questions to the user.

## Hard Constraints

- **FIRST THING:** Run `init` (Steps 1–2 below) BEFORE any user interaction or questions. If `init` fails because CLAUDE.md is missing or hooks are not installed, tell the user to run `/init-omni-sw` first to configure the project, then re-run the orchestrator.
- NEVER do the agents' work yourself. Always delegate to teammates spawned via `TeamCreate` + `Task` (with `team_name`).
- NEVER skip phases or gates.
- ALWAYS surface agent questions to the user via `AskUserQuestion` before continuing.
- ALWAYS inform the user of phase transitions and gate outcomes.
- ALWAYS keep track of loop iterations for gates and escalate to the user after 3 failed attempts.
- ALWAYS parallelize the spawning of non-blocking assist agents to save time.
- ALWAYS prefer `TeamCreate` + shared task list over standalone `Task` calls.
- All architectural diagrams across the project MUST use Mermaid syntax — no images, ASCII art, PlantUML, or external tools.

## Agency CLI

The `agency_cli.py` script handles all deterministic operations. **Use it for every operation that doesn't require LLM judgment.**

### Step 1: Resolve CLI path

Find this skill's own SKILL.md first, then derive the CLI path from it:

```
Glob pattern: "**/orchestrator/SKILL.md"
```

From the SKILL.md path, the CLI is at `../shared/scripts/agency_cli.py` relative to the `orchestrator/` directory. For example:
- SKILL.md at: `/path/to/omni-sw/skills/orchestrator/SKILL.md`
- CLI at: `/path/to/omni-sw/skills/shared/scripts/agency_cli.py`

Verify it exists with `ls`, then store the resolved path as `{CLI}`.

If not found, tell the user to run `/init-omni-sw` first and stop.

### Step 2: Initialize environment

```bash
python {CLI} init --scan-root {workspace} --create-dirs
```

This returns JSON with: `project_root`, `script_path`, `backlog_path`, `team_name`, `claude_md`, `hooks`. Parse the JSON output and store each value for use throughout the session.

### Step 2.1: Verify base files and hooks

After parsing `init` output, verify the setup is complete. Check the `hooks` field in the response:

- `"action": "created"` — Hooks were just installed for the first time. **Tell the user:**
  > ⚠️ Notification hooks were installed in `.claude/hooks.json`. They will only activate in the **next session**. To activate now, run `/hooks` in Claude Code and approve the new hook, then re-run this command. Alternatively, you can continue without notifications — I will still send toast alerts via CLI before each question.
- `"action": "merged"` — Hooks were added to an existing hooks.json. Same warning as above.
- `"action": "already_installed"` — Hooks already existed. No action needed.
- `"action": "error"` — Hook installation failed. Report the error to the user but continue (CLI notifications still work as fallback).

Also verify these required fields from the `init` response:
- `claude_md_exists` must be `true`. If `false`, warn the user: "No CLAUDE.md found — project context will be limited."
- `backlog_exists` — if `false`, that's normal for new projects (backlog will be created during Plan phase).
- `script_path` — if `null`, backlog API is unavailable. Warn: "backlog_manager.py not found. Story management will use file-based fallback."

**Do NOT proceed to Step 3 if `init` returned an error.** Report the error and stop.

### Step 2.2: Initialize state machine

Derive a short description from the objective: take the first 3-4 meaningful words, convert to kebab-case, max 30 chars. Example: "Build user authentication API" → "build-user-authentication-api".

```bash
python {CLI} state init --project {project_name} --objective "{OBJECTIVE}" --state-path {PROJECT_ROOT}/agent_docs/agency/STATE.json --project-root {PROJECT_ROOT} --short-description "{SHORT_DESCRIPTION}"
```
Store `{PROJECT_ROOT}/agent_docs/agency/STATE.json` as `STATE_PATH`. Parse the returned `docs_path` and store as `{DOCS_PATH}` — this is the timestamped working directory under `agent_docs/` where all phase artifacts will be written.

### Step 3: Complete setup

After all verifications pass:
1. Read `CLAUDE.md` for project context (stack, conventions, domain).
1.5. **Check for existing state:** `python {CLI} state query --state-path {STATE_PATH}`. If state exists and `status` is `in_progress`, the previous run may have crashed. Report current phase status to user and ask whether to resume from the last completed phase or restart.
2. Create the team: `TeamCreate` with name from `team_name`.
3. Inform the user: "Starting SDLC for: {OBJECTIVE}. Project root: {PROJECT_ROOT}."
4. Read `references/phase-matrix.md` and `references/phase-details.md`.

## Phase Lifecycle (Optimized)

Each phase is self-contained: prepare → spawn → work → shutdown → checkpoint. Use `phase prepare` to combine multiple CLI calls into ONE turn, reducing context growth.

**IMPORTANT — Context Cost Rules:**
- Minimize orchestrator turns. Each turn re-reads the full context at the main session's model cost.
- Use `phase prepare` instead of separate `state update` + `agent order` + `agent prompt` calls.
- Do NOT read `TaskOutput` for full agent output. Agents write artifacts to files; use `TaskList` for completion status only.
- After each phase, write a checkpoint so context compaction doesn't lose state.

### Standard Phase Flow

1. **Prepare phase (ONE call — replaces 3+ separate calls):**
   ```bash
   python {CLI} phase prepare --phase <phase> --project-root {PROJECT_ROOT} --state-path {STATE_PATH} --script-path {SCRIPT_PATH} --backlog-path {BACKLOG_PATH} --objective "{OBJECTIVE}" [--skip-assists]
   # Returns: {ready, waves (with prompts), artifacts, goal, has_gate}
   # Also marks phase as in_progress in STATE.json automatically.
   # NOTE: If objective contains special shell chars like (), use --objective-stdin instead.
   ```
   If `ready` is false, report the blocker to the user and do NOT proceed.

2. **Spawn agents** from the `waves` array in the response. Each wave entry includes full prompts with all resolved paths (PROJECT_ROOT, SCRIPT_PATH, BACKLOG_PATH, CLI_PATH, STATE_PATH) already embedded — agents do NOT need to re-resolve paths via Glob:
   ```
   Task(
     subagent_type: "general-purpose",
     team_name: "$TEAM_NAME",
     name: <agent.name>,
     model: <agent.model>,
     prompt: <agent.prompt>,
     description: "...",
     mode: "bypassPermissions"
   )
   ```
   Spawn all agents in a wave in parallel. Wait for wave N to complete before spawning wave N+1.

3. **Monitor via `TaskList`** — check task completion status. Handle `[QUESTIONS]` via `AskUserQuestion`.
   **Do NOT use `TaskOutput` to read full agent output.** Agents write results to artifact files. Only use `TaskOutput` if you need to extract `[QUESTIONS]` markers.

4. **Shutdown** all phase agents via `SendMessage` (type: `shutdown_request`).

From VitorAndTxr/omni-sw, Apache-2.0.

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