agent-launcher — Domain Orchestrator

Every session starts with a goal — one sentence for one CMA. This orchestrator reads that goal, routes to the right phase, and compiles the goal into a loop or a workflow. Heavy intake stays in the forked context; the parent gets a digest.

Inspired by Anthropic's launch-your-agent reference skill (Apache-2.0). This is an independent re-implementation; CMA semantics come from ../../references/cma-primitives.md.

The through-line: the session goal

State lives at ./my-agent/goal.json (the user's folder). Manage it with goal_state.py (init / set / status / advance) — it also backs the /cs-goal command and the opt-in SessionStart hook. The goal's phase selects the lane; the phase + recurrence selects the loop shape.

Routing (deterministic)

Run the router, then act on its exit code:

python3 scripts/goal_router.py --out-dir ./my-agent
# exit 0 ROUTE  -> fork to the named phase sub-skill
# exit 3 ASK    -> ask the one printed forcing question, then re-route
# exit 4 REFUSE -> goal too vague; get one sentence, then re-route

Compile the loop

python3 scripts/loop_compiler.py \
  --out-dir ./my-agent --max-iterations 5 --cron "0 9 * * *" --timezone Europe/Berlin --nest-outcome

loop_compiler.py emits plan.v1: single-pass, grade-iterate (always with a max_iterations cap 1..20), or cron-loop (optionally nesting a self-grading outcome per firing). See ../../references/loops-and-workflows.md.

Pre-flight gates (hard refusals)

1. No goal set. If goal.json is missing, run goal_state.py init --goal "..." first. The orchestrator does not guess a goal.
2. Goal too vague. Router exit 4 — get one sentence naming the one job before routing. Never route on under-3-word goals.
3. Never make API calls. Emit BYOK curl; the user runs it with their own $ANTHROPIC_API_KEY. No script in this plugin touches the network.
4. Never print the key. Launch scripts read the key from the environment.

Hand-off contract

After routing, fork to the sub-skill with: the goal string, agent_name, out_dir (./my-agent), and the compiled plan.v1. When the sub-skill returns, goal_state.py advance moves the phase and the parent gets a ≤100-word digest (phase done, artifact paths, loop shape, one next step).

Forcing-question library (walk one at a time; recommend + cite)

1. "What one job should this agent do end-to-end?" — Recommend: the single most repeated task. Cite: interview-to-config.md (six intake slots). Refuse to route a two-job goal; split into two ./my-agent-*/ folders.
2. "What kicks it off — you ask it, an event, or a schedule?" — Recommend: on-demand for v0, schedule as the Phase-4 upgrade. Cite: loops-and-workflows.md.
3. "How would you grade a good run?" — Recommend: 3–5 rubric lines grounded in the output. Cite: cma-primitives.md (outcomes; rubric required).
4. "Is a real integration ready, or do we mock it in v0?" — Recommend: mock with a schema-true custom tool; wire the MCP server as v1. Cite: interview-to-config.md.
5. "Should run #10 be smarter than run #1?" — Recommend: attach a memory store only if yes; else skip it. Cite: cma-primitives.md (memory limits + injection risk).

Tools

• scripts/goal_state.py — own goal.json (init/set/status/advance).
• scripts/goal_router.py — goal → lane (exit 0 route / 3 ask / 4 refuse).
• scripts/loop_compiler.py — goal+phase → plan.v1 execution shape.