Alireza Rezvani 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
| Lane (phase) | Sub-skill | Loop/workflow |
|---|---|---|
| interview | interview |
single-pass workflow |
| stage-launch | stage-launch |
single-pass workflow |
| grade-iterate | grade-iterate |
bounded grade→iterate loop |
| run-without-you | run-without-you |
recurring cron deployment loop |
| wrap-up | wrap-up |
— |
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)
- No goal set. If
goal.jsonis missing, rungoal_state.py init --goal "..."first. The orchestrator does not guess a goal. - Goal too vague. Router exit 4 — get one sentence naming the one job before routing. Never route on under-3-word goals.
- 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. - 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)
- "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. - "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.
- "How would you grade a good run?" — Recommend: 3–5 rubric lines grounded in the output. Cite: cma-primitives.md (outcomes; rubric required).
- "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.
- "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— owngoal.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.v1execution shape.