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name: control-simulation description: "Assemble safe NeuroMANCER open- and closed-loop rollouts with System, preview, moving-horizon, and PSL simulators; use this route for control wiring, signals, normalization, and external-data boundaries." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Control simulation

Use this sub-skill when a task needs a short, inspectable NeuroMANCER rollout, a differentiable closed loop, reference preview, moving-horizon inputs, or a PSL plant/emulator. It is verified against the neuromancer==1.5.6 API surface and is CPU-first. Start with the API contract, then select a bounded procedure from the workflows. Use troubleshooting before changing tensor axes, keys, padding, or backends.

Route by intent

• Neural or symbolic nodes in a rollout: use Node and System; keep objective, constraint, and loss syntax in the symbolic-problems route.
• Future-known references or disturbances: use SystemPreview and make the preview length and end-of-sequence padding explicit.
• A model that consumes a short time history: wrap its Node in MovingHorizon; reset or replace its history between independent episodes.
• A ground-truth or synthetic plant: choose a named entry in neuromancer.psl.systems, then use a short simulate call with an explicit backend and seed.
• Learned differentiable predictive control: compose policy and plant nodes in a closed-loop System; follow the recipe in the workflow reference and send objectives/constraints to symbolic-problems.
• Recorded or domain data: first establish a local file/schema and data permission. Do not silently invoke a PSL download or a long building/control example.

Minimum safe procedure

1. Decide the time convention: per-node tensors are (batch, features); rollout dictionaries are (batch, time, features). Give the system a one-step initial state and a time-bearing exogenous key or an explicit nsteps.
2. Give every node an explicit unique name and order nodes so outputs needed by a later node already exist in the current step. Use the same key for a recurrent state transition only when that overwrite is intentional.
3. Run scripts/simulation_smoke.py --run or an equivalent four-step CPU fixture before adding a real plant, preview, loss, or trainer.
4. For PSL, choose NumPy first for a non-differentiable check or CPU Torch when gradients are needed. Use set_stats=False for a bounded structural smoke unless statistics and normalizers are part of the test.
5. Record shapes, keys, horizon, seed, backend, padding policy, and any external-data prerequisite. Treat GPU, network, credentials, and long training as separate optional verification.

The generated graph owns wiring and simulation knowledge only. Route general sequence batching/training to data-training, neural blocks/integrators to dynamics-modeling, symbolic objectives and constraints to symbolic-problems, and structured maps to structured-operators.