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From marconi
Runs a closed-loop transmit-then-receive experiment in simulation: transmit a capture into a simulated device's scene and confirm the link via a receive capture. Useful for waveform sanity checks and reproducible experiments.
How this skill is triggered — by the user, by Claude, or both
Slash command
/marconi:tx-experimentThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Run a closed loop entirely in simulation: transmit a signal into a device's scene, then capture from that device and confirm the signal arrived. This is the basis for experiments (e.g. recover a transmitted tone, sanity-check a waveform).
Run a closed loop entirely in simulation: transmit a signal into a device's scene, then capture from that device and confirm the signal arrived. This is the basis for experiments (e.g. recover a transmitted tone, sanity-check a waveform).
capture), synthesize one (render_scene with inline elements), or load_capture a file. Note its sample_rate — v1.0 requires the receiving capture to use the same sample rate.transmit_capture(device_id, capture_path, freq, confirmed=True). CONFIRM_TX is on by default to catch wrong-frequency / wrong-device mistakes; set confirmed=True only after you've checked both. The payload becomes an iq_file element of the device's scene. (This change is session-scoped — it is not re-persisted to the scene file.)capture the device at a center/sample_rate that covers the transmit frequency (sample rate must match the payload's), then run survey-spectrum (or build-receiver) and confirm the payload is present where you transmitted it.freq − capture_center_freq within the received band. Keep the transmit frequency inside the receive band.npx claudepluginhub yoelbassin/gr-mcp --plugin marconiCreates simulated RF devices with configurable tones, FM carriers, noise floors, and IQ file replay for testing without hardware.
Provides process-based discrete-event simulation in Python using SimPy — processes, queues, shared resources, and time-based events. Use for manufacturing, service operations, network traffic, or logistics simulation.
Guides LTSpice/PyLTSpice circuit simulation from netlist creation through batch simulation, trace inspection, and convergence debugging.