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Collect system and cognitive health metrics. Returns a structured health report with status classifications (ok/warning/critical) for hardware, OS, and cognitive subsystems.
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Aim the ChatterBot head to find and center a target in view — the user, a person, an object, an animal. Runs a closed visual loop (capture, judge where the target is, nudge pan/tilt, repeat) until the target is centered, or reports it could not find the target after searching. Use when the user says point at me, look at me, turn to face someone, find the cat, center on the person. For a one-off snapshot without re-aiming use camera-capture; for a manual fixed angle use head-move.
Capture a still photo from the ChatterBot head camera. The captured frame is attached to your own visual input, so you can SEE it and answer questions about what is in view — whether the user is present, whether there is a cat, what the scene looks like. The camera rides the pan/tilt head, so it shows whatever the head is currently aimed at; aim first with head-move if needed. To also show the photo to the user on screen, follow with the display tool (the observation includes a ready <img> URL). Use when the user asks what you see, to take a picture or snapshot, or to check whether something or someone is in view.
Move the ChatterBot head — aim the pan/tilt camera or play an expressive gesture. The bot is a stationary companion head; this points its gaze, it does NOT drive or navigate. Use when the user asks you to look somewhere, turn toward/away, look up/down, re-center, or nod/shake/scan. Angles are degrees 0-180 with 90 centered (pan 0=full right, 180=full left; tilt 0=down, 180=up, mounting-dependent). Give pan and/or tilt for absolute aim, OR a gesture (not both). Returns the confirmed pose once the head settles.
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Generate an original image from a text description, locally (Bonsai-Image 4B, ternary-quantized, on a long-lived studio server). Use when the user wants a picture, illustration, avatar, or face created from a description that does not already exist on the web. For existing photos of real things, prefer image search instead; for simple diagrams or line drawings, prefer authoring inline SVG.
| name | check-health |
| type | python |
| description | Collect system and cognitive health metrics. Returns a structured health report with status classifications (ok/warning/critical) for hardware, OS, and cognitive subsystems. |
| schema_hint | {"out":"$variable (optional)"} |
Homeostasis monitor. Collects hardware metrics (GPU, CPU, RAM, disk, network, processes) via system-health.sh and cognitive metrics (memory integrity, tool effectiveness, world model health, planning performance) from internal state. Returns a structured health report.
No required input. Runs all checks automatically.
Success (status: "success"):
value: Human-readable summary string — lists concerns if any, otherwise "All systems nominal."$health) contains a JSON object with these top-level keys:
overall_status: "ok" | "warning" | "critical"concerns: list of human-readable concern strings (empty if all ok)system: hardware/OS metrics (cpu, gpu, ram, disk, network, processes)cognitive: cognitive metrics (memory, planning, tool_model, world_model, user_interaction)health = executor.get_json("$health")
overall = health["overall_status"] # "ok", "warning", or "critical"
concerns = health["concerns"] # e.g. ["Plan completion rate low: 45% (last 20 plans)"]
Do NOT use health.get("extra", {}).get("overall_status") — overall_status and concerns
are at the top level of the Note content, not nested under extra.
src/scripts/system-health.sh synchronously to collect OS/hardware metricsScheduled health check (planner decides what to report):
{"type":"check-health","out":"$health"}
The planner reads $health via executor.get_json("$health") and decides whether to
say or ask the user based on overall_status and concerns.