Split 2 of 7: episode and campaign data model

Examples/WendyDataCampaign/campaign.yaml

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+# The campaign schema version this file is written against. Required; this
+# release of the agent supports version 1.
+version: 1
+
+name: forklift-failures
+fleet: warehouse-west
+
+sources:
+  # Per-source capture policy is optional; without one a source records
+  # continuously. Which modes an adapter honors depends on the source kind:
+  # cameras do continuous and snapshot, audio does continuous and threshold,
+  # ROS 2 and telemetry do continuous only. A mode an adapter does not
+  # implement for its kind still validates and deploys; deployment warns and
+  # that source records continuously.
+  - camera: front
+    capture:
+      mode: snapshot
+      interval: 2s
+      max_resolution: 1280x720
+  - ros2: /lidar/points
+  - ros2: /vehicle/odometry
+  # An audio source with a level-threshold capture policy: when the cabin
+  # microphone's peak level crosses -20 dBFS, the agent seals a 30-second WAV
+  # fragment. level_db is a negative-valued field, so the comparison is against
+  # a negative threshold.
+  - audio: cabin-mic
+    capture:
+      mode: threshold
+      trigger: "level_db > -20"
+      fragment: 30s
+
+capture:
+  buffer: 10s
+  after_trigger: 20s
+  triggers:
+    - event: emergency_stop
+    - model.uncertainty: "> 0.65"
+
+upload:
+  # One of always, wifi, or manual.
+  when: wifi
+  # Optional logical dataset name the fleet backend maps to storage. This is
+  # not a URL; devices never receive bucket layouts or credentials.
+  destination: forklift-episodes
+  # Optional device-side bandwidth cap for uploads.
+  max_rate: 5MB/s
+
+retention:
+  # Optional cap for on-device episode storage.
+  local_quota: 10GiB
+
+export:
+  annotation: cvat

Examples/WendyDataModelApp/campaign.yaml

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+# Campaign for the WendyDataModelApp reference app. Deploy with:
+#
+#   wendy data campaign deploy campaign.yaml
+#
+# This is the only campaign the example needs. It works with the app
+# running, on a single-camera device, because the app no longer opens the
+# camera: it subscribes to the same producer this campaign's capture
+# adapter consumes (the sensors entitlement, see wendysensors.py). The
+# earlier telemetry-only variant existed solely to work around the
+# device-conflict that arrangement removes.
+version: 1
+
+name: model-harness-demo
+
+sources:
+  # "front" selects the only healthy camera on a single-camera device.
+  # Captured continuously and uncapped, so the episode holds the actual
+  # frames the model consumed: the manifest's model_input ledger names each
+  # sample the app received, and cameras/<source>/index.jsonl locates the
+  # bytes for the same sample_id. after_trigger bounds the volume to about
+  # twenty seconds of video per episode.
+  #
+  # A "capture: {mode: snapshot, interval: 30s}" policy here stores far
+  # less, and remains correct — the manifest then reports the source's
+  # payload_retention as capture_policy_keeps_a_subset, so a consumer knows
+  # the episode holds bytes for only some of the frames the model saw.
+  - camera: front
+
+capture:
+  # Keep 10 seconds of pre-trigger application records and 20 seconds
+  # after the trigger.
+  buffer: 10s
+  after_trigger: 20s
+  triggers:
+    # Fired by the app's edge-triggered person event.
+    - event: person_detected
+    # Fired by any prediction whose attributes.uncertainty exceeds 0.65:
+    # frames where YOLOv8n saw nothing above its confidence threshold
+    # (uncertainty 1.0) or only low-confidence detections. Each such
+    # prediction names the sample it came from, so the episode can be
+    # replayed against the exact frame that confused the model.
+    - model.uncertainty: "> 0.65"
+
+upload:
+  when: always
+
+retention:
+  local_quota: 5GiB
+
+export:
+  annotation: cvat
+
+# Pin the model this campaign collects for, matching the version every
+# prediction record reports in attributes.model_version.
+models:
+  yolov8n: "8.3.63"