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name: prediction description: "Runs Luminoth image and video predictions, demo web serving, and public Detector/read_image/vis_objects API workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Luminoth Prediction

Use this sub-skill for Luminoth inference workflows: lumi predict over images or videos, lumi server web, and the public Python API exposed as Detector, read_image, and vis_objects.

Before acting, use the root router at ../../SKILL.md when the user's request may also involve installation, package-level troubleshooting, or another Luminoth workflow.

Route by task

User goal	Use this file	Notes and cross-links
Run one or many image predictions	references/workflows.md	Produces JSON lines for images and can save annotated images.
Run video predictions	references/workflows.md	Requires video dependencies; video JSON output is intentionally not emitted.
Predict a mixed directory of images and videos	references/workflows.md and scripts/check_prediction_inputs.py	The bundled checker reports recognized files, ignored suffixes, class-filter conflicts, and ffmpeg availability.
Serve the demo web app or call its POST API	references/workflows.md	This is a demo interface, not production serving.
Use Luminoth from Python	references/api-reference.md	Covers Detector(checkpoint=None, config=None, prob=0.7, classes=None), read_image(path), and vis_objects(...).
Diagnose prediction failures	references/troubleshooting.md	Includes missing checkpoint, missing ffmpeg, media format, web API, and CLI misuse cases.

Boundary and handoff rules

• Stay here for inference, visualization, class filtering, media outputs, demo-server startup, and /api/<model_name>/predict/ usage.
• Route checkpoint refresh/list/download/import/export, aliases, IDs, and local index repair to ../checkpoints/SKILL.md.
• Route training/evaluation, model configs, training run directories, and TensorBoard to ../training/SKILL.md.
• Route dataset conversion or TFRecord/class-file preparation to ../dataset-preparation/SKILL.md.
• Do not edit checkpoint indexes, package checkpoints, convert datasets, or run train/eval lifecycle tasks from this sub-skill.

Common response shape

When a prediction request arrives, answer in this order:

1. Decide whether the user wants CLI output, saved media, the Flask demo, or the Python API.
2. Confirm whether a checkpoint, an explicit config, or the default accurate alias should be used.
3. Check the input media type and whether any video output requires FFmpeg.
4. Decide whether class filters, --max-detections, or --min-prob need to be mentioned in the final command.
5. If the task spans checkpoint discovery or package installation, route that part to the owning sibling sub-skill before continuing.

Safe preflight

For CLI prediction jobs, especially mixed image/video directories, run the bundled checker before invoking lumi predict:

# From this prediction sub-skill directory, or replace scripts/... with the
# absolute path to this bundled helper in the installed skill tree.
python scripts/check_prediction_inputs.py ./media --save-media-to ./preds --output ./preds/objects.json

The checker is self-contained and does not import Luminoth. It validates the media paths and flags that Luminoth's prediction CLI will otherwise skip, ignore, or fail on late.

Good prediction handoffs

• If the user only needs a checkpoint id or alias, answer with the checkpoints sub-skill first and come back here only after the checkpoint exists.
• If the user only needs a config decision, answer with the training sub-skill first and come back here only after the run config is settled.
• If the user is asking for output interpretation, mention the JSON object shape and the media artifact that will be written.
• If the user is asking for code, prefer the Python API reference over CLI prose so the response stays aligned with the public signatures.

Source artifact policy

The repository's prediction CLI and Flask demo are exposed by the installed lumi entry point, so their source implementations are reference-only in this skill. The bundled helper is a safe preflight tool rather than a copy of the runtime prediction implementation: it performs no inference, network access, checkpoint mutation, or writes except normal terminal output.