Give jevotron a file and a little guidance. Get field-level anomaly scores, a focused review queue, and a cache that makes the next run cheaper.
Run your first scan Explore the commands Install agent skills
$ jevotron scan airports.csv --guidance "Check airport locations."
First, set your API key
Get a key from the TypeSafe dashboard and set it in your shell:
export TYPESAFE_API_KEY="your-api-key"
Already set? You're ready to scan. preview needs no key.
API key setup → · See the actual airport results →
01 / PREVIEW
See what goes in¶
Inspect chunks, field paths, and exact model requests before making an API call.
02 / SCAN
Assess every entry¶
Jev scores selected fields together. Each entry gets the same guidance and optional examples.
03 / REVIEW
Start with the warnings¶
Sort suspicious entries, export CSV, or pipe JSONL into your existing shell workflow.
Small setup. Useful defaults.¶ #
CSV, YAML, JSON, TOML, text, OBO, and more work out of the box, including
gzip files. The format reference covers defaults and
format-specific options. Select fields with
--field, add a sentence with --guidance, and run. Longer instructions can
come from --guidance-file. A local Python config is
available when a project needs custom parsing or reusable settings.
Unchanged input reuses its assessment. SQLite saves each successful result as it arrives. Reorder a file, change a reporting threshold, or resume a failed run without reassessing unchanged entries.
A review aid with visible evidence. Reports retain each field's probabilities, the entry score, source location, and assessment date. The warning score is the highest field anomaly probability; you choose the threshold.
Try a complete example¶ #
| Example | What you'll do |
|---|---|
| Airports / CSV | Find two injected country errors in public data, then compare versions. |
| Inventory / YAML | Apply written rules and a chosen exemplar to stock records. |
| Measurement units / OBO | Score definitions and repeated synonyms within independent stanzas. |
| Agent traces / JSONL | Classify public agent traces and individual steps, then compare with published labels. |
Agent traces: a measured pilot¶
On a small, length-filtered sample of 24 public traces, jt matched 130 of 163 step-quality labels (79.8%). Harmful-step precision was 89.7%, with 70.3% recall. The example uses original messages and tool definitions, with human labels withheld from the model.
Try the trace example → · Read the full analysis and limitations →