One-shot anomaly detection on the command line¶
One-shot anomaly detection on the command line
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 / airports.csv
$ 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:
`[](https://cmungall.github.io/jevotron/#%5F%5Fcodelineno-0-1)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 →
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