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Jevotron: Multiple Jev integrations from 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.

Hacker Newschrismungall2026/10/1 · 2026/10/15 分钟阅读

以下正文同步自 Hacker News,版权归原站所有,已转换为易读排版。

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 →

Inspect chunks, field paths, and exact model requests before making an API call.

Jev scores selected fields together. Each entry gets the same guidance and optional examples.

Sort suspicious entries, export CSV, or pipe JSONL into your existing shell workflow.

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.

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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