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TypeSafe Triage Guard

Three composable Jev judgment pipelines at roughly 120 lines of composition logic each: support-ticket triage behind a safety guardrail, observability alert triage with dedupe and auto-page decisions, and a deploy risk gate over PR diffs.

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Python
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MIT
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Community
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shivam2003-dev
Last verified
Oct 2026

Three composable judgment pipelines on TypeSafe's Jev: support-ticket triage, observability alert triage, and a deploy-risk gate.

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Updated Sep 2026

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About this project

Three composable judgment pipelines built on TypeSafe's System One model, Jev — a model that returns typed, calibrated probabilities instead of generated text. This repo is R&D / a worked example, not a product: it exists to test what the TypeSafe primitives (Noul, Choice, Score) are actually good for outside the docs' own demos, across three different domains, with one shared architecture.

Quoted from the project's README. Read the full README.

Highlights

  • Each pipeline sends its whole question set in one system_one() call rather than one round trip each.
  • battery.py turns N Nouls plus one severity Score into an action via a named, code-owned policy table.
  • Every pipeline checks .confidence before acting, so low routing confidence forces human review.
  • triage() runs the guardrail first and returns immediately if it blocks, skipping classification.

Watch out

The repo describes itself as R&D and a worked example, not a product, built to test what the primitives are good at outside the docs' own demos - so read it as a pattern catalogue rather than something to deploy. Most of the commands shown use --mock, and the mock is a keyword-overlap heuristic with no semantic understanding: the README says it misses a phrase like pick a lock on someone else's front door entirely because there is no shared vocabulary, and that its Score and Choice outputs get noisy on near-zero-overlap inputs, so its raw severity numbers should not be read as meaningful. Passing tests therefore demonstrate composition logic - thresholds, weighting, short-circuiting - and not model quality. The real model path needs an early-access key; a live-smoke CI job does run all three CLIs against Jev on every push using a repository secret, and one unedited JSON output per CLI is included. Beyond those single samples there is no evaluation set, no accuracy figure, no false-positive rate, no latency and no cost per pipeline documented anywhere. Weights and policy tables are the intended tuning surface, but no defaults table or threshold list is published. This README copy is cut off mid-JSON in the alert example, so the later sections, including the referenced write-up on the calibration objective, were not visible. Copy reviewed against the repository README on 2026-10-04, not from running it.

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