jev-eval skillA
jev-eval is agent-read markdown (skill) from onewave-ai/claude-skills: Build and run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Evaluating a typed-decision config
**The eval set is the product.** A System One model's accuracy is dominated by how the
question was written, and the failure mode is silent — it returns a confident, type-valid,
wrong answer. Without labels you cannot tell a bad question from a bad model.
Measured: rewriting the criteria moved an open model from **4/15 to 14/15** on 15 records.
No model change. Then the same comparison at 150 records put that model at **61%** overall
against Jev's **97%** — the 15-record read was an artifact of a small, easy set. Both facts
are the point: wording swings results, and small sets lie about which way.
## Run it
```bash
python ~/.claude/skills/jev-eval/scripts/sweep.py labelled.json configs.json \
--backend jev|von --question <name>
```
`labelled.json` is `[{"id","state","truth"}]`. `configs.json` maps a config name to
`{"instructions", "criteria"}` — a dict of options makes it a **choice**, a list of levels
makes it a **score**, omitting it makes it a **noul**. The script reads the Jev key from
Keychain (`typesafe-api-key`), prints accuracy per config, labels the spread
…Read the whole file at its exact version.
How to install
mdr add onewave-ai/claude-skills/jev-eval@git:20260923.352d573mdr add onewave-ai/claude-skills/jev-eval@sha256:c2f7fe3f473e83d1Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_qkspczuhkor63vwg)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260923.352d573 latest | 2026-09-23 | 352d573 | 6,572 B | A | view · diff |
| git:20260921.1fa6175 | 2026-09-21 | 1fa6175 | 5,441 B | A | view |
Audit of the latest version
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- pass: No instruction to send local credentials anywhere
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Source
onewave-ai/claude-skills · 306 stars · license MIT · pushed 2026-09-23 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_qkspczuhkor63vwg GET https://markdownregistry.com/api/v1/resolve?ref=onewave-ai/claude-skills/jev-eval GET https://markdownregistry.com/api/v1/blob/c2f7fe3f473e83d1c8274ce754a5c5ae6fedb3a076787c3537c3645b5f17143a
Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
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