DeRonin Jev Skills Workflow

项目摘要

Jev 安装指南及适用场景说明，涉及 coding agent 集成。

来源原文

How to use Jev, and where it actually gives you the 100x: setup takes 10 minutes: 1. join the waitlist, people are getting approved same day 🔗 https://typesafe.ai 2. install the official skill so your agent writes correct calls: - npx skills add typesafe-ai/skills --skill typesafe-ai on Claude Code it's two commands, the marketplace add on its own doesn't install anything: - claude plugin marketplace add typesafe-ai/skills - claude plugin install typesafe@typesafe-ai 3. create an API key in the dashboard 4. in your prompt just say: "use the TypeSafe skill" now the part nobody is posting: the 100x isn't the model, it's where you put it you don't get it by swapping your LLM for Jev you get it by deleting the calls that never needed a language model open your agent and find every call that just picks something: > which tool next > is this spam > is this chunk relevant > does this need a human > is this diff risky none of those are writing tasks they're if statements you outsourced to a frontier model here's the upgrade, in order: 1. replace each one with a typed question Choice picks from up to 255 options, Score places it on a 2-10 level scale, Noul returns a raw 0-1 2. batch them questions in one call run in parallel and barely move the latency, and output tokens are free so ask every question you might need, including the ones you'll throw away 3. threshold on confidence, not on the answer under 0.5 escalate to a big model or a human 0.85+ before anything irreversible 4. never let it invent options build the candidate list in code, from the DOM, the retriever, the tool trace then let it pick 5. put it in the loop, not next to it router picks the cheap model, gate checks the tool call before it runs, judge verifies the output after that's where the heaviest calls in your agent are hiding 6. start with compaction tonight score every tool call, drop the dead ones, keep the survivors verbatim instead of a lossy summary lowest effort win available and you'll see it on tomorrow's bill the honest part: text only right now, no images, no audio and on broad benchmarks it loses to frontier models but somebody ran 18,514 emails through it zero-shot and got 98.33% against a TF-IDF classifier trained on 14,800 labelled examples that got 98.39% no training data, $1.12 total it wins on narrow, well specified decisions which is most of what your agent is actually doing all day today gonna share use case how i integrated it to content creation and how i find winning meta ads now in a seconds...

原记录的限制

• Implementation details are based on the author's public post on X.
• Performance figures and benchmarks are author-reported community claims.
• Production deployments may require custom calibration and policy thresholds.