jev-integrate
From the author
Wire a System One model (Jev, or an open reproduction like Von) into a product feature — routing, guardrails, scoring, classification. Use when replacing an LLM call that returns a label rather than prose, when adding a typed decision to an agent loop, or when deciding between the hosted Jev API and a local open model. Covers question design, the eval-set-first workflow, threshold calibration, confidence gates, and the traps measured on real data.
Install
npx skills add OneWave-AI/claude-skills --skill jev-integrate -a claude-code
Installs to .claude/skills/jev-integrate/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
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Why 86
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Similar skills
Closest to this one by what they do and when they are used.
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- jev-auditOneWave-AI/claude-skillsAudit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.Planning★ 368
- building-with-jevnotque/vexjoy-agentWrite, compose, integrate, and improve programs that call Jev, TypeSafe's System One judgment model.Writing★ 443
- jev-decisionDevin-AXIS/iPolloWorkUse Jev to select among available tools, skills or agents, or evaluate outputs against explicit rubrics. Use when the user requests Jev or a bounded decision would help; not for open-ended writing or research.Science★ 7.2K
- awesome-jevAbdelStark/awesome-typesafe-jevDiscover and compare Jev and TypeSafe SDKs, integrations, applications, and evaluations. Use when selecting public resources for a typed-decision workflow or checking what evidence exists for a Jev claim.Productivity★ 603
- meta-evalagentscope-ai/OpenJudgeUse when the user wants to build an evaluation system for an LLM/agent application but doesn't know where to start — they have traces, prompts, RAG pipelines, or nothing at all. Also use when the user mentions evaluation, eval, benchmarking, testing LLM quality, measuring agent performance, assessing RAG accuracy, or wants to compare prompts/models. This skill is the entry router: it asks diagnostic questions then recommends which sub-skill (local workflow) to use next.Testing★ 887
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