Does this skill require an API key?

Yes, a free-tier OpenCode Zen key stored in ~/.jev/zen.key or exported as ZEN_API_KEY. The author verified that the decision endpoint is callable directly from external scripts without paid credits.

Which judgments should not be offloaded to Jev?

The README says generation tasks, decisions that need multi-turn context, and one-off trivial judgments stay with the main model. Jev handles classification, screening, scoring and evidence checks instead.

What does one offloaded judgment actually cost, and how fast is it?

The author's measurements from 2026-09-20 across 10-plus real calls: 283-694 input tokens per call, cost 0 on the free tier, and about 2 seconds end to end. Four questions in one request ran in parallel normally, and Chinese state and criteria worked.

What does the bundled jev.py handle that a hand-rolled script gets wrong?

Four failure modes the README lists as hit in practice: transient HTTP 500 from the Zen gateway, which it retries once or twice; a bare urllib User-Agent getting blocked with 403, fixed by sending curl/8.9.1; GBK pipe corruption on Windows, fixed by reading sys.stdin.buffer as UTF-8; and 401s from asking for jev-latest instead of the permitted jev-1.13-free.

What is in the bundled Taobao comment-triage pipeline?

examples/comment_triage.py is pulled from a real lab-instrument shop on Taobao. New comments go through four parallel questions, is_question, category and priority among them, with questions routed to expert replies and chit-chat skipped, plus a compliance pre-check before anything is sent.

Why does batching matter more than the single-call price?

Because raw items stay in the script. Screening 50 comments through the main model pulls tens of thousands of characters into context, and every later turn is billed for them again, whereas this path returns only 50 lines of conclusions to the conversation.

How do I install it, and will the agent use it without being told?

Clone it into the agent's skills directory, ~/.claude/skills/jev for Claude Code or ~/.zcode/skills/jev for ZCode. Once installed the agent reads the trigger rules in SKILL.md and calls jev.py automatically on classification, screening, scoring and check tasks. It is MIT-licensed, adapted from the official typesafe-ai/skills SKILL.md.

CommunityJev Tools & CLIs

Jev Agent Skill

Claude Code and ZCode skill that offloads classify, route, batch-screen, score and compliance-check judgments to Jev through OpenCode Zen's free tier, bundling a zero-dependency jev.py caller and a Taobao comment-triage pipeline that keeps raw items out of the agent context.

See highlights

Open github.com

Stars: 0
Language: Python
License: MIT
Published by: Community
Last verified: Oct 2026

yuyang2230/jev-agent-skill

User repository on GitHub

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Free typed judgments for AI agents: offload classify/screen/score/verify to Jev (TypeSafe System One) via OpenCode Zen. Claude Code / ZCode skill. 给AI代理省token的免费决策分流技能

Forks: 1
Last pushed: Updated Sep 2026

Repo stats from the GitHub API, cached Oct 2026.

Tagscommunity claude-code skills token-optimization opencode-zen

Works forJev for Classification Jev for Routing Jev for Scoring Jev for Verification

Used inJev for E-commerce & Retail Jev for Customer Support

Languages: Python
Platforms: CLI
Capabilities: Open sourceRequires API key

Highlights

Bundles jev.py, a zero-dependency caller with transient-500 retry, WAF-safe User-Agent and GBK-pipe-safe UTF-8 stdin
Measured single calls used 283-694 input tokens at cost 0 with about 2s end-to-end in the author's September 2026 tests
Ships comment_triage.py, a production Taobao-shop pipeline that keeps raw comments in the script so only conclusions return to context
Batches up to four parallel questions per request; triggers automatically via SKILL.md rules on classification, screening, scoring and checks
MIT-licensed, adapted from the official typesafe-ai/skills SKILL.md

Watch out

MIT license. A single-author skill with no stars at verification; it depends on the OpenCode Zen free tier and reports the author's own measurements, so treat the bundled benchmark as one user's data.

Questions about this entry

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