jev.nvim
Neovim 插件,根据自然语言问题对函数评分,并在 quickfix 中展示结果。
Neovim plugin that scores functions against a natural-language question and lists results in quickfix.
EXPLORE JEV
查找模型路由、代码审查、工单分类与智能体检查工具,了解判断结果如何进入工作流。
THE RESOURCE INDEX
项目、工具、教程与文章
原始链接,按用途整理。
支持中文与多关键词,例如:浏览器 DOM。
全部资源
Neovim 插件,根据自然语言问题对函数评分,并在 quickfix 中展示结果。
Neovim plugin that scores functions against a natural-language question and lists results in quickfix.
Git 提交信息钩子,检查提交说明与暂存改动是否一致,并检查可能的凭据。
Git commit-message hook that compares the message with the staged diff and checks for possible credentials.
Claude Code 的 Stop 钩子,结合会话证据检查未经验证的完成声明;发生错误时放行。
Claude Code Stop hook that checks transcript evidence for unverified completion claims; errors allow the turn to end.
使用 Jev 检查编程智能体的工作。
Software Factory Foreman: an agent supervisor that uses Jev decisions to keep coding agents on task.
带看板的代码审查流程。
A staged code-review workflow and local dashboard built with TypeSafe Jev.
为 Claude Code 和 Codex 的任务选择模型;需要 Node.js 20.12+、Jev 密钥和已登录的对应 CLI。
Per-turn model routing for Claude Code and Codex; requires Node.js 20.12+, a Jev key, and the corresponding CLI.
通过 MCP 插件进行本地代码审查。
Local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev.
Guardrails for Pi built on pi-typesafe that steer the agent instead of interrupting you: Jev judges irreversible and off-task tool calls, detects stuck loops, checks unverified done claims, flags slop
Go CLI and single-binary MCP server exposing TypeSafe judgments to Claude Desktop, Claude Code, and Codex.
TypeSafe Jev as a decision layer for the Pi coding agent: a measured tool-call gate plus jev_ask for typed, calibrated answers
Per-turn model & reasoning routing for Codex, driven by Jev (TypeSafe System One): picks the model, thinking depth and speed mode for every turn.
A calibrated context sieve for Claude Code: every tool result is judged by a System One model before it enters context.
Agent-ergonomic CLI for TypeSafe's Jev: fast calibrated judgments (pick, rate, check, rank, triage, guard) from the shell
Jev (TypeSafe System One) backed auto mode for the Pi coding agent: semantically auto-approves bash, write, and edit tool calls and fails closed when a decision cannot be made.
MCP server for TypeSafe Jev: typed classify, score, check, match and screen for any agent, with confidence on every answer
Configurable semantic linting powered by Jev, with file-level NOUL judgments and a magic-strings plugin.
TypeSafe (Jev) skill routing for Hermes Agent: names the one skill worth loading, before the model call. Opt-in, stdlib only, ~$0.001 per routed turn.
Semantic tool routing and typed System One decisions for the Pi coding agent using TypeSafe Jev
CLI and agent skill for TypeSafe System One (Jev): typed Choice, Score, and Noul judgments.
Minimal agent loop where Jev directs control flow and a LangChain chat model writes argument values and the final response.
Automated database migration safety reviewer powered by TypeSafe AI (Jev System One model)
Claude Code mod that routes decisions to TypeSafe's Jev model: ranks installed skills per prompt, and answers the agent's own this-or-that questions when confident.
MCP server exposing TypeSafe Jev as typed, calibrated judgment tools: classify, score, check, batched ask. Ships as a Claude Code plugin.
Semantic MCP firewall powered by Jev — screens every tool call, tool result, and tool description with calibrated System One verification. 94% block recall, 0 false positives, ~$0.00002/check.
Local proxy that picks the Claude model and effort per message using TypeSafe Jev. Routes subagents, leaves your cached main chat alone.
System-architecture skill for TypeSafe AI Jev/System One — find fuzzy semantic judgment and turn it into small Choice/Score/Noul primitives.
MCP server giving coding agents typed, calibrated judgments from TypeSafe's Jev model
TypeSafe AI (Jev) adversarial reviewer and typesafe_ask tool for the omp coding agent
Utilizing Jev, the RLCD-type model provided by TypeSafe AI, to independently and cheaply judge agentic coding sessions.
Predict another skill's next closed decision with TypeSafe Jev — without running that skill.
Fast semantic code search & diff sanity auditor for AI coding assistants (Antigravity, Cursor, Claude Code) powered by TypeSafe System One.
Prompt-injection and dangerous-action guard for coding agents (Claude Code, Codex, pi, ACP), powered by Jev
Per-prompt capability router for coding agents: resolves installed skills, MCP servers, agents and commands against your prompt via TypeSafe Jev, and measures whether the injection actually helps.
Open-source LLM router that uses TypeSafe's Jev to pick a model, on top of LiteLLM
Connect JEV to MCP clients and compare its judgments against general-purpose LLMs using shared datasets and measurable accuracy.
MCP server that puts TypeSafe Jev on the coding loop in Cursor, Codex, and any MCP client
Single-agent Pi coding coprocessor with Jev semantic gates, baseline-to-current diff review, and append-only observability telemetry.
A CLI and GitHub Action that assesses code-change risk using deterministic rules and TypeSafe Jev, recommending checks and reviewers before merge.
A Stop hook that stops your coding agent from stopping too early. Plain-language rules, judged by jev.
Reusable GitHub Action: agent fix loop gated by checks, an AI reviewer, and TypeSafe Jev
Guardrail + model router for LLM gateways on TypeSafe's Jev (System One model), with an independent accuracy/calibration/latency evaluation. Stdlib Python.
A pi extension that exposes TypeSafe (Jev, System One) judgments as five pi tools, so a model can make narrow semantic judgments while your code and your users keep control of thresholds, weights, and actions.
Autonomous Jev pull-request review with typed decisions, calibrated approval gates, and trusted-owner escalation
Experimental Hermes plugin: Jev-assisted model routing plans with budget and capability constraints. API access pending.
Typed judgment layer for coding agents — gates from PRD to ship. Jev-ready, provider-agnostic.
Grok skill: Jev as a judgment sensor in a builder-agent loop (priors × probabilities → next act)
MCP server exposing TypeSafe System One judgments (noul, choice, score) as agent tools
Experimental protocol for evidence-aware agent handoffs, with Jev-assisted review before results reach the lead agent.
A collection of skills for AI agents (Kiro, Cursor, Windsurf, Claude Code, and others). Each skill is a reusable module that teaches the agent to perform complex tasks with context, structure, and best practices.
A skill for writing and improving programs that call Jev, TypeSafe's System One model
Search the web with TypeSafe's Jev: source selection, query understanding and relevance ranking. Built with Search1API.
Rust CLI powered by Jev from TypeSafe.ai that ranks agent skills for the next step using live session context. Includes Claude Code hooks, structured JSON, abstention, and local feedback. Requires a TypeSafe API key.
A prose linter that sniffs out AI writing tells. Zero dependencies, countable rules plus one judgment model.
PoC: TypeSafe Jev as the reviewer for Hermes Agent smart command approvals. 8.7x faster, 4.4x fewer prompts, measured on 153 real commands. Approvals only.
Shift every LLM call to the cheapest model that can handle it. Routing decided by TypeSafe Jev in ~180 ms. No training data. Policy in plain YAML. TypeScript and Python.
Not every coding task needs your best model. Experimental Jev-powered model routing for Claude Code — V3 prototype runs today, V4 routes at the task boundary.
Cut Claude Code's skill manifest by ~75% with TypeSafe Jev. Scores every installed skill for relevance and hides the rest via skillOverrides — 12,750 → 3,185 tokens on a 217-skill install, for $0.0009 a session.
Map a codebase into units and let Jev (TypeSafe AI) hand an AI coding agent the ten files that matter for a task
An agent skill to discover TypeSafe Jev opportunities, design typed questions, and learn from recent community experiments.
Claude Code plugin that scores review findings, debug hypotheses and design options with TypeSafe's Jev — calibrated probabilities instead of one more opinion.
Agent skill that spots bounded-judgment steps, tries a typed decision model (TypeSafe's Jev) first, and documents every attempt
Hermes Agent skill whose north-star gate is judged by Jev (TypeSafe System One): turn an intention into a checkable finish line, generate the run prompt, and let Jev rank what is still unproven.
Stops AI coding agents from claiming work is done without evidence. Deterministic hooks decide, TypeSafe's Jev advises. Append-only ledger, zero runtime dependencies.
Paper trading agents on a live tape, decided every second by TypeSafe's Jev (System One). Electron desktop app.
Agent skill: design judgment-assisted systems with TypeSafe Jev (System One). Maps Choice/Score/Noul onto decision theory, reranking, and routing. Composition algebra, question design, validation gates. MIT.
Find the AI agent runs that broke because their environment did: missing keys, tools, files, permissions, network, or context. Powered by TypeSafe Jev.
Systematic software development framework for AI coding agents upgraded with TypeSafe Jev System One typed decisions
Structured code review for GitHub and Gitea Actions, powered by TypeSafe Jev and written in Rust.
Agente de trading para o mercado BTC Up/Down de 5 minutos da Polymarket: modelo em código, Jev (TypeSafe System One) como portão, ordens maker, calibração e shadows em paper
Input moderation for Mastra agents on TypeSafe Jev — one file
I kept watching coding agents burn context on decisions that aren't hard - triage 400 tickets, tag 600 files, route to one of six teams. jev-mode moves those verdicts to a typed-judgment model. I A/B'd it: 78% fewer tokens, 16x less work-attributable input, accuracy 96.1% vs 93.7%. Python, no deps, MIT.
Semantic linter for AI coding agents and CI code review. Detects silent failures, weakened tests, scope creep, unnecessary abstractions, and other semantic code smells.
A pre-execution gate for AI agent actions, powered by TypeSafe's Jev (System One) model.
Every tool call your agent makes, checked before it runs. A Claude Code plugin that uses TypeSafe AI's Jev to verify each pending tool call against the session plan, then allows it, asks you, or blocks it. Proof of concept
Stage the git hunks that match a sentence. Exact patch, preview first, staging only, decided per hunk by Jev.
Keeps the lines of a command's output that matter for the task. Exact text, full output recoverable, decided per line by Jev.
Rust CLI powered by Jev from TypeSafe.ai that ranks agent skills for the next step using live session context. Includes Claude Code hooks, structured JSON, abstention, and local feedback. Requires a TypeSafe API key.
A coding agent and personal assistant with System One reflexes (TypeSafe Jev) on top of the Pi coding agent
JevHarness: TypeSafe Jev agent tool-call gate. execute / confirm / reject in code.
A fully audited, reproducible decision‑theoretic mixture‑of‑agents framework comparing multiple selector strategies, including OpenJev, with transparent calibration, limitations, and verification artifacts.
Standalone TypeSafe Jev code-review CLI: typed decisions over a local git diff.
Agent-friendly Go library and JSON-first CLI for TypeSafe AI's Jev and System One API
Tag every line of a command's output, then trim by tag, so your coding agent reads the signal, not the noise. Design stage.
Jev (TypeSafe System One) × ASReview SYNERGY abstract screening demo — Choice/Noul vs gold labels
Six calibrated gates for Claude Code, judged by TypeSafe Jev: rules, scope, intent, done, claims, and commit honesty. Each one escalates, none ever approves.
Open auto mode for AI agents — a calibrated tool-call firewall powered by TypeSafe Jev. Ships as a Claude Code hook
Rust policy engine using TypeSafe Jev and deterministic checks to route AI agent decisions.
Semantic PR gate: .jev.yml rules as TypeSafe Jev questions. Not a review bot.
A Claude Code hook that asks whether the decision you are writing needs a model at all. Includes a measured 149-row comparison of TypeSafe Jev against Claude Haiku 4.5.
试试项目英文名、相关用途,或重置筛选。
部分项目补充了中文用途说明,原始描述保留供对照。在 GitHub 阅读完整目录 ↗