技能潮 SkillTide 提交收录
官方说明7 个来源MCP 服务8 天前
jigyasudham/veto

jigyasudham/veto

Specialist agent council for AI coding CLIs
在 GitHub 查看 ↗直链 · 无中转页
配置模板(请按你的客户端调整)
claude mcp add veto -- npx -y veto
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
4 ★ · jigyasudham/veto · 取自该仓库的 GitHub 页面 · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
评分构成
综合分 703 · 4 ★ · 7 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 jigyasudham/veto;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

veto

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

> **93 agentic tools. 49 specialists. Every major AI CLI. Self-learning. Zero extra cost on subscriptions.**
An MCP server that runs locally on your machine, plugs into Claude Code, Codex CLI, Gemini CLI, Antigravity CLI, Cursor, Windsurf, Zed, and JetBrains using your existing subscriptions — giving every AI a council of specialist agents, local LLM support, SDD agents, playwright automation, persistent cross-platform memory, a self-learning router that re-tunes its tier thresholds automatically every 20 recorded task outcomes (reviews record outcomes for you; configurable via auto_apply_learning), CI/CD gates, workspace discovery, and bidirectional IDE communication.
> **Billing note:** "Zero cost" applies to subscription plans (Claude Max, Gemini Advanced, etc.). If you are on API/pay-per-token billing, LLM reasoning done for Veto agents (via the agentic loop or MCP Sampling) counts toward your token usage like any other turn. veto init detects API key environment variables and warns you automatically.
---
Getting Started
Prefer not to install anything? Every command also works via npx:
The two are independent by design. veto init writes MCP configs that launch the server with npx -y --package @jigyasudham/veto@latest veto-server — npx re-resolves @latest against the registry on every client restart, so the **MCP server auto-updates itself** and a global copy can never pin it to an old version. The **global install only provides the CLI** (veto doctor, veto sessions, the statusline, …); keep it current with npm i -g @jigyasudham/veto@latest when veto doctor says it's behind.
---
How the Agents Work
**No API keys, zero extra cost.** Every worker agent is a deterministic expert module at its core, with two optional layers of LLM reasoning on top — all of it delegated to the AI you're already paying for.
Default — Deterministic expert modules
Out of the box, each of the 42 worker agents runs as a hand-written expert module (plan() / analyze() in src/agents/) — **not** an LLM. They always run, work offline, and cost zero tokens. Their depth varies by design: analysis agents (security scanner, secrets, dependency audit, clone detector, privacy, auth, performance, compatibility, …) apply real algorithms — regex/AST detection, OWASP/CWE rules, hash-based clone matching — while planning agents (coder, debugger, tester, …) are structured expert playbooks: curated steps, checklists, and pitfalls for the task category, built to be reasoned over by the AI you already pay for rather than to reason themselves.
Path A — Agentic loop (most clients: Claude Code, Cursor, Windsurf)
Veto returns the specialist's role, rubric, and an output contract as an llm_upgrade prompt. The host AI reasons as the specialist and passes structured JSON back to complete the operation. This is the primary path — it costs nothing beyond your existing subscription and works on every client.
Path B — MCP Sampling (clients that support server.createMessage)
Where Sampling is available, the same upgrade happens server-side without the extra round-trip. Note: the July 2026 MCP spec revision deprecates Sampling protocol-wide (12-month sunset), so the agentic loop (Path A) is Veto's long-term default; Sampling remains a transparent optimization where it exists.
The 7-agent **Council** is LLM-first — its value is the multi-agent debate — but it too falls back to a deterministic verdict when no LLM path is available. When multiple agents run, they execute in parallel.
---
Specialist Roles
**49 specialists: 42 deterministic worker agents across 6 domains + a 7-agent Council.** The Council debates trade-offs before you build; the worker agents do the hands-on analysis and planning. Each is a deterministic expert module that can upgrade to LLM reasoning — see How the Agents Work. List them anytime with veto agents.
**Council (7)**
Lead Dev · PM · Architect · UX · Devil's Advocate · Legal · Security
**Development (12)**
Coder · Code Reviewer · Tester · Debugger · Refactor · Database · API · Frontend · Backend · DevOps · Performance · Migration
**Security (6)**
Security Scanner · Auth Agent · Data Privacy · Secrets Agent · Dependency Audit · Penetration Tester
**Memory (5)**
Context Manager · Decision Logger · Project Mapper · Pattern Learner · Knowledge Base
**Research (7)**
Researcher · Tech Advisor · Cost Analyzer · Competitor Analyzer · Risk Assessor · Estimator · Ethics & Bias
**Quality (5)**
Code Quality · Documentation · Accessibility · Compatibility · Error Handling
**Workflow (7)**
Task Planner · Task Coordinator · File Manager · Git Agent · Search Agent · Reporter · Automation
来源
awesome · https://github.com/jigyasudham/veto
按领域浏览
MCP 服务
Traction
4 ★
收录于
昨天

更多MCP 服务

查看MCP 服务全部 5,525 条 →

所属领域与标签

每个标签都能点进去看同类的全部条目。数字是当前库里的真实条数。

浏览全部领域 →

← 返回索引