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Vexjoy Agent

VexJoy AI Agent with Jev Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop.

Install

$claude plugin install jev-auto-compact@jev-auto-compact -y

Install command taken from the project's README or registry entry. Always review code before running it.

VexJoy Agent

Essays and writing behind this toolkit live at vexjoy.com.

VexJoy Agent connects plain-English requests to specialist agents, skills, and workflows. /do selects the knowledge and tools needed for your task. Hooks enforce specific checks, and scripts handle repeatable work.

The aim is to give capable models useful domain knowledge without making you learn the toolkit's catalog.

43 agents, 62 skills, 78 hooks, 147 scripts. Agents carry domain knowledge, skills provide reusable methods, hooks enforce selected checks, and scripts handle repeatable plumbing.

Works across Claude Code (/do), Codex ($do), Factory (/do), Reasonix (/do).

What It Looks Like

$ claude

> /do debug this Go test

  Routing: go-engineer + systematic-debugging
  Phase 1/4: Reproduce: running test, capturing failure...
  Phase 2/4: Hypothesize: 3 candidates from stack trace...
  Phase 3/4: Verify: isolated root cause in connection pool timeout
  Phase 4/4: Fix: patch applied, test passing, PR opened

  ✓ Delivered: PR #847, fix connection pool timeout in health check

The router pairs a Go agent with a debugging skill, then follows the task through verification and delivery.

The Pipeline

  ROUTE        PLAN         EXECUTE      VERIFY       DELIVER      RECORD
 ┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐
 │ /do  │───▶│ Task │───▶│Agent │───▶│Tests │───▶│  PR  │───▶│Route │
 │Router│    │ Plan │    │+Skill│    │Gates │    │Branch│    │Result│
 └──────┘    └──────┘    └──────┘    └──────┘    └──────┘    └──────┘

/d — Jev-Powered Router

/d requires TypeSafe's Jev. Jev classifies the request, checks that the selected route preserves the requested outcome, and then dispatches the agent, skill, and pipeline.

Intent preservation is production behavior: /d restates the requested outcome and gates dispatch on a Jev receipt for that exact proposed intent, preventing an agent from quietly expanding an apple into an orchard.

Choose either transport:

# Preferred: Jev through Vercel AI Gateway
export JEV_TRANSPORT=vercel
export AI_GATEWAY_API_KEY=...

# Alternative: Jev's direct API
export JEV_TRANSPORT=direct
export TYPESAFE_API_KEY=...

JEV_TRANSPORT=auto is the default. It prefers Vercel when AI_GATEWAY_API_KEY is set, then uses the direct API when only TYPESAFE_API_KEY is set. An explicit transport never silently switches to the other one. The TypeSafe MCP plugin is not required for /d.

Intent-alignment judgments are recorded in learning.db without raw request text. Receipts keep the Jev judge model separate from the executing agent's configured model and effort level. View proposed-intent difference rates by agent profile, judge model, and transport:

python3 scripts/jev-intent-stats.py --days 30

Example:

> /d fix the flaky test in the payments module

  Intent alignment (/d):
    -> Restated outcome: Fix the flaky payments test without changing unrelated behavior.
    -> Jev: aligned

  ROUTING (/d): testing-automation-engineer + testing-preferred-patterns
  Source: jev (confidence: medium)
  Invoking...

Anti-Rationalization

Checks require evidence rather than confidence.

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