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.

Hooks run at configured events. Skills state what to verify; blocking hooks enforce the checks they cover. Coverage depends on the runtime and tool path.

Knowledge Work Is First-Class

The content engine researches, drafts in a calibrated voice, checks 397 writing patterns, and adapts finished pieces for each platform. /html produces a self-contained report, deck, prototype, chart, or diagram.

It Proves Its Own Changes

Toolkit changes use direct review and relevant checks. Model comparisons can settle specific uncertainties; they are not required for every edit. PHILOSOPHY.md explains the validation policy. what-didnt-work.md records failed experiments, routing reversals, unvalidated A/B citations, disabled lint rules, and program refutations.

The automated nightly evolution loop (/evolve, writes to evolution-reports/) ran regularly through mid-May 2026. It is currently dormant; recent evidence has come from manual PRs instead.

Installation

git clone https://github.com/notque/vexjoy-agent.git ~/vexjoy-agent
cd ~/vexjoy-agent
./install.sh

Installs into ~/.claude/ and into ~/.codex/, ~/.factory/, ~/.hermes/, and ~/.reasonix/ when the runtime command is on PATH or its home directory exists. install.sh wraps the vexinstall engine (scripts/vexinstall/). Each SessionStart in the repo re-syncs through the same engine. Symlinks (default in a git checkout) follow git pull; --copy gives a stable snapshot.

Layout and safety rules: docs/installer-layout.md.

Want only part of the toolkit? Run ./install.sh --configure, or copy .local.example/profile.yaml to .local/profile.yaml and edit it. Without a profile, the full toolkit installs. Details: .local.example/README.md.

Jev Auto-Compact plugin (optional, requires TYPESAFE_API_KEY):

claude plugin marketplace add ./plugins/jev-auto-compact
claude plugin install jev-auto-compact@jev-auto-compact -y

Replaces generated compaction summaries with Jev-judged verbatim pruning after context reaches 60%. Inspect recorded before/after tokens and duration with python3 scripts/jev-compact-evidence.py.

Full setup: docs/start-here.md

claude --system-prompt "."

Four Layers

Full skill catalog: docs/skills.md.

┌─────────────────────────────────────────────────┐
│  SKILL.md                                       │
│  ┌─ Frontmatter ─────────────────────────────┐  │
│  │ triggers, pairs_with, success-criteria     │  │
│  └────────────────────────────────────────────┘  │
│  Reference Loading Table (conditional imports)   │
│  Phased Instructions (numbered, with gates)      │
│  Verification (evidence requirements)            │
└─────────────────────────────────────────────────┘

Built with the Toolkit

A game built entirely by Claude Code using these agents, skills, and pipelines:

Choose Your Path

I just want to use it Install, learn /do, done.

I do knowledge work Writing, research, data analysis, moderation, HTML artifacts. No code.

I'm a developer Architecture, extension points, adding agents and skills.

I'm an AI power user Routing tables, pipelines, hooks, telemetry DB.

I'm an AI agent Machine-dense inventory. Tables, paths, schemas.

Philosophy

• Outcome-first operation. Describe the result; routing selects the relevant catalog entries.
• Programs compute; models judge and generate. Use deterministic code where the answer is computable.
• Density. Every word carries instruction, rule, or decision. Cut everything else.
• Breadth over depth. Right context ensures correctness. Unfocused context adds cost.
• Structural enforcement. Exit codes enforce what instructions can't. Quality gates are automated, not advisory.
• Everything pipelines. Complex work decomposes into phases. Phases have gates. Gates prevent cascading failures.

Full design philosophy: PHILOSOPHY.md

Maintenance

One report-only script surfaces upkeep work; it prints a digest and never edits, deletes, or blocks.

• python3 scripts/stale-skill-scan.py --top 20 ranks stale skills and agents as pruning candidates. Run it quarterly; see docs/deprecation-template.md.

Scheduled work follows the same boundary as everything else: judgment uses models; repeatable plumbing uses 147 scripts.

Contributing

See CONTRIBUTING.md.

License

MIT. See LICENSE.