VexJoy Agent: AI Agent Reference

September 5, 2026 · View on GitHub

Repository paths, schemas, and conventions for any AI agent working here.

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Repository Map

vexjoy-agent/
  agents/                  # Domain expert markdown files + INDEX.json
  skills/                  # Skill directories, each with SKILL.md
  hooks/                   # Python event hooks + lib/ shared modules
  scripts/                 # Deterministic CLI tools (Python + shell)
  commands/                # Slash-menu command definitions
  adr/                     # Architecture Decision Records (gitignored)
  docs/                    # Documentation (you are here)
  evals/                   # Evaluation test suites
  plugins/                 # UI panel plugins (custom-panel, example-panel)
  ~/private-skills/        # User private skills repo (separate private repo, discovered by sync hook)
  research/                # Research pipeline artifacts
  retro/                   # Retrospective data
  services/                # Service configurations
  templates/               # Scaffolding templates
  tests/                   # Cross-component test suites
  artifacts/               # Build/generated artifacts
  .claude/                 # Repo-local settings (gitignored)
  .local/                  # User overlay directory (gitignored)
  .local.example/          # Overlay templates shipped with repo

Component Types

Type	Location	What It Is
Agent	`agents/*.md`	Domain expert. Markdown file with YAML frontmatter. Gets loaded as system prompt when routed to.
Skill	`skills///SKILL.md`	Workflow methodology. Phased instructions with gates. Paired with an agent at routing time.
Hook	`hooks/*.py`	Python script triggered by Claude Code lifecycle events. Reads JSON from stdin, outputs JSON to stdout.
Script	`scripts/.py`, `scripts/.sh`	Deterministic CLI tool. No LLM judgment. Pure computation, file ops, API calls.
Command	`commands/*.md`	Slash-menu entry point. Maps `/command-name` to a skill invocation.

Full Component Inventory

For the complete inventory of agents, skills, and pipelines, query programmatically:

python3 scripts/routing-manifest.py --json      # all agents, skills, pipelines as JSON
python3 scripts/routing-manifest.py --compact    # compact text manifest for LLM context
python3 scripts/generate-agent-index.py          # regenerate agents/INDEX.json
python3 scripts/generate-skill-index.py          # regenerate skills/INDEX.json

/do classifies complexity (Trivial, Simple, Medium, Complex), selects a domain agent from agents/INDEX.json, pairs a skill, adds relevant enhancements (anti-rationalization, TDD, verification), and dispatches. Agents use judgment; scripts compute. Hooks inject context, record telemetry, and enforce checks at lifecycle boundaries.

Entry Point: `/do`

1. Parse request -> classify complexity (Trivial | Simple | Medium | Complex)
2. Check force-route triggers -> if matched, emit `Call the Skill tool with \`skill-name\`.`
3. Look up agent in agents/INDEX.json -> fallback to static routing table
4. Pair agent with skill (domain default or task-verb override)
5. Dispatch: agent executes with skill methodology loaded as instructions

Trivial = reading a file the user named by exact path. Everything else routes through an agent.

Hook System

Event Types

Event	When It Fires	Hook Can Block?
`SessionStart`	Session begins	No
`UserPromptSubmit`	Before processing user message	No
`PreToolUse`	Before a tool executes	Yes (exit 2)
`PostToolUse`	After a tool executes	No
`PreCompact`	Before context compression	No
`PostCompact`	After context compression	No
`TaskCompleted`	Task/subagent finishes	No
`SubagentStop`	Subagent session ends	Yes (exit 2)
`Stop`	Session ends	No
`StopFailure`	Session ends due to failure	No

Hook Output Format

{
  "hookSpecificOutput": {
    "hookEventName": "PostToolUse",
    "additionalContext": "injected into system context",
    "userMessage": "displayed verbatim to user"
  }
}

Sync Lifecycle

hooks/sync-to-user-claude.py fires on SessionStart when cwd is this repo. Copies into ~/.claude/ so Claude Code in other repos gets agents, skills, hooks, scripts. When ~/.codex/hooks/ exists, it also adds per-file symlinks for new hooks/*.py and hooks/lib/*.py there (never overwrites, never deletes; reported as .codex/hooks(+N linked, M current)).

Source	Destination	Sync Mode
`agents/`	`~/.claude/agents/`	File-by-file copy, stale removed
`skills/`	`~/.claude/skills/`	File-by-file copy, stale removed
`hooks/`	`~/.claude/hooks/`	File-by-file copy, stale removed
`scripts/`	`~/.claude/scripts/`	File-by-file copy, stale removed
`commands/`	`~/.claude/commands/`	Additive only (never removes)
`.claude/settings.json` hooks	`~/.claude/settings.json` hooks	Replace hook section
`.mcp.json`	`~/.mcp.json`	Merge servers (don't overwrite existing)

Routing Telemetry

Database: ~/.claude/learning/learning.db (SQLite, WAL mode, FTS5 full-text search)

Lifecycle

Decide: /do picks an agent and skill and stamps a [do-route] marker into the dispatch prompt
Record: routing-decision-recorder.py writes one decision row per marker on PostToolUse
Resolve: routing-outcome-finalizer.py scores the outcome on the user's next prompt; the SubagentStop recorder and the Stop fallback catch the rest
Report: learning-db.py route-health prints the loop's correctness metrics

The same database also holds the governance event log written by the safety gates and the voice corpus written by prompt-capture.py. Each subsystem owns its own topic.

CLI Quick Reference

python3 scripts/learning-db.py route-health
python3 scripts/learning-db.py route-stats --by agent
python3 scripts/learning-db.py route-delta --from SHA --to SHA
python3 scripts/learning-db.py stack-usage
python3 scripts/learning-db.py review-fps

Frontmatter Schema

Agent Frontmatter

---
name: {domain}-{function}-engineer
version: 2.0.0
description: |
  Use this agent when [trigger conditions].
color: blue | green | orange | red | purple
routing:
  triggers: [keyword1, keyword2]
  pairs_with: [related-skill]
  complexity: Simple | Medium | Medium-Complex | Complex
  category: language | infrastructure | review | meta
---

Skill Frontmatter

---
name: skill-name
description: |
  What this skill does and when to use it.
version: 1.0.0
user-invocable: true | false
context: fork                    # optional: run in isolated sub-agent
agent: golang-general-engineer   # optional: declare executor agent
model: sonnet | opus             # optional: model preference (consult `/do` SKILL.md Model Selection; Haiku retired)
allowed-tools: [Read, Write, Bash, Grep, Glob, Edit, Skill, Task, Agent]
routing:
  triggers: [keyword1]
  pairs_with: [related-skill]
  complexity: Simple | Medium | Complex
  category: process | content | pipeline | validation
---

Settings Architecture

File	Location	Tracked?	Purpose
settings.json	`~/.claude/settings.json`	No	Hook registrations, permissions. Replaced on sync.
settings.local.json	`.claude/settings.local.json`	No	Repo-local overrides (MCP permissions)
CLAUDE.md	Repo root	Yes	Global instructions for all sessions

Quick Reference: Routing a Request

User says "fix the failing Go tests"

1. /do classifies: Simple (code change)
2. Force-route check: "Go test" matches -> go-patterns (MANDATORY)
3. Agent: golang-general-engineer
4. Skill: go-patterns (force-routed, loads testing reference)
5. Enhancements: anti-rationalization-testing auto-injected
6. Plan: task_plan.md created (Simple+ complexity)
7. Dispatch: agent executes with go-patterns testing methodology
8. Record: the route decision and its outcome land in learning.db

Key Conventions for Operating Agents

Route through /do. Every non-trivial request enters via /do. Direct agent invocation bypasses routing logic and misses enhancements.
Agents think, scripts compute. If it's deterministic and measurable, there's a script. Use it instead of reasoning about it.
Load only what you need. Context is scarce. Load agent + skill + relevant references. Don't preload the full inventory.
Hooks enforce, agents comply. Hooks are the enforcement layer. Don't fight them. If a hook blocks, it's correct until proven otherwise.
Telemetry is automatic. Hooks record route decisions, outcomes, and governance events. Don't hand-record what hooks already capture.
Subagents for isolation. Complex tasks spawn subagents. Each gets fresh context with only what it needs.
Plans before complexity. Medium+ tasks require a plan file (task_plan.md). Don't skip planning.
Verify before declaring done. verification-before-completion exists for a reason. Run the checks.
Anti-rationalization is non-negotiable. If evidence contradicts your hypothesis, update the hypothesis.
Check prior failures. Search docs/what-didnt-work.md before repeating an experiment.