skills/meta/install/SKILL.md
Verify VexJoy Agent installation, diagnose issues, and guide first-time setup.
npx skillsauth add notque/claude-code-toolkit installInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Verify your VexJoy Agent installation, diagnose issues, and get oriented. Use after cloning the repo and running install.sh, when something seems broken (hooks not firing, missing commands), for first-time orientation, or after a git pull to verify nothing broke.
Goal: Run deterministic health checks and report results.
Step 1: Run install-doctor.py
python3 ~/.claude/scripts/install-doctor.py check
If the script is not found at scripts/install-doctor.py, try ~/.claude/scripts/install-doctor.py.
Step 1b: Run runtime health check
python3 ~/.claude/scripts/toolkit-health.py --json
If the script is not found at scripts/toolkit-health.py, try ~/.claude/scripts/toolkit-health.py. This surfaces runtime health (hook error rate, stale memory files) that install-doctor.py does not cover. It exits 1 when has_warnings is true — treat that as "warnings to surface," not a setup failure. Capture the flags array for Step 3.
Step 2: Interpret results
| Result | Action |
|--------|--------|
| All checks pass | Skip to Phase 3 (Inventory) |
| ~/.claude missing | Guide user to run install.sh — go to Phase 2 |
| Components missing | Guide user to run install.sh — go to Phase 2 |
| Hooks not configured | Guide user to run install.sh — go to Phase 2 |
| Broken symlinks | Symlink targets moved. Re-run install.sh --symlink --force |
| Python deps missing | Run pip install -r requirements.txt from the repo directory |
| Permissions wrong | Run chmod 755 on affected files |
Step 3: Display results clearly
Show the check output to the user with a clear pass/fail summary. Display the raw script output without paraphrasing or reformatting, because the script already formats diagnostics for readability and rewriting them risks losing detail or misrepresenting status.
Append the runtime-health result from Step 1b: if has_warnings is true, list each entry from the flags array as a WARN line in the summary (e.g. stale memory files, elevated hook error rate). When has_warnings is false, report runtime health as OK. WARN flags are advisory — they inform the user without blocking the pass/fail verdict for setup.
Gate: Health check complete. If issues found, proceed to Phase 2. If clean, skip to Phase 3.
Goal: Guide the user through fixing detected issues.
Step 1: Determine if install.sh needs to run
If ~/.claude is missing or components are not installed, the user needs to run install.sh. Tell them:
The toolkit hasn't been installed yet. Run this from the repo directory:
./install.sh --symlink # recommended: updates with git pull
./install.sh --dry-run # preview first
Wait for the user to confirm they've run it, then re-run the health check. This phase is interactive because installation changes system state -- always show the user what needs fixing and let them choose before acting.
Step 2: Fix individual issues
For fixable issues (permissions, missing deps), offer to fix them:
# Fix permissions
find ~/.claude/hooks -name "*.py" -exec chmod 755 {} \;
find ~/.claude/scripts -name "*.py" -exec chmod 755 {} \;
# Install Python deps (from repo directory)
pip install -r requirements.txt
Only run fixes the user approves, because automated fixes to ~/.claude can break an existing setup if assumptions about the environment are wrong.
Step 3: Re-check
After fixes, re-run:
python3 ~/.claude/scripts/install-doctor.py check
Gate: All checks pass. Proceed to Phase 3.
Goal: Show the user what they have installed.
Step 1: Run inventory
python3 ~/.claude/scripts/install-doctor.py inventory
Step 2: Display summary
Show the actual counts returned by install-doctor.py inventory -- never display hardcoded numbers, because component counts change with every install and stale numbers erode trust. Present them as:
Your toolkit is ready. Here's what's installed:
Agents: [N] specialized domain experts
Skills: [N] workflow methodologies ([N] user-invocable)
Hooks: [N] automation hooks
Commands: [N] slash commands
Scripts: [N] utility scripts
Gate: User sees their inventory. Proceed to Phase 3.5.
Goal: Show which MCP servers are available and their status.
Step 1: Run MCP registry check
python3 ~/.claude/scripts/mcp-registry.py list
If the script is not found at scripts/mcp-registry.py, try ~/.claude/scripts/mcp-registry.py.
Step 2: Display MCP status
Show the MCP inventory as:
MCP Servers:
[✓] Chrome DevTools MCP — Live browser debugging
Paired skills: wordpress-live-validation
[✓] Playwright MCP — Automated browser testing
Paired skills: wordpress-live-validation
[✓] gopls MCP — Go workspace intelligence
Paired skills: go-patterns
[✗] Context7 MCP — Library documentation lookups
Install: claude mcp add context7 -- npx @anthropic-ai/mcp-context7@latest
Use checkmark for connected MCPs and X for missing ones. For missing MCPs, show the install command.
Gate: MCP inventory displayed. Proceed to Phase 4.
Goal: Give the user their bearings -- what to do first.
Step 1: Show the three essential commands
Getting started:
/do [describe what you want] — routes to the right agent + skill
/comprehensive-review — 20+ reviewer agents in 3 waves
/install — run this again anytime to check health
Step 2: Show a few practical examples
Try these:
/do debug this failing test
/do review my Go code for quality
/do write a blog post about [topic]
/do create a voice profile from my writing samples
Step 3: Mention the docs
Documentation:
docs/QUICKSTART.md — 30-second overview
docs/REFERENCE.md — quick reference card
Gate: User is oriented. Installation complete.
The script hasn't been installed yet. Check if the user is in the repo directory. If so, run it directly from scripts/. If not, guide them to clone and install first.
Run chmod +x install.sh first.
The toolkit requires Python 3.10+. Guide the user to install Python for their platform.
documentation
Document translation: quick/normal/refined modes with chunked parallel subagents and glossary support.
development
AI image generation: Gemini and Nano Banana backends; single/series/batch workflows with prompt-to-disk.
testing
Unified voice content generation pipeline with mandatory validation and joy-check. 13-phase pipeline: LOAD, GROUND, STATS-CHECKPOINT, GENERATE, HOOK-GATE, VALIDATE, REFINE, VARIETY-GATE, JOY-CHECK, ANTI-AI, CLOSE-GATE, OUTPUT, CLEANUP. Use when writing articles, blog posts, or any content that uses a voice profile. Use for "write article", "blog post", "write in voice", "generate content", "draft article", "write about".
documentation
Critique-and-rewrite loop for voice fidelity validation.