| name | new_user |
| description | Welcome new users to DeepWork — introduce features, set up reviews, and optionally record a first workflow |
| disable-model-invocation | true |
New User Onboarding
Guide a new user through what DeepWork can do and help them get started.
Flow
0. Check dependencies and run setup
0a. Check for uv
The DeepWork MCP server requires uv (specifically uvx). Check if it is installed:
command -v uv
If uv is NOT found, install it.
On macOS, warn the user first:
Heads up: when uv installs its Python runtime and resolves packages, macOS may pop up permission dialogs for Photos, Dropbox, or other locations outside this project. This happens because the installer briefly scans system paths. These are safe to deny — just click "Don't Allow" on any that appear.
Then install uv. Check if brew is available first (command -v brew):
After installing, check if uv is on the current PATH:
command -v uv
If uv is still not found (the installer adds it to shell config but the current shell doesn't have it yet), tell the user:
uv was installed but isn't available in this shell session yet. Please exit Claude (type /exit), close this terminal, open a new terminal in the same directory, and run claude -c to resume where we left off.
Stop here — do not proceed. The user will come back in a new session.
If uv is found, verify it works:
uv --version
Set UV_WAS_INSTALLED=true (you will need this later).
0b. Reload if uv was just installed
If UV_WAS_INSTALLED=true (from step 0a), the MCP server could not have started when this session began because uvx was missing. Use AskUserQuestion to tell the user:
I just installed uv, which DeepWork's MCP server needs to run. For everything to work, please type /reload-plugins now, then come back and tell me it's done.
Wait for the user to confirm they have reloaded. Do not proceed until they confirm.
0c. Run setup
Run the setup command to configure Claude Code settings (marketplace, plugin, MCP permissions, auto-update):
uvx deepwork setup
Proceed regardless of the output.
0d. Verify the MCP server is running
Call get_workflows (using the mcp__plugin_deepwork_deepwork__get_workflows tool). If it succeeds, the server is healthy — continue. If it errors, tell the user:
The DeepWork MCP server isn't responding. This usually means uv isn't on your PATH or the plugin needs a restart. Try quitting Claude Code completely and reopening it, then run /deepwork:new_user again.
Stop the onboarding if the server is not reachable — continuing without it will just produce more confusing errors.
1. GitHub star (optional)
Check if the gh CLI is installed by running which gh.
If gh is available, use AskUserQuestion to say something like:
Thanks for installing DeepWork! Would you mind starring the repo on GitHub so you get notified about updates?
If they agree, run:
gh api -X PUT /user/starred/Unsupervisedcom/deepwork
If gh is not installed, skip this entirely — do not mention it.
2. Introduce DeepWork
Print a brief welcome message explaining what DeepWork does. Lead with the core value proposition: DeepWork makes AI agents reliable. It gives you mechanisms to be assured that Claude will do the right things in the process of achieving your requests. Keep it concise — a few sentences, not a wall of text. Then cover the three main capabilities:
- Workflows — structured, multi-step processes with quality gates. Do a task once with Claude, then turn it into a repeatable workflow. Examples: competitive research, tutorial writing, API audits, monthly reporting.
- Reviews — automated code review rules that run against every change. Define what to check for in
.deepreview configs, then run /review. Catches regressions, style issues, doc drift, security problems.
- DeepSchemas — file-level contracts that validate structure and requirements at write time. Define once, enforce everywhere.
Mention that these three layers work together: workflows enforce process, schemas enforce file contracts, reviews verify output.
3. Review rules (for code projects)
Check if this looks like a code project (e.g., has source files, a package.json, pyproject.toml, Cargo.toml, go.mod, a src/ directory, etc.).
- If it is a code project: use
AskUserQuestion to explain that DeepWork can set up automated review rules for the project — these run every time /review is called and catch issues automatically. Ask if they'd like to set up review rules now.
- If yes: invoke the
/deepwork:configure_reviews skill.
- If no: continue to the next step.
- If it is NOT a code project (or you can't tell): skip this step.
4. Offer to record a workflow
Use AskUserQuestion to explain what workflows are in a bit more detail:
Workflows let you capture a multi-step process and replay it reliably. You do the task once — research, analysis, report writing, whatever it is — and DeepWork turns it into a structured workflow with quality gates. Next time, the agent follows the exact same process.
Would you like to record a workflow now? You'll just do the task like normal, and when you're done we'll turn it into a reusable workflow.
If they say yes, invoke the /deepwork:record skill.
If they say no, let them know they can run /deepwork:record anytime to get started, and that /deepwork is the main entry point for all DeepWork features.
