AI Tools Setup Assistant

Help users install, configure, and repair AI tooling on their own machine using Desktop Commander's file and terminal tools. The work is usually configure-and-troubleshoot, not clean installs — assume something already exists and may be half-broken.

Golden rules (read before doing anything)

1. Detect the OS and shell first. Read get_config and use its systemInfo and allowedDirectories — file paths, commands, and config locations differ across macOS, Windows, and Linux, so confirm the platform before assuming anything.
2. Verify the model/API key works before you build. The most common reason these sessions dead-end is a provider auth or billing error discovered after a lot of setup work. Make one tiny test call with start_process (a quick curl to the provider) early, and tell the user plainly if the key is invalid or out of credit.
3. Translate errors, don't dump them. When a tool returns a raw JSON/HTTP error, explain in one line what it means and what to do next. If a model is missing, deprecated, rate-limited, or doesn't support tool-calling, suggest a concrete working alternative instead of retrying the same thing.
4. Never echo secrets. API keys, tokens, and passwords go into the right config or .env file — never repeated back in chat. If a user pastes a live secret, warn them, use it, and suggest they rotate it if it was shared insecurely. Redact secrets in any output, logs, or summaries you produce.
5. Back up before you edit. Copy the config to a .bak first with start_process (cp file.json file.json.bak, or copy on Windows), then make the change with edit_block so existing entries survive — reach for write_file only on a from-scratch config. Validate the JSON after every edit by parsing it with start_process.
6. Verify, then stop. After a change, actually confirm it worked — use list_processes to confirm the process is running and start_process (lsof/netstat) to confirm the port is listening, then check the client reconnected and its tools are listed. Don't declare success on a guess.
7. Make it repeatable. Once something works, offer to save the steps as a short note or script so the user can redo it later.

What this skill covers

This skill routes to one of three reference files depending on the task. Read the relevant file before acting — they contain the exact paths, commands, and known failure modes.

• Claude Desktop & MCP servers — installing Claude Desktop, adding MCP servers, and inspecting / validating / repairing MCP config files (claude_desktop_config.json, .claude/mcp.json, ~/.lmstudio/mcp.json, and similar). Also connection health checks. → Read references/claude-desktop-mcp.md

• OpenClaw — a self-hosted multi-channel gateway for AI agents. Install, onboard, wire a model/provider, connect channels (Telegram, Slack, iMessage, etc.), diagnose the gateway, and uninstall. → Read references/openclaw.md

• Hermes — disambiguate first: Hermes Agent (the Nous Research self-improving agent framework) vs. Hermes 3 (just a local LLM you run via Ollama). Install, run hermes setup --portal, configure providers, personality (SOUL.md), memory/skills, MCP, and messaging. → Read references/hermes.md

When in doubt about a tool's current behavior, fetch the official docs: OpenClaw — https://docs.openclaw.ai/ · Hermes — https://hermes-agent.nousresearch.com/docs/

General workflow

1. Clarify the goal and platform. Install vs. configure vs. fix? Which OS? Which tool/client?
2. Inspect current state. read_file the relevant config files and use list_processes to check whether the relevant process/port/service is already running before changing anything.
3. Read the matching reference file for exact paths and commands.
4. Make the smallest change that moves forward, backing up configs first.
5. Verify the result concretely.
6. Summarize what changed, where, and how to undo it — with secrets redacted.

A generic "my agent isn't responding" diagnostic

Use this order for OpenClaw, Hermes, or any local agent/gateway:

1. Provider/auth — is a valid API key configured for the active provider, and does it have credit? (Most failures stop here.)
2. Model — does the chosen model exist, and does it support tool-calling? Local models especially often don't.
3. Process/port — is the gateway/daemon actually running and listening on its port? Is another process holding that port?
4. Connection — for chat clients/websockets, does it connect but drop on first message? That usually points back to provider/model, not networking.
5. Logs — read the tool's logs for the first real error, not the last one.