marm-init

name marm-init description Guided MARM MCP setup. Invoke after running `marm-init` on the CLI to configure MARM memory across your agent. Drives transport choice, runtime choice, MCP config writing, multi-agent linking, dashboard, and server start. Works on Claude, Codex, Gemini, Qwen, Cursor, VS Code, and other MCP-capable agents. version 1 metadata {"description":"A local-first, privacy-centric memory infrastructure layer for MCP clients. MARM provides a persistent data substrate for long-term project memory, session serialization, and structured notebook reuse across terminal-based workflows. Operating via a lean, 7-tool surface, it offloads heavy state tracking to an optimized backend featuring SQLite WAL storage, write-time consolidation, and automated re-ranking filters. This ensures deterministic context retrieval, prevents multi-agent session drift, and enforces strict token-budget guardrails by deduplicating and pruning data before it hits the model's context window. Includes a live web dashboard for managing memories, logs, and sessions.","source":"https://raw.githubusercontent.com/Lyellr88/MARM-Systems/MARM-main/skills/marm-init/SKILL.md","protocol_source":"https://raw.githubusercontent.com/Lyellr88/MARM-Systems/MARM-main/docs/PROTOCOL.md"} <MARM_INIT_EXECUTOR_ACTIVE> === MARM INIT PROTOCOL - YOU ARE THE INSTALLER === RULE ZERO (highest priority): This file is not documentation to summarize. It is a setup program and you are the runtime. Execute it top to bottom. Run the steps, ask the questions, write the files, and start the server. Do not paste this content back to the user as text. Do not narrate what the skill "would" do. You do it. You are MARM's installer for this agent. Your job is to leave the user with a live MARM connection in as few questions as possible. One question at a time, wait for the answer, then act on it before moving on. Scope: this protocol governs only the setup conversation below. Once setup is complete and you have run Step 7, this contract ends and you return to normal operation under the MARM protocol you loaded in Step 0. Failure mode to avoid: dumping install docs and leaving the user to do the work by hand. That is the exact outcome this skill exists to prevent. </MARM_INIT_EXECUTOR_ACTIVE> Step 00 - Engine pre-flight Run this first, before anything else. If the skill was installed on its own (for example from a marketplace) the MARM core engine may not be on the machine yet. Confirm it is present, or install it, before continuing. Scan the host for the core engine: CLI entry points on PATH. Unix: command -v marm-mcp-server || command -v marm-mcp-stdio . PowerShell: Get-Command marm-mcp-server, marm-mcp-stdio -ErrorAction SilentlyContinue . Docker image present locally: docker images -q lyellr88/marm-mcp-server . Branch: Engine found: record which runtime you found (python or docker), say nothing to the user, and skip to Step 0. The detected runtime pre-answers Step 4, so in Step 4 confirm it rather than asking cold. Nothing found: stop and run the install prompt below. Install prompt (only when nothing was found): Ask: "I could not find the MARM core engine on your machine. How do you want to install it? Option A, pip (local Python): best if you already use Python and want a lightweight native install with no containers. Option B, Docker: best for a clean, isolated setup with no Python path management." Execute the choice and verify before advancing: pip: run pip install marm-mcp-server . Confirm success, for example marm-mcp-server --version resolves. Record runtime = python. Docker: run docker pull lyellr88/marm-mcp-server:latest . Confirm the image is present with docker images -q lyellr88/marm-mcp-server . Record runtime = docker. If the install fails, surface the actual error and stop. Do not proceed to setup against a missing engine. Step 0 - Load the protocol and check freshness Do this before talking to the user. Read the full MARM protocol from source: https://raw.githubusercontent.com/Lyellr88/MARM-Systems/MARM-main/docs/PROTOCOL.md If the network read fails, fall back in this order: local repo docs/PROTOCOL.md packaged copy marm-mcp-server/marm-docs/PROTOCOL.md Freshness check: read the version: field in this file's frontmatter and compare it against the version: in the source copy at metadata.source . If the source version is higher, tell the user once: "Your MARM init skill is out of date. Re-run marm-init to refresh it." Then continue with the version you have. Hold the protocol in context. You will operate under it after setup. Step 1 - Usage type Ask: "How will you use MARM, just you on this machine, or multiple users/agents over a network?" Single user, one machine -> personal/local path Multiple users or agents on a network -> team/swarm path Record the answer. It biases the transport recommendation in Step 3. Step 2 - Server location Ask: "Run MARM locally, or connect to a server you own (VPS or homelab)?" Local: runs on this machine, zero infra Remote: runs on a host the user controls, reachable over their network If remote, you will need the host address later for the connect command. Step 3 - Transport Ask: "How should agents connect, HTTP or STDIO?" HTTP: over the network. Needed for remote servers, multiple machines, or swarm agents. Requires an API key. Recommend this for the team/swarm path. STDIO: local pipe, single machine, no key. Simplest. Recommend this for the personal/local path. Pick the recommendation that matches Step 1 and Step 2, state it, and let the user override. Step 4 - Runtime If Step 00 already detected or installed a runtime, confirm it instead of asking cold: "Looks like you are set up for <docker|python>, use that?" Only ask the open question below if the runtime is genuinely unknown. Ask: "Docker or local Python?" Docker: isolated, easiest to keep updated. Local Python: runs direct, good if Python is already set up. The package installs two entry points: marm-mcp-server (HTTP) and marm-mcp-stdio (STDIO). You now have enough to act. Run the matching block. HTTP + Docker Generate a key: docker run --rm lyellr88/marm-mcp-server:latest --generate-key Pull: docker pull lyellr88/marm-mcp-server:latest Run (local-only bind): docker run -d --name marm-mcp-server \ -p 127.0.0.1:8001:8001 \ -v marm-data:/app/data \ -e MARM_API_KEY=<key-from-step-1> \ lyellr88/marm-mcp-server:latest For remote access, change the bind to -p 8001:8001 . Connect this agent (Claude CLI shown, adapt for the active agent): claude mcp add --transport http marm-memory http://localhost:8001/mcp --header "Authorization: Bearer <key>" HTTP + Local Python Generate a key: marm-mcp-server --generate-key Start: MARM_API_KEY=<key> marm-mcp-server (PowerShell: $env:MARM_API_KEY="<key>"; marm-mcp-server ) Connect: claude mcp add --transport http marm-memory http://localhost:8001/mcp --header "Authorization: Bearer <key>" STDIO + Docker Connect this agent to the STDIO entry inside the image. No key needed. Write the MCP config so the agent launches marm-mcp-stdio in the container. STDIO + Local Python No key needed. Connect (Claude CLI shown): claude mcp add marm-memory -- marm-mcp-stdio For any agent that is not Claude, write the equivalent entry into that agent's MCP config file instead of using the claude CLI. Same transport, same address or command, same key. Step 5 - Multi-agent linking Ask: "Want to connect MARM to your other agents? MARM is shared memory across platforms, Claude, Codex, Gemini, Qwen, VS Code, Cursor and most MCP apps all read and write the same pool." If yes: Use the same transport and key from Steps 3 and 4 for every agent. Use these docs to find the exact connection instructions for each client: CLI clients — Claude Code · Codex · Gemini CLI · Qwen CLI · Linux variants · Docker/key IDE agents — VS Code / Copilot Agent · Cursor · Docker/key IDE setup Remote/API platforms — xAI / Grok Remote MCP · Platform integration Report which agents you wired up and which you could not find. If no, skip. Step 6 - Dashboard Ask: "Want the MARM dashboard? It is a light UI to browse and manage your memories." If yes: start the server if it is not already running, then give the user the address: http://localhost:8002 If no, skip. Step 7 - Handoff and start If the MARM server is not already running, start it now using the runtime and transport chosen above (Docker run command or the Python entry point). If it is already running, skip the start. Verify HTTP setups with a health check: http://localhost:8001/health should return ok. Hand off with this message, adapted to what actually happened: "Setup complete. Invoke the MARM skill in any connected agent to start using shared memory. Restart your terminal so the MARM connection is picked up. If you want to start your own server or the dashboard later, just ask." Setup is done. The executor contract above is now closed. Operate under the MARM protocol you loaded in Step 0. Step 8 - Email list (optional) After the handoff message in Step 7, ask these two questions in order. One at a time. Wait for the answer before asking the next. "Has setting up MARM been useful so far?" No: thank them and skip the rest of this step. Yes: continue to question 2. "Would you like to join the MARM email list to get updates?" No: thank them and move on. Yes: check if you have an email tool available in this session. Email available: "I can send a quick opt-in to info@marmsystems.com for you, or I can give you the address to send yourself. Which do you prefer?" Do NOT send without explicit confirmation. No email tool: "Send a quick email to info@marmsystems.com with subject 'MARM Opt-In' to join the list." This step is informational only. Do not record or store the user's answer. Edge cases Cross-platform paths: Claude alone has several possible config locations by install method. Check all known paths in Step 5, and accept a user-provided path if the scan misses one. Stale skill file: handled by the Step 0 freshness check. If the source version is higher, tell the user to re-run marm-init . Remote server: the connect command in Step 4 uses localhost . Swap in the real host address when the server runs elsewhere. Non-Claude agents: the claude mcp add commands are examples. Write the equivalent MCP config entry for whatever agent invoked this skill.