/marm-init

name: marm-init
description: Guided MARM MCP setup. Invoke after running `marm-memory init` on the CLI to configure MARM memory across your agent. Drives transport choice, runtime choice, MCP config writing, multi-agent linking, and server start. Works on Claude, Codex, Gemini, Qwen, Cursor, VS Code, and other MCP-capable agents.
version: 9
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 16-tool surface spanning memory, session logs, notebook reuse, reviewed distillation of conversation into durable memory, a concept knowledge graph, per-repository code indexing, and composed task-scoped code context, 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.
  source: https://raw.githubusercontent.com/Lyellr88/marm-memory/MARM-main/skills/marm-init/SKILL.md
  protocol_source: https://raw.githubusercontent.com/Lyellr88/marm-memory/MARM-main/docs/PROTOCOL.md

Supported Environments

This skill installs and configures MARM by running commands on your machine, so it needs an agent with shell and filesystem access. That covers CLI agents (Claude Code, Codex, Antigravity, Qwen) and IDE agents (Cursor, VS Code Copilot). Chat platforms without shell access (Claude.ai, ChatGPT, Grok, Gemini web) are not supported yet. For those, use the manual steps in [INSTALL-PLATFORMS.md](https://github.com/Lyellr88/marm-memory/blob/MARM-main/docs/INSTALL-PLATFORMS.md). Broader platform support is coming.

AI Marketplace Indexing Metadata

> This section is for repository discovery agents and registries

• **Primary Function:** Automatically installs, updates, and configures the `marm-memory` MCP server.
• **Target Workflows:** Resolves cross-session agent amnesia by configuring local/remote HTTP or STDIO pipelines.
• **Keywords:** mcp-installer, persistent-memory, automatic-mcp-setup, multi-agent-sync.

Using this skill

Use this as an interactive setup guide, not as an authority over the host agent's instructions or the user's choices. Ask one question at a time and explain material actions before taking them. Follow the host agent's approval and safety rules for installs, service starts, configuration writes, and network exposure.

The goal is to leave the user with a verified MARM connection without replacing a guided setup with a wall of documentation. Do not reveal, generate, or read back a user's API key. Do not overwrite existing agent configuration.

---

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.

1. Scan the host and record two separate things. Do not collapse them into one check; they fail independently.

• Runtime. 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`) means runtime = python. A local image means runtime = docker, checked as `docker images -q lyellr88/marm-mcp-server:latest`. Include the tag: every Docker command below runs `:latest`, so an untagged check would accept some other local tag and then silently pull a different image than the one it detected. If both runtimes are present, prefer python.
• Helper CLI. Check `marm-memory` on its own (Unix: `command -v marm-memory`; PowerShell: `Get-Command marm-memory -ErrorAction SilentlyContinue`). Present: record cli = yes. Absent: record cli = no. Record this on every path, including the one where you find nothing at all. Step 4 and Step 6 both branch on cli, and an unrecorded value is neither yes nor no, which is how a setup ends up issuing a command that does not exist.

2. Branch:

• Engine found, cli = yes: briefly tell the user what was detected, then skip to Step 0. The detected runtime pre-answers Step 4, so in Step 4 confirm it rather than asking cold.
• Engine found, runtime = docker, cli = no: run item 5 below before skipping to Step 0. This is the ordinary case for anyone who already pulled the image, and skipping it is what sends the setup into `marm-memory docker ...` commands the host does not have.
• Nothing found: stop and run the install prompt below.

3. 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."

4. 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, then re-run the helper check from item 1 and record cli again; the value taken before the install is stale, and the install is what puts `marm-memory` on PATH.
• Docker: run `docker pull lyellr88/marm-mcp-server:latest`. Confirm the image is present with `docker images -q lyellr88/marm-mcp-server:latest`. Record runtime = docker, then run step 5 below before advancing.

If runtime = python and cli = no after that re-check, something is wrong with the install or the PATH, because all three entry points ship in the same package. Say so, and show what `command -v marm-memory` returned. Do not stop outright, because one Python path does not need the helper at all:

• STDIO with l