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Agent skill

Onboard

by ThinkfleetAI in ThinkfleetAI/memmesh

Set up MemMesh for a new project — verify the MCP server is wired, pick local vs hosted, import any existing project knowledge (MEMORY.md, CLAUDE.md, a mem0 export), and seed initial scopes.

Apache-2.0Auto-check passedAgent Workflows

Install Onboard

skills CLI
$ npx skills add ThinkfleetAI/memmesh --skill onboard -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ThinkfleetAI/memmesh onboard --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ThinkfleetAI/memmesh.git skills-src && mkdir -p .claude/skills && cp -r skills-src/integrations/memmesh-plugin/skills/onboard .claude/skills/onboard && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
onboard
GitHub stars
420
Token cost
~357 tokens
SKILL.md length
142 words
Files
1
Skills in repo
24
Repo updated
First seen
Licence
Apache-2.0

At a glance

Set up MemMesh for a new project — verify the MCP server is wired, pick local vs hosted, import any existing project knowledge (MEMORY.md, CLAUDE.md, a mem0 export), and seed initial scopes.

  • Works in 4 steps: Verify wiring → Local or hosted? → Seed existing knowledge → …
  • Tasks that involve Agent instruction files
  • SKILL.md covers 1. Verify wiring, 2. Local or hosted?, 3. Seed existing knowledge and 4. Confirm
  • Calls npx; needs MEMMESH_API_KEY

What it does

Onboard is an agent skill from ThinkfleetAI/memmesh. Set up MemMesh for a new project — verify the MCP server is wired, pick local vs hosted, import any existing project knowledge (MEMORY.md, CLAUDE.md, a mem0 export), and seed initial scopes. Use on first run in a repo, when the API key changes, or to re-run setup after config changes.

Its SKILL.md is about 360 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Agent instruction files. It works with Mem0 and Model Context Protocol. The repository describes itself as: Persistent, self-improving memory for AI agents. Local-first Rust memory engine with MCP support. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Agent instruction files

Example prompts

  • “/onboard”

Requirements

  • Node.js
  • A credential in MEMMESH_API_KEY

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Verify wiring
  2. Local or hosted?
  3. Seed existing knowledge
  4. Confirm

What it can do on your machine

Read from SKILL.md and the folder at commit bba48f8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • MEMMESH_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Onboard loads about 357 tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 142 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~73
When it runs · the whole SKILL.md, loaded when a task matches
~357

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ThinkfleetAI/memmesh at commit bba48f8, republished under its Apache-2.0 licence (© ThinkfleetAI). 142 words, ~357 tokens.

Download SKILL.mdSave it as .claude/skills/onboard/SKILL.md (or your agent's skills folder).
name
onboard
description
Set up MemMesh for a new project — verify the MCP server is wired, pick local vs hosted, import any existing project knowledge (MEMORY.md, CLAUDE.md, a mem0 export), and seed initial scopes. Use on first run in a repo, when the API key changes, or to re-run setup after config changes.

onboard

Get a project ready to use MemMesh.

1. Verify wiring

bash
memmesh doctor        # binary + MCP config + skill presence

If the MCP server isn't connected, run npx @thinkfleet/memmesh install (see the memmesh-cli skill), then re-check.

2. Local or hosted?

  • Local (default): SQLite, no key, offline. Good for dev tools.
  • Hosted: set MEMMESH_API_KEY / sync.url in ~/.thinkfleet-memory/config.toml. Needed for cross-device sync, the full SDK, and server-side prediction/verticals.

3. Seed existing knowledge

If the repo already has durable context, import it (see the import skill): MEMORY.md, CLAUDE.md, an ADR folder, or a mem0 export (then consider memmesh-migrate for a full switch). Feed each item via memory_observe so the engine extracts + builds the graph, scoped project.

4. Confirm

Run stats to show what's now in scope, and remind the user of the two-rule loop: the agent will observe their messages and recall at session start automatically (the always-on memmesh skill). Nothing else to configure.

© ThinkfleetAI, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in integrations/memmesh-plugin/skills/onboard of ThinkfleetAI/memmesh.

Open the folder on GitHubat commit bba48f8

Compare with similar skills

Onboard next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Onboard compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Onboard this skillThinkfleetAI/memmesh420—~357Automated safety check: PassApache-2.0
Agent Setup Health Audittw93/Waza7.2k—~5.2kAutomated safety check: NotesMIT
Semantix GuideGnosil/semantix821—~2.1kAutomated safety check: PassMIT
Working With Claude Code Docsobra/superpowers-developing-for-claude-code142—~1.5kAutomated safety check: PassNone
Agnixagent-sh/agnix445—~874Automated safety check: PassApache-2.0
Claude Docs Consultantcentminmod/my-claude-code-setup2.7k—~959Automated safety check: PassMIT

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More from ThinkfleetAI/memmesh

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  • Behaviors

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  • Graph

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Works with

Categories

Questions about Onboard

What does Onboard do?

Set up MemMesh for a new project — verify the MCP server is wired, pick local vs hosted, import any existing project knowledge (MEMORY.md, CLAUDE.md, a mem0 export), and seed initial scopes. Onboard is an agent skill from ThinkfleetAI/memmesh.md, a mem0 export), and seed initial scopes.

When should I use Onboard?

Onboard fits situations like: tasks that involve Agent instruction files.

How do I install Onboard in Claude Code?

Run `npx skills add ThinkfleetAI/memmesh --skill onboard -a claude-code`. Or copy the skill folder (integrations/memmesh-plugin/skills/onboard in ThinkfleetAI/memmesh) into .claude/skills/onboard in your project. Claude Code loads it when a task matches its description.

How do I install Onboard in Codex?

Run `npx skills add ThinkfleetAI/memmesh --skill onboard -a codex`. Or copy the skill folder (integrations/memmesh-plugin/skills/onboard in ThinkfleetAI/memmesh) into .agents/skills/onboard in your project. Codex loads it when a task matches its description.

Can I use Onboard in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ThinkfleetAI/memmesh --skill onboard -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/onboard, .gemini/skills/onboard, .github/skills/onboard and .opencode/skills/onboard in your project.

What does Onboard need to run?

Going by SKILL.md and its folder, Onboard needs the command-line tools its instructions call (npx) and credentials named MEMMESH_API_KEY. Our summary lists: Node.js; A credential in MEMMESH_API_KEY.

Does Onboard access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Onboard safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Onboard use?

Onboard is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Onboard use?

About 357 tokens (SKILL.md is roughly 1.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Onboard?

Skills that share tags, products or a category with Onboard: Agent Setup Health Audit (tw93/Waza, 7.2k stars), Semantix Guide (Gnosil/semantix, 821 stars), Working With Claude Code Docs (obra/superpowers-developing-for-claude-code, 142 stars) and Agnix (agent-sh/agnix, 445 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Onboard?

ThinkfleetAI (a GitHub organization) maintains it in ThinkfleetAI/memmesh, which has 420 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on August 25, 2026.

Source: ThinkfleetAI/memmesh on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.