Twin Me Up

Teach it once. Every agent you use works the way you would, and asks before it acts.

Quick start · One-prompt install · How it works · Works with · The research · Safety

Why

You probably use more than one AI agent. Each one starts from zero. It does not know what you care about, which corners you never cut, or which decisions you always want to make yourself. So you repeat yourself, correct the same mistakes, and hope nothing consequential happens before you look.

Twin Me Up is a small, local, open-source service that gives every agent the same answers to three questions:

1. How does this person want this kind of work done? Your confirmed guidance, criteria, exceptions and red lines, briefed to the agent at the start of work.
2. Am I allowed to do this? Explicit grants, scoped to a workflow and a set of actions, with optional expiry. Anything not granted returns ask. Optional hooks enforce that outside the model.
3. What did I just learn? Your decisions and corrections become candidate lessons. Only you can confirm them.

It is not a chatbot of you, a personality clone or an autonomous replacement for you. The evidence does not support those claims, and the research explains why.

What makes it different

Compared with what you might use today

Quick start

You need Python 3.11 or later and uv (or pipx). macOS and Linux are tested. Windows should work but is untested.

uv tool install twin-me-up        # or: pipx install twin-me-up

twinmeup init # your name and a passphrase, in your own terminal twinmeup connect --list # every supported agent system twinmeup connect claude-code # the exact steps for one of them, with your paths twinmeup doctor # check everything, including a live MCP handshake

twinmeup init asks for your name and a passphrase. The passphrase guards everything only you should do: confirming lessons, granting authority, exporting and erasing. Agents are never given it, and those commands refuse to run without it.

Only need the MCP server? After twinmeup init, any MCP client can start it with uvx twin-me-up. Prefer the latest code? uv tool install git+https://github.com/GitDakky/twin-me-up.

Upgrading? Run uv tool upgrade twin-me-up (or pipx upgrade twin-me-up), then twinmeup doctor. The FAQ explains what an upgrade changes for your twin.

Using Claude Desktop? After twinmeup init, download the .mcpb desktop extension from the latest release and open it. One bundle works on macOS, Windows and Linux.

Install with one prompt

Already working inside an agent? Paste this into it.

Copy-paste install prompt

Install Twin Me Up for me and connect it to yourself. Twin Me Up is a local, open-source

work twin: it learns how I make decisions, briefs you at the start of work and tells you when you need my approval. Repository: https://github.com/GitDakky/twin-me-up

Work step by step and show me each command you run.

1. Install it. Prefer uv: uv tool install twin-me-up If uv is not available, use pipx: pipx install twin-me-up If PyPI cannot be reached, install from GitHub instead: uv tool install git+https://github.com/GitDakky/twin-me-up Then check with: twinmeup version

1. Ask me to run twinmeup init myself, in my own terminal, and wait until I say it is done. It sets my name and a passphrase that only I know. Do not run it for me, do not ask me for the passphrase, and never type a passphrase anywhere.

1. Run twinmeup connect --list, pick the entry that matches you (or generic), then run twinmeup connect and carry out the steps it prints to register the MCP server and install the skill. If it offers optional enforcement hooks, show me the action map and the hook configuration first, and install them only if I agree.

1. Run twinmeup doctor and report the result.

1. If your system needs a new session to load MCP tools, tell me. Otherwise call twin_brief for workflow "general" and summarise what it says.

While you work with my twin:

• Call twin_brief at the start of a piece of work.
• Call twin_authorise before anything consequential: sending, paying, publishing, deleting or committing on my behalf. If the answer is "ask", prepare a draft, request my approval with twin_request_approval, and act only once twin_approval_status says it is approved.
• Give every write a request_id, so a retry never records anything twice.
• When I correct you, record it with twin_record_correction.
• Never try to confirm lessons, approve requests, grant authority, export or erase. Those are mine, and the twin will refuse you anyway.

The agent installs the package, connects itself and checks the result. The two steps that matter stay with you: choosing the passphrase, and approving any enforcement hooks. The prompt is also in docs/install-prompt.md.

How it works

flowchart LR

you([You]) twin[("Your twin local store")] agent["Any agent"] hook{{"Pre-action hook"}} you -- "confirm lessons grant and approve" --> twin twin -- "candidate lessons to review" --> you agent -- "twin_brief twin_authorise request approval" --> twin agent -- "record decisions and corrections" --> twin agent -. "every governed tool call" .-> hook hook -- "ungranted: block or ask" --> agent hook -- "checks grants" --> twin

The loop

1. Orient. At the start of work the agent calls twin_brief. It gets your confirmed guidance for the workflow, with boundaries and escalation rules always included, and a list of what it is authorised to do.
2. Check. Before anything consequential it calls twin_authorise. The answer is allow only when a live grant covers the action. Otherwise it is ask: the agent prepares a draft, requests your approval and waits for your decision.
3. Learn. When you make a decision or correct the agent, it records the decision or the correction and proposes lessons from it.
4. Ratify. You review the queue and confirm, revise or reject each lesson. Only confirmed lessons reach future briefs.
5. Prove. You freeze held-out decisions and test the twin against two baselines before trusting it with more.

Tools your agents get

What only you can do

These commands need your passphrase, so agents cannot run them.

Approvals

An agent that needs an action it has not been granted asks for it, then waits:

1. twin_authorise says ask. The agent prepares a draft and calls twin_request_approval with a one-line summary of what an