tare-cli 0.4.0

Zero the scale before you weigh.
Prove your coding agent starts without your personal setup, then measure it.

Your coding agent knows you. Every run loads your global instructions, memories, skills, MCP servers and connected accounts. At work that is the point. In an evaluation it is noise. Compare runs with and without a skill on your own machine, and both runs carry your setup. You measure your machine, not the skill.

tare starts the agent in a room that holds nothing of yours and proves the room is clean before every run. It works with Claude Code, Codex, Pi and Google's Antigravity CLI on Linux and WSL, and with the first three on macOS.

$ tare probe claude
tare probe · claude 2.1.289 · /home/me/tare
  control   your real setup shows: instructions, home path, mcp, skills, plugins, email, reach
  leaks     none
  declared
    email        account email                                subscription login
    credentials  /home/tare/.claude-config/.credentials.json  copy of the login, needed by the CLI
tare: 0.00

tare: 0.00 is the zeroed scale. Nothing of your setup reached the model except what the reading declares. A reading with leaks names each one and the file it came from, and the agent does not start.

Quick start

uv tool install tare-cli            # or: pipx install tare-cli
tare probe claude                   # prove the room is clean
tare claude -- -p "fix the tests"   # run Claude Code in it

On Linux and WSL, tare also needs bubblewrap (sudo apt install bubblewrap). The install guide has the details per platform, and Reading a probe explains every line of the output above.

To compare agents, skills or models, tare calibrate, tare swap and tare cliff start many runs and open a live dashboard that leads with the answer:

Features

• Measures what the model receives. The probe records the request the agent sends to a fake model server on your machine. It makes no paid calls and never asks the agent about itself, because an agent asked about its own context can refuse or be wrong. How it works
• Knows when it cannot see. A control run starts the agent in your real setup. If the probe could not see your setup there, the reading is tare: not proven (blind) instead of a 0.00 that proves nothing. Reading a probe
• Keeps your files out and your login working. The room has no home directory of yours, no credentials of your other tools and no secret environment variables. It gets a fresh copy of your login, which tare deletes after the run. Running agents in rooms
• Compares skills, plugins and models. tare calibrate starts each side several times from scratch and reports pass rates with their intervals. Calibrate
• Scores what no test can decide. tare judge lets a second agent score a page by your rubric, without agent or model names, and tare judge-noise shows how far its scores vary. Judge
• Tells the workspace from the agent. tare swap lets two agents continue each other's unfinished work and reports whether the workspace or the agent decided the outcome. Swap
• Finds the step where a failed run was lost (experimental). tare cliff restarts the run from its saved steps and names the step after which it no longer passes. Cliff
• Live dashboard, cost and reruns. The dashboard leads with each run's answer and follows it live, each report ends with tokens and cost, and tare rerun repeats a run from its recipe. Dashboard and rerun · Tokens and cost

When to use

Documentation

Status

tare is on PyPI as tare-cli. Claude Code, Codex and Pi run on Linux, WSL and macOS; the Antigravity CLI runs on Linux and WSL, and stops with a message on macOS for now. What comes next is in the open epics.

Contributing

Every change starts as an issue. Larger work is an epic, split into features, and one epic becomes one branch and one pull request. AGENTS.md describes the process, the checks and the conventions. Run the tests with uv run pytest.

License

MIT. See LICENSE.

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