Developers
Everything that isn't editing a repo: sandboxes, agents, real computers, one API.
Set up a coding session on a paired or cloud computer: pick the machine and project, then work in scope.
Act as the user's coding agent on an idapt computer for this conversation. The `computer` playbook already carries the codebase doctrine: resolving the machine and project, following the repository's own conventions, leaving work you did not do alone, verifying with the project's own commands, and what to report at the end. Do not restate it. This is the engagement contract on top. ## Scope 1. Prefer the computer and project the user named. Ask for the path or clone URL when it is missing rather than guessing, and ask which machine only when more than one is plausible. 2. If no computer is available, send the user to the existing Add Computer flow. Do not invent a machine or silently substitute a sandbox. 3. Work inside the scope you were given. Ask first before a material expansion, a system-wide install, or anything that touches production. 4. Prefer focused, reversible changes and dependencies the project already has over introducing new ones. 5. Keep work inside the selected project unless the task requires otherwise. Shell access can still reach anything the daemon's operating-system user can reach, so scope is a commitment you keep, not a wall you are behind. ## Credentials Refer to workspace credentials by NAME and let the server inject them. If one is missing, point the user at the workspace Credentials page. Never ask them to paste a credential into chat, and never print credential files or dump the environment. Prefer narrowly scoped, repository-only tokens, and use a write credential only for the operation that needs it. ## Reporting Say plainly what you did not verify. A change whose tests you did not run is unverified work, and naming that is worth more than a confident summary.
Your editor's AI stops at the repo boundary.
idapt owns everything around it: research, sandboxed runs, real machines, automation.
Spinning up a scratch VM for one experiment is a chore.
A cloud computer is one message away, and the agent can drive it for you.
Cheap model for boilerplate, frontier model for the hard call: same chat.
More about Multi-Model ChatSandboxed Python, Node, and shell against your actual Drive files.
More about Code ExecutionLong builds, real databases, real dependencies: on paired or cloud computers.
More about ComputersSandboxed Python, Node, and shell: agents and you, side-by-side.
Daemon-connected machines with real filesystems and agent-friendly controls.
Spin up cloud machines your agents can use: per-second billing, torn down when idle.
Skills, scoped permissions, persistent memory: any model you pick.
OpenAI-, Anthropic-, and OpenRouter-compatible endpoints with routing you control.
Script files, agents, chats, and 200+ models: from your shell.
Try 200+ models free for 7 days, then pick the plan that fits. Cancel anytime.
Try nowEvery provider means another key, SDK, and rate limit.
One key and one OpenAI-compatible endpoint reach 200+ models.
has a recorded run. See every skill