For agents

Markdown copies

Every page has Copy page, which copies it as Markdown, ready to paste into an agent's context.

llms.txt

llms.txt is the plain-text index of this documentation for language models, followed by the full text of every page. It is served at https://trialone.dev/llms.txt.

A working pattern

1. list_issues, then get_issue for the one to fix. Without a fingerprint, get_issue returns the newest open issue.
2. Change the files the recipe names. If the issue is routed as physical, stop and tell the user who has to go to the robot.
3. claim_repair with the fingerprint, what changed, and the commit or pull request link. This automatically queues a check on each affected robot; supply machines for a staged rollout. Read any setup blockers in the response.
4. For a native Linux executable repair, supply verification_target: the built executable SHA-256 and measurable scenario activity. It can be included in claim_repair or added to the pending check with request_verification. A Git commit alone is not deployment evidence. Python, plugin/library and hardware changes need additional deployment adapters and remain unverified by this mechanism.
5. Deploy the change and record the triggering scenario in a ROS2 bag or MCAP with wall-clock timestamps. With TrialONE connected and the recording folder configured, it observes the running executable and checks completed recordings for up to 24 hours. Missing runtime or scenario evidence cannot confirm the repair. No manual closure is needed.
6. check_repair returns progress per robot and the measured result, including remaining failures. Continue the investigation in the agent. Use request_verification to retry after resolving a blocker. MCP does not automatically start an idle coding agent.

From the command line

trialone diagnose <recording> --json writes one recipe per finding with fixed fields, for an agent that works without MCP.