An explicit tool-feedback loop
Study how a task continues from command and file results, then define the evidence your own workflow needs at each step.
Study Kimi Code CLI’s tool loop, focused subagents, lifecycle hooks, and editor protocol as references for your own workflows. Use them to clarify task roles, execution checks, and context handoffs around work in Floatboat.
Kimi Code CLI is Moonshot AI’s current open-source coding agent for the terminal. Its official description includes reading and editing code, running commands, searching files, and fetching web pages, with the agent choosing subsequent steps from tool feedback. It is an application around the model, not a Skill package.
For a Floatboat user improving a workflow, the useful ideas are role separation, explicit tool feedback, and well-defined points for checks. Kimi Code CLI provides a concrete reference for those mechanisms. ACP also illustrates how an interface can remain separate from the process that executes an agent task.
The maintained repository is MoonshotAI/kimi-code and uses MIT. The earlier Python project, MoonshotAI/kimi-cli, was archived and directs users to the newer CLI. Keep that distinction in mind when reading tutorials: the old project’s architecture, package names, and instructions are not interchangeable with the current release.
Understand what the project demonstrates, then decide which mechanism addresses a real gap in your own workflow.
Study how a task continues from command and file results, then define the evidence your own workflow needs at each step.
Use subagent contexts as a reference for dividing exploration, planning, and implementation without losing the parent task’s goal.
Study hooks and interface protocols when deciding where to validate an action and how context should pass between tools.
Use these scenarios as design references for work organized in Floatboat. Any software integration needs its own compatibility and permission checks.
Separate research, planning, and implementation outputs so each role contributes a result the next stage can inspect.
Design a validation or review point before a command, file change, or external action proceeds.
Use the interface–runtime separation as a reference when a workflow spans an editor, a workspace, and an execution process.
Start with a task and a small testable change, whether that means better instructions, a new tool connection, or a runtime component.
Use kimi-code documentation when studying the mechanisms; legacy Python CLI examples can describe a different architecture.
Specify what each delegated task receives, what it returns, and which operation must wait for review.
Check whether each stage gets enough relevant information without carrying unrelated conversation history into every step.
Compare independent agents through their interfaces, extension models, and approach to carrying work forward.