Manage long-running or parallel agent work around five-hour and weekly usage limits. Check consumption between batches, pause new work at 95 percent, preserve a clear handoff, and check actual usage again before resuming.
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Use when long-running or parallel agent work must respect 5-hour and weekly usage limits by checking usage between waves, pausing near the cap, and resuming only when the window is clear.
安装前可以查看交给 AI 代理的指令内容。
Keep long-running agent work inside the current 5-hour and weekly usage windows. Check usage before launching substantial work and between waves of parallel subagents. If an active 5-hour or weekly limit is at or above 95%, pause new work until the window is clear enough to continue safely.
Prefer a first-party host usage tool when available. In Claude Code, use:
npx -y ccusage@latest blocks --active --json
Use the JSON to identify the active block start, current cost or percentage, and time remaining. On wake, compare the active block start timestamp with the previous one; a new timestamp is stronger evidence than "enough time passed."
If the tool reports cost instead of a direct percentage, convert through the current account limit when known. For Claude Max-style 5-hour blocks, some users prefer an earlier caution threshold around $500-550; treat that as a user-configured guardrail, not a universal rule. The default stop rule is still 95% of the active 5-hour or weekly limit.
When a wake/resume tool is available, schedule a wakeup for:
min(3600, secondsUntilWindowClears)
If the runtime clamps wake delays to 60-3600 seconds, chain wakeups for longer waits. Each wakeup should re-check usage, reschedule if still over budget, and continue only when the window is safely below the threshold.
Make wake prompts self-contained. Include:
Avoid short-interval polling for things the host will notify you about, such as background task or subagent completion. For budget pauses, a prompt-cache miss after a long sleep is acceptable; preserving the limit matters more.
If you pause, tell the user which window is over threshold, the observed usage, when you scheduled or expect the next check, and what work remains. Keep enough state in the wake prompt that the next turn can resume without relying on conversation momentum.
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Guide public skill additions and updates through file placement, catalog documentation, installation discovery, integration settings, and validation. Distinguish ordinary skills from instruction-based, app-backed, and generated skills so each receives the appropriate checks.
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Monitor another AI agent’s work and independently investigate the same task. Compare the original request with actual changes and test evidence, report gaps and concrete follow-up steps, and make limited fixes when authorized.
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Open Agent-Native apps beside the conversation through Dispatch MCP, a connection for app tools. Create or edit content using the current screen and selection, then read saved results back to confirm the changes.
中文简介正在准备中。当前显示英语简介。
Organize large coding tasks with Claude Fable directing lower-cost subagents. Delegate bounded research, edits, and tests, then have Fable verify evidence, resolve conflicting findings, integrate changes, and review the final result.
中文简介正在准备中。当前显示英语简介。
Organize work so an expensive AI model handles planning, judgment, and final review while cheaper helper agents perform bounded research, coding, and testing. Define ownership and verification requirements, then inspect the evidence and integrate their results centrally.
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Configure a software delivery workflow that turns selected feedback and maintenance signals into verified, reviewed changes. Define information sources, schedules, working directories, and separate permissions for actions such as implementation, merging, deployment, and notifications.
中文简介正在准备中。当前显示英语简介。