メニュー
Use Manus for async tasks that exceed local tools: broad research, deliverables such as PDF/PPT/CSV, connector-based work, or long multi-step jobs. Create a task, poll or receive webhooks, then fetch results with the bundled script.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | manus |
| description | Use Manus for async tasks that exceed local tools: broad research, deliverables such as PDF/PPT/CSV, connector-based work, or long multi-step jobs. Create a task, poll or receive webhooks, then fetch results with the bundled script. |
Prefer Manus only when local tools or sub-agents are not enough.
Docs: https://open.manus.im/docs
manus-1.6; use -lite for cheaper exploratory work--task-idSCRIPT="<SKILL_DIR>/scripts/manus_client.mjs"
Create:
node "$SCRIPT" create \
--prompt "Your prompt" \
--mode agent \
--profile manus-1.6
Optional:
--attachment /path/to/file repeatable--connector <uuid> repeatable--task-id <id>--label <text>--locale zh-CN--interactiveStatus:
node "$SCRIPT" status --task-id <task_id>
Result:
node "$SCRIPT" result --task-id <task_id>
List:
node "$SCRIPT" list --limit 10 --status completed
Delete:
node "$SCRIPT" delete --task-id <task_id>
If Manus asks a follow-up question:
node "$SCRIPT" create \
--task-id <original_task_id> \
--prompt "User reply"
References:
<SKILL_DIR>/references/api.md<SKILL_DIR>/references/setup.md<SKILL_DIR>/scripts/webhook-transform.mjs<SKILL_DIR>/scripts/webhook_transform.py<SKILL_DIR>/scripts/manus_client.py飞书云文档（Docx / Wiki 文档）：读取和编辑飞书文档内容。当用户给出文档 URL 或 token，或需要查看、创建、编辑文档、插入或下载文档图片附件时使用。文档中嵌入的电子表格、多维表格、画板，先用本 skill 提取 token 再切到对应 skill。当用户给出 doubao.com 的 /docx/ 或 /wiki/ URL/token 时，也应直接使用本 skill；路由依据是 URL 路径模式和 token，而不是域名。不负责文档评论管理，也不负责表格或 Base 的数据操作。当用户明确要操作飞书思维笔记时，也使用本 skill。
飞书画板：查询和编辑飞书云文档中的画板。支持导出画板为预览图片、导出原始节点结构、使用多种格式更新画板内容。 当用户需要查看画板内容、导出画板图片、编辑画板时使用此 skill。不负责：飞书云文档内容编辑（lark-doc）、文档内嵌电子表格/Base（lark-sheets / lark-base）。
会议纪要整理工作流：汇总指定时间范围内的会议纪要并生成结构化报告。当用户需要整理会议纪要、生成会议周报、回顾一段时间内的会议内容时使用。
End-to-end driver that takes a ready-for-review pull request all the way to a clean merge. Use whenever the user wants Claude to shepherd, babysit, watch, or drive a PR to green and merge it — e.g. "take PR #123 through CI and merge it", "babysit this PR until everything is green", "land this PR and clean up". It loops over the PR's checks and reviews: fixes real CI failures (re-running only genuine flakes), replies to every review comment, resolves each conversation, and re-polls until a strict gate passes — all checks actually completed and passing, every thread resolved or outdated, mergeable with no required approval outstanding — not merely "looks fixed". Then it merges with the repo's preferred method, deletes the remote branch unless it is long-lived, removes the local branch and git worktree, and fast-forwards local main/master. Trigger even when the user only describes the outcome ("get this merged once CI is happy") without naming the steps.
Structured pedagogical deep-dive for new substantive topics across STEM, technical/engineering practice, and humanities/business/history. Use this skill whenever the user asks to learn, understand, deep-dive, explore, or be taught a new concept they don't already have a working model of — including phrasings like "what is X", "how does Y work", "why does Z happen", "explain W", "带我入门 X", "讲讲 X", "教我 X", "我想搞懂 X", even when they don't use the word "teach" or "learn". Trigger especially when the topic is a named concept, framework, theorem, mechanism, technology, historical event, or intellectual movement. Do NOT trigger for quick factual lookups ("when did X happen"), tool/syntax questions ("how do I curl with auth"), conversational chitchat, or mid-flow technical discussion where the user is already engaged on the topic.
Apply whenever you are about to produce written output that an INTERPRETING READER — a human, or another AI agent that will act on your words — will read and act on: emails, IM/Slack, PR or issue descriptions, review comments, design or decision docs, customer-facing copy, AND natural-language task specs, prompts, or agent mandates whose executor is a person or an LLM. Use it even when the user did not ask for "communication" and the message is only a sub-step of a larger task (e.g. the PR body after a feature, or a prompt you are writing for a downstream agent) — those incidental cases are where audience-blind writing slips through, so reach for this proactively. It makes you model the reader's actual capability and what they will infer or assume, then emit only the calibrated artifact. Do NOT use for output consumed literally by a deterministic interpreter — executed code, configs, schemas, queries, regexes, test fixtures — where correctness is binary and there is no mind to model.