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name: deep-research
description: Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report.
when_to_use: When the user wants a deep, multi-source, fact-checked research report on any topic. BEFORE invoking, check if the question is specific enough to research directly — if underspecified (e.g., "what car to buy" without budget/use-case/region), ask 2-3 clarifying questions to narrow scope. Then pass the refined question as args, weaving the answers in.
disable-model-invocation: true

Run the "deep-research" workflow.

运行 "deep-research" 工作流。

Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report.

深度研究调度器——扇出多路网络搜索、抓取来源、以对抗方式核查论断、综合出一份带引用的报告。

When the user wants a deep, multi-source, fact-checked research report on any topic. BEFORE invoking, check if the question is specific enough to research directly — if underspecified (e.g., "what car to buy" without budget/use-case/region), ask 2-3 clarifying questions to narrow scope. Then pass the refined question as args, weaving the answers in.

当用户想要就任何主题获得一份深入、多来源、经事实核查的研究报告时使用。调用之前,先检查问题是否足够具体、可以直接开展研究——如果不够明确(例如没有预算/用途/地区限制的"买什么车好"),先提出 2-3 个澄清问题缩小范围,再把精炼后的问题作为 args 传入,并将澄清得到的答案融入其中。

Phases:

阶段:

Invoke: Workflow({ name: "deep-research" })

调用方式:Workflow({ name: "deep-research" })

If the user asks you to modify this workflow or write a new script, load the workflow-authoring skill first.

如果用户要求修改此工作流或编写新脚本,请先加载 workflow-authoring 技能。

【评论】"每条论断需 2/3 票反驳才被否决"的对抗式核查是该工作流的显著设计:宁可保留存疑论断也不轻易删除,以降低单一搜索代理出错对结论的影响。