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Agent Skill · 研究检索

multi-agent-deep-research(多智能体深度研究)

multi-agent-deep-research 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

multi-agent-deep-research 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 23,090。

总安装 4,368周安装 182GitHub Stars 公开资料未说明下载 1,456核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:multi-agent-deep-research(多智能体深度研究)
来源仓库:https://github.com/zzxx-bit/multi-agent-deep-research
安装命令:
openclaw skills install multi-agent-deep-research
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install multi-agent-deep-research

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

协调深度研究项目,利用本地工件跟踪与显式证据分类账提升研究可靠性。适用于学术调研、市场分析等需引用原始来源的任务。支持 AgentSkills 兼容文件夹结构,便于成果归档与复用。

安装命令为 openclaw skills install multi-agent-deep-research,需预留足够磁盘空间存放中间文件。使用前应确认数据来源合法性,避免抓取受版权保护内容。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install multi-agent-deep-research;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

multi-agent-deep-research 怎么安装?

在终端执行 openclaw skills install multi-agent-deep-research。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

multi-agent-deep-research 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

multi-agent-deep-research 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

multi-agent-deep-research 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

multi-agent-deep-research 用的人多吗?

公开数据是累计安装 4,368 次,近一周安装 182 次,下载 1,456 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

multi-agent-deep-research 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
multi-agent-deep-research

多智能体深度研究 · 本页

4,368—只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
openclaw-deep-research
description
Coordinate deep, source-verifiable research projects in OpenClaw using AgentSkills-compatible folders, local artifact tracking, and explicit evidence ledgers. Use when the user wants a high-quality report or investigation that spans many sources, needs auditable claims, benefits from delegation, or must manage context-window limits by writing project state to local files during an OpenClaw workflow.

OpenClaw Deep Research

Run research as a file-backed production workflow, not as a chat-only exercise. Optimize for source traceability, narrow ownership, compact handoffs, and a final report that distinguishes facts, interpretations, comparisons, and forecasts.

Platform notes

  • Install this skill under ~/.openclaw/skills/openclaw-deep-research/ for user-wide use or skills/openclaw-deep-research/ inside a workspace when using OpenClaw local skills.
  • Keep the actual research directory under the active workspace so report artifacts remain close to the task.
  • Assume delegation is optional. Use subagents only if the current OpenClaw setup exposes them and the user explicitly wants parallel work.

Quick start

  1. Freeze the topic, audience, report shape, and research cutoff date before collecting sources.
  2. Create one canonical project root in the current workspace and treat it as the single source of truth.
  3. Persist project memory immediately: status, task board, source ledger, claim ledger, fact-check log, handoffs, and draft report.
  4. If the user explicitly wants multi-agent work, split the project into bounded roles with disjoint write scopes. Otherwise use the same workflow locally without delegation.
  5. Collect sources first, write claims second, draft only from verified claims, and end with a QA pass that checks dates, comparability, and unsupported conclusions.

Non-negotiables

  • Keep exactly one canonical project directory.
  • Write project memory to local files; do not rely on chat history as durable state.
  • Prefer primary sources for unstable or high-stakes facts.
  • Give every substantive claim a claim_id and at least one source_id.
  • Put absolute dates on fast-moving facts and on the report cutoff.
  • State the comparison metric and time window whenever comparing countries, companies, models, markets, or policies.
  • Separate hard_fact, reported_fact, interpretation, comparison, and forecast.
  • Do not let multiple agents edit the same files unless the user explicitly wants that tradeoff.

Workflow

1. Freeze scope

  • Clarify the research question, target reader, deliverable format, and report cutoff date.
  • Prefer dimensional comparison over a single headline ranking when the topic is structurally uneven.
  • Record the scope and stop conditions before searching.

For a starter layout, load references/project-layout.md.

2. Create the file-backed workspace

  • Create the project root and a minimal scaffold for workflow, sources, claims, checks, handoffs, and deliverables.
  • Record current phase, next actions, and open questions in a short status file.
  • Update these files every round so a later agent can resume without replaying chat.

For copy-paste starter files, load references/templates.md.

3. Decide the delegation pattern

  • Only use OpenClaw subagents when the user explicitly asks for multi-agent or delegation work and the current setup supports them.
  • Use one agent per bounded responsibility or per independent research slice.
  • Assign each agent one objective, one read set, one write scope, one cutoff date, and one handoff target.
  • Keep the PM/orchestrator role local whenever possible so synthesis and quality control stay centralized.

For role options and handoff contracts, load references/delegation-patterns.md.

4. Collect sources

  • Build the source ledger before building the narrative.
  • Prioritize primary sources: official documents, filings, company docs, papers, model cards, release notes, government pages, and original datasets.
  • Use high-quality secondary sources for synthesis and triangulation when primary material is incomplete.
  • Record enough metadata that another reviewer can reopen the source later.

For evidence rules and claim classes, load references/evidence-standards.md.

5. Build claims

  • Convert source notes into atomic claims.
  • Attach source_id, claim class, confidence, date range, and comparability notes.
  • Mark unsupported or unresolved claims as draft, blocked, contested, or equivalent; do not quietly promote them into the report.

6. Normalize comparisons

  • Build a comparison matrix when the report compares two or more entities.
  • Explicitly mark rows as comparable, partial, or not_comparable.
  • If two sources use different units, populations, definitions, or time windows, say so instead of forcing a clean ranking.

7. Draft the report

  • Put the research cutoff date near the top.
  • Draft from verified claims only.
  • Keep the main text readable, but make the evidence chain auditable through source and claim ledgers.
  • Include a methods section, limits section, and open questions or future watchpoints section.

8. QA the report

  • Recheck every numerical claim, dated statement, and leadership claim.
  • Downgrade or remove statements that are true only under narrow assumptions.
  • Ensure the final report does not blur facts and inference.
  • Confirm that every citation in the report resolves back to the local ledgers.

Delegation rules

  • Keep agent ownership disjoint by file path or by work package.
  • Require every handoff to include: done, verified, open, next, and changed files.
  • Do not ask scouts to write synthesis if their job is evidence collection.
  • Do not ask drafters to invent facts or fill gaps from memory.
  • Route conflicts back to the source layer, not to a rhetorical compromise.

Writing rules

  • Prefer direct, dated language: "As of 2026-03-21..." over relative timing.
  • Use cautious wording for vendor claims, self-reported performance, and fast-moving policy changes.
  • Avoid totalizing conclusions like "X is winning overall" unless the evidence really supports that scope.
  • End with what is still uncertain and what could change the conclusion.

Reference map

  • references/project-layout.md -> canonical directory structure and when to create each artifact.
  • references/delegation-patterns.md -> role menu, handoff contract, and anti-duplication rules.
  • references/evidence-standards.md -> source hierarchy, claim classes, and QA gates.
  • references/templates.md -> compact starter templates for ledgers, status files, and handoffs.
  • references/platform-notes.md -> OpenClaw specific installation and usage notes.

平台分布

OpenClaw

92.52%
按下载量换算1,347

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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