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multi-agent-deep-research多智能体深度研究

Agent Skill

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。

总安装

4,368

周安装

182

GitHub Stars

公开资料未说明

下载量

1,456
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

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OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

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需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

92.52%
按下载量换算1,347

安全审计

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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

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