research-multi-agent

research-multi-agent is a skill for Claude Code from shirleyharleywiley/multi-agent-deepresearch. It costs 72 tokens per session (14,092 once invoked), scanned A, original, MIT.

A multi-role, repeated research method that divides a topic among specialised research roles and combines their findings into a structured report. It covers topics such as competitors, technology choices, markets, companies, people, policies, supply chains, and arguments.

In plain words
What is it for?
Use it to plan research, run parallel investigations, combine and check evidence, identify gaps, and produce versions for audiences such as CEOs or CTOs.
Why use it?
It helps expose missing information, conflicting findings, weak sources, and unmet constraints during research instead of relying on one pass.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: agent in frontmatter; names the TodoWrite tool.

Good fit Use it to plan research, run parallel investigations, combine and check evidence, identify gaps, and produce versions for audiences such as CEOs or CTOs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/shirleyharleywiley/multi-agent-deepresearch/research-multi-agent
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add shirleyharleywiley/multi-agent-deepresearch --skill research-multi-agent
Clone the repo
git clone --depth 1 https://github.com/shirleyharleywiley/multi-agent-deepresearch

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for research-multi-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/shirleyharleywiley/multi-agent-deepresearch/research-multi-agent/github.svg)](https://agentmods.dev/skills/shirleyharleywiley/multi-agent-deepresearch/research-multi-agent)
Your own site
<a href="https://agentmods.dev/skills/shirleyharleywiley/multi-agent-deepresearch/research-multi-agent"><img src="https://agentmods.dev/badge/skills/shirleyharleywiley/multi-agent-deepresearch/research-multi-agent/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for research-multi-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/shirleyharleywiley/multi-agent-deepresearch/research-multi-agent"><img src="https://agentmods.dev/badge/skills/shirleyharleywiley/multi-agent-deepresearch/research-multi-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 14,092 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00072 $0.14092
Opus 5 $0.00036 $0.07046
Sonnet 5 $0.00014 $0.02818
Haiku 4.5 $0.00007 $0.01409

Measured 10d ago against content hash 00d34aad7200, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

research-multi-agent scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

Phase A → curl 爬取(工具前置,P0,必须先于 agents 执行)
skills/research-multi-agent/SKILL.md · 1,113 lines

How it starts

The opening of the file, as written. The whole thing — 1,113 lines — stays where its author put it; the contents beside it link to each section on GitHub.

多角色多轮迭代深度调研方法论

六角色三层架构 × 多轮迭代 × 工具前置


Role 0:调研类型选择

调研启动时先确定调研类型,不同类型对应不同的信息源优先级、终止条件和输出格式。

类型 核心问题 侧重点 终止条件差异
竞品分析 X vs Y 谁更好?为什么? 差异点、优劣势、时机 核心差异是否已覆盖
技术选型 选 A 还是 B? 约束、性能、社区、成本 技术风险是否已评估
市场研究 市场规模和趋势? 规模、增速、用户画像 数据是否可交叉验证
人物研究 这人是谁?为什么成功? 背景、动机、关系网 关键事件链是否完整
政策研究 政策如何影响行业? 监管要点、合规风险、豁免条件 政策原文是否已覆盖
供应链研究 产业链上下游关系? 关键环节、议价能力、替代风险 核心供应商是否已识别
观点论证 验证某个观点是否成立? 论据质量、反驳角度、来源可信度 核心论据是否已覆盖正反两面
企业研究 这个企业是如何运作的? 商业模式、组织架构、核心能力 核心机制是否已清晰描述

执行方式:当用户提供调研主题时,主动询问调研类型。如果用户未指定,根据主题特征自动推断并告知用户。


方法论核心架构

输入:调研主题 + 约束条件(如"面向CEO"、"只讲X自身")
  ↓
【快速洞察层 QuickInsight】
  ↓
Phase A → curl 爬取(工具前置,P0,必须先于 agents 执行)
  ↓
Phase B → Role 1(并行探针):将主题拆解为 N 个子方向,错峰并行搜索
           输入 ← 主题 + 约束条件
           输出 → 原始发现列表(每个 agent 一个子方向的结果)
  ↓
Phase C → Role 4(信息综合):实时合并新发现,识别已确认/有冲突/未知
           输入 ← Role 1 的原始发现列表
           输出 → 结构化合并发现(含:已确认N条、有冲突N条、未知N条)
  ↓
Phase D → Role 6(边界守卫):检查约束条件是否满足
           输入 ← 合并发现 + 约束条件
           输出 → 约束合规清单(每条约束:✓合规 / ⚠越界→已纠正)
  ↓
【元认知层 MetaInsight】
  ↓
Phase E → Role 2(差距分析):识别信息差距,指导下一轮搜索
           输入 ← Role 4 合并结果(重点:未知条目)
           输出 → 优先缺口列表(每条:缺口名称 — 预期来源 — 边际收益)
  ↓
Phase F → Role 3(知识整合):将多轮发现整合为完整叙事
           输入 ← Role 4 合并结果 + Role 6 合规清单
           输出 → 调研报告草稿(含:发现 + 缺口说明 + 参考来源)
  ↓
【判断层 XInsight】
  ↓
Phase G → Role 5(终止判断):综合所有前置输出,决定继续或终止
           输入 ← Role 2 差距分析 + Role 3 草稿 + Role 6 合规清单
           输出 → 终止/继续判定 + 下一步优先缺口

           终止条件(必须全部满足):
             ① 核心问题已解决
             ② 高价值发现最低可信度 ≥ 中高
             ③ Role 6 约束合规 ✓
             ④ 剩余缺口属于结构性限制
           → 全部满足 → 终止迭代,进入 Phase H
           → 任一未满足 → 进入下一轮迭代

  ↓
【输出层 OutputInsight】
  ↓
Phase H → Role 7(多读者差异化输出):面向不同读者生成差异化报告版本
           输入 ← 最终报告草稿 + 目标读者列表 + 原始约束条件
           输出 → 面向各读者的独立报告版本(CEO版/CTO版/运营版等)
           执行时机 → Phase G 终止判定后(仅执行一次)
           → 多读者场景:并行产出差异化版本
           → 单读者场景:直接输出适配版本

  ↓
输出:完整调研报告 + 信息缺口说明 + 参考来源 + 多读者版本(可选)

Read the full file on GitHub · 1,113 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 1,113 lines · 72 tokens per session scan A 00d34aad7200

Subscribe to this mod's changes

research-multi-agent is a skill published in the GitHub repository shirleyharleywiley/multi-agent-deepresearch (11 stars, last pushed 4mo ago), licensed MIT. It adds 72 tokens to every session and 14,092 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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