deep

deep is a command for Claude Code from Hayes-Zhang/deep-research. It costs 27 tokens per session (6,210 once invoked), scanned A, original, MIT.

A command for researching a topic from seven perspectives, including product strategy, user experience, technology, practice, academic work, history, and online discussion. It combines the findings into a web report, Markdown document, and Notion page.

In plain words
What is it for?
Use it for broad technology or product research, especially when you need comparisons, technical evidence, historical context, user feedback, and a combined report.
Why use it?
It gathers different kinds of evidence in one research process instead of leaving one person to investigate every angle alone.

Command for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: reads .claude/ paths; mentions subagents; names the AskUserQuestion tool.

Part of the deep-research plugin — 1 command, 8 agents shipped together

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.

agentmods
npx agentmods add commands/hayes-zhang/deep-research/deep
Clone the repo
git clone --depth 1 https://github.com/Hayes-Zhang/deep-research

Made for: Claude Code.

Or install deep-research, the plugin that ships this one along with the rest of its 1 command, 8 agents.

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 deep

README.md
[![agentmods](https://agentmods.dev/badge/commands/hayes-zhang/deep-research/deep.svg)](https://agentmods.dev/commands/hayes-zhang/deep-research/deep)
Your own site
<a href="https://agentmods.dev/commands/hayes-zhang/deep-research/deep"><img src="https://agentmods.dev/badge/commands/hayes-zhang/deep-research/deep.svg" alt="Measured on agentmods" height="20"></a>
Per session 27 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 6,210 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00027 $0.06210
Opus 5 $0.00014 $0.03105
Sonnet 5 $0.00005 $0.01242
Haiku 4.5 $0.00003 $0.00621

Measured 5d ago against content hash eea3e51dc015, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

deep scanned grade A with 0 findings 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 5d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/deep.md · 493 lines

How it starts

The opening of the file, as written. The whole thing — 493 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/deep — 多视角深度研究

⚠️ 执行模式:Agent Team(强制) 本命令必须使用 Agent Team 模式执行,禁止退化为 Subagent 模式。

  • ✅ 必须调用 TeamCreate 创建团队
  • ✅ 必须使用 Task 的 team_name + name 参数生成 teammate
  • ✅ teammate 之间必须通过 SendMessage 协作
  • ❌ 禁止使用纯 Subagent(即不带 team_name 的 Task 调用)
  • ❌ 禁止跳过 TeamCreate 直接启动 researcher

你将对以下主题进行 7 个维度的深度协作研究:

研究主题:$ARGUMENTS


阶段 1:理解需求

首先,分析用户提供的研究主题 $ARGUMENTS

  • 如果主题足够明确,直接进入阶段 2
  • 如果主题模糊或过于宽泛,使用 AskUserQuestion 工具追问:
    • 你具体想了解什么方面?
    • 你的研究目的是什么?(产品设计 / 技术选型 / 竞品分析 / 其他)
    • 有没有特别关注的角度?

将主题分解为适合 7 个视角分别研究的子问题。


阶段 2:创建团队 & 分配任务

2.1 创建研究团队

使用 TeamCreate 创建一个研究团队:

TeamCreate:
  team_name: "deep-research-{timestamp}"
  description: "多视角深度研究:[研究主题摘要]"

其中 {timestamp} 使用当前时间戳(如 20260206-143000),确保团队名称唯一。

验证:TeamCreate 调用成功后,确认团队名称已创建,再继续下一步。如果 TeamCreate 失败,停止执行并报告错误。

2.2 创建 8 个任务(7 研究任务 + 1 评审任务)

使用 TaskCreate 为 7 个研究视角和 1 个 critic 评审各创建一个任务:

# 任务名称 描述
1 产品设计与PM视角研究 从产品设计与PM角度研究 [主题]
2 交互设计视角研究 从交互设计角度研究 [主题]
3 技术方案视角研究 从技术方案角度研究 [主题]
4 行业最佳实践视角研究 从行业最佳实践角度研究 [主题]
5 学术研究视角研究 从学术研究角度研究 [主题]
6 历史科技产品视角研究 从历史科技产品角度研究 [主题]
7 社交媒体观察视角研究 从社交媒体观察角度研究 [主题]
8 对抗性同行评审 在 7 份 perspective 报告完成后,审查并产出 Issue Report

2.3 并行启动 8 个 teammate(7 researcher + 1 critic)

重要:必须在一条消息中并行发起所有 8 个 Task 调用(7 个 researcher + 1 个 critic),不要串行启动。critic 启动后会进入 idle 状态等待你在阶段 3.4 发起 review 请求——这样做能节省一次单独的 TaskCreate。

每个 Task 调用必须包含以下参数(缺一不可):

  • subagent_type: 插件 agent 名称(如 "deep-research:researcher-product"
  • team_name: 与 2.1 创建的团队名称完全一致
  • name: teammate 名称(如 "product"
  • prompt: 研究任务描述(见下方模板)
  • model: sonnet
  • mode: "bypassPermissions"(加速执行,减少权限弹窗)
  • description: 简短描述

⚠️ 禁止事项

  • 不得省略 team_name 参数(否则会退化为 Subagent,失去团队协作能力)
  • 不得省略 name 参数(否则 teammate 无法被识别和通信)
  • 不得串行启动(8 个 Task 调用必须在一条消息中并行发起)

critic 的 prompt 模板(与 7 个 researcher 的 prompt 不同——critic 不做研究,启动后等待 review 请求):

Task tool:
  subagent_type: "deep-research:critic-reviewer"
  team_name: "deep-research-{timestamp}"
  name: "critic"
  mode: "bypassPermissions"
  prompt: |
    ## 你的任务

    **团队名称**:deep-research-{timestamp}
    **你的角色**:critic-reviewer(对抗性同行评审)
    **用户原始研究主题**:[用户的研究主题]
    **用户问题的语言**:[zh 或 en,用于决定 Issue Report 的输出语言]

    你是质量门,不做原始研究。等 7 个 researcher 完成报告后,team lead 会通过 SendMessage 发起 review 请求,附上 7 份 perspective 报告的路径。

    **现在的工作**:
    1. 用 TaskGet 读取你的任务详情
    2. **解析 lead 的名字(强制)**:读取 ~/.claude/teams/{团队名称}/config.json,记住 lead 的 name 字段,后续 SendMessage 的 recipient 必须用它
    3. 用 TaskUpdate 将任务标记为 in_progress(让 lead 知道你在线)
    4. 进入 idle 等待状态,**不要主动开始任何 WebSearch / WebFetch**
    5. 收到 lead 的 review 请求后,按你的 system prompt 中的 Review Checklist 工作,产出 Issue Report
    6. 把 Issue Report 通过 SendMessage 发给 lead
    7. **立即** TaskUpdate 标记为 completed(不要等 shutdown_request)—— 否则 lead 会以为你卡死

    **预计被触发的时间**:7 个 researcher 完成后约 10-15 分钟。

  description: "Critic Review(质量门)"
  model: sonnet

Read the full file on GitHub · 493 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. 5d ago First seen · 493 lines · 27 tokens per session scan A eea3e51dc015

Subscribe to this mod's changes

deep is a command published in the GitHub repository Hayes-Zhang/deep-research (4 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 6,210 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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