deepthink

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Chinese

/deepthink — Deep Research & Analysis Pipeline

/deepthink — 深度研究与分析流程

You are a research orchestrator running an iterative intelligence pipeline. Your job: take a raw idea, conduct deep market research, analyze it through multiple frameworks, stress-test the analysis, research the weaknesses found, re-analyze, and iterate until the analysis stabilizes — then produce a comprehensive HTML report.
The output feels like a week of analyst work compressed into one session: deep market data, multi-framework intelligence, adversarial stress testing, and a polished presentation-ready report.

你是一名研究协调者,负责运行迭代式智能分析流程。你的任务是:接收原始想法,开展深度市场研究,通过多个框架进行分析,对分析结果进行压力测试,研究发现的弱点,重新分析,反复迭代直到分析结果趋于稳定——然后生成一份综合性HTML报告。
最终输出相当于将分析师一周的工作压缩到一次会话中:深度市场数据、多框架智能分析、对抗性压力测试,以及一份可直接用于展示的精美报告。

Pipeline Overview

流程概述

IDEA → RESEARCH → THINK → RED-TEAM → RESEARCH gaps → RE-THINK → RE-RED-TEAM → ... → REPORT
                                                                    (iterate until convergence)
Convergence criteria: The pipeline stops iterating when:
  1. Red team verdict is SURVIVES or stable WOUNDED (same verdict two rounds in a row)
  2. Failure probability deltas between rounds are < 5% on all major failure modes
  3. No new KILL SHOTS found in the latest red team round
  4. Maximum 3 iterations (to bound cost)

想法 → 研究 → 分析(/think) → 红队测试(/red-team) → 研究漏洞 → 重新分析 → 再次红队测试 → ... → 生成报告
                                                                    (迭代至结果收敛)
收敛标准: 满足以下条件时,流程停止迭代:
  1. 红队测试 verdict 为 SURVIVES(通过)或连续两轮稳定为 WOUNDED(有瑕疵)
  2. 各主要失败模式的失败概率变化幅度在两轮间小于5%
  3. 最新一轮红队测试未发现新的 KILL SHOTS(致命漏洞)
  4. 最多迭代3次(控制成本)

Invocation

调用方式

When invoked with
$ARGUMENTS
:
  1. If
    $ARGUMENTS
    contains a clear idea, market, or question → proceed to Phase 1
  2. If
    $ARGUMENTS
    is empty or too vague, ask ONE question via AskUserQuestion: "Describe the idea or market you want to research in 2-3 sentences. What are you trying to build, enter, or understand?"
  3. Do NOT ask more than one round of questions. Work with what you have.

当通过
$ARGUMENTS
调用时:
  1. 如果
    $ARGUMENTS
    包含明确的想法、市场或问题 → 进入第一阶段
  2. 如果
    $ARGUMENTS
    为空或过于模糊,通过AskUserQuestion提出一个问题: "请用2-3句话描述你想要研究的想法或市场。你想要打造什么、进入哪个领域,或是想要理解什么?"
  3. 最多只提问一轮,基于现有信息开展工作。

Phase 1 — Deep Market Research

第一阶段 — 深度市场研究

Goal

目标

Build a comprehensive understanding of the competitive landscape, market dynamics, existing players, funding patterns, and adoption signals before any analytical frameworks run.
在运行任何分析框架之前,全面了解竞争格局、市场动态、现有参与者、融资模式以及市场 adoption signals( adoption信号)。

Step 1.1 — Scope the Research

步骤1.1 — 确定研究范围

Based on the idea, identify 4-6 research vectors:
  • Direct competitors: Who is building this or something adjacent?
  • Market size: TAM/SAM/SOM estimates from credible sources
  • Funding landscape: Who has raised money in this space? How much? When?
  • Adoption signals: What evidence exists that the market is real (or not)?
  • Regulatory/structural: Any barriers, tailwinds, or structural dynamics?
  • Technology readiness: Is the underlying tech mature enough?
Present the research plan briefly:
undefined
基于目标想法,确定4-6个研究方向:
  • 直接竞争对手: 谁正在开发同类或相关产品?
  • 市场规模: 来自可信来源的TAM/SAM/SOM估算
  • 融资格局: 该领域哪些主体获得了融资?金额多少?时间?
  • Adoption信号: 有哪些证据表明市场真实存在(或不存在)?
  • 监管/结构因素: 存在哪些壁垒、有利条件或结构性动态?
  • 技术成熟度: 底层技术是否足够成熟?
简要呈现研究计划:
undefined

Deep Research: [Idea Title — 3-5 words]

深度研究:[想法标题 — 3-5个词]

Idea: [1-2 sentence restatement]
Research vectors:
  1. [vector] — [what we need to find]
  2. [vector] — [what we need to find] ...
Starting deep research across [N] vectors...
undefined
想法: [1-2句话重述]
研究方向:
  1. [方向] — [需要调研的内容]
  2. [方向] — [需要调研的内容] ...
开始针对[N]个方向开展深度研究...
undefined

Step 1.2 — Execute Research

步骤1.2 — 执行研究

Spawn research agents in parallel using
Agent
tool with
run_in_background: true
. Each agent gets one research vector and uses
WebSearch
and
WebFetch
to gather data.
Agent prompt template:
You are a market research analyst. Your job is to research ONE specific vector
thoroughly using web searches.

IDEA: [the idea]
YOUR RESEARCH VECTOR: [specific vector]
WHAT TO FIND: [specific questions to answer]

INSTRUCTIONS:
- Run 3-5 web searches with different query formulations
- Look for: specific companies, funding amounts, market size estimates, adoption
  data, technology maturity signals, regulatory developments
- Prefer recent data (2024-2026)
- Include specific numbers, names, and sources
- If you find conflicting data, note both claims with sources

OUTPUT FORMAT:
Return a structured research brief:
使用
Agent
工具并行启动研究代理,设置
run_in_background: true
。每个代理负责一个研究方向,使用
WebSearch
WebFetch
收集数据。
代理提示模板:
你是一名市场研究分析师。你的任务是通过网络搜索,全面研究一个特定方向。

想法:[目标想法]
你的研究方向:[特定方向]
需要调研的内容:[具体问题]

说明:
- 使用不同的查询方式进行3-5次网络搜索
- 重点查找:特定公司、融资金额、市场规模估算、adoption数据、技术成熟度信号、监管动态
- 优先选择近期数据(2024-2026年)
- 包含具体数字、名称和来源
- 如果发现相互矛盾的数据,请注明两种观点及来源

输出格式:
返回结构化研究简报:

[Vector Title]

[方向标题]

Key Findings

关键发现

  • [Finding 1 — with source/date]
  • [Finding 2 — with source/date] ...
  • [发现1 — 附来源/日期]
  • [发现2 — 附来源/日期] ...

Companies/Players

企业/参与者

CompanyWhat they doFundingStageThreat Level
公司业务内容融资情况发展阶段威胁等级

Market Data

市场数据

  • [Specific numbers with sources]
  • [带来源的具体数字]

Signals

信号

  • [Bullish signal — evidence]
  • [Bearish signal — evidence]
  • [利好信号 — 证据]
  • [利空信号 — 证据]

Gaps in Knowledge

知识缺口

  • [What we couldn't find or verify]

Use `model: "sonnet"` for research agents. Name them: `research-competitors`,
`research-market-size`, `research-funding`, etc.
  • [无法找到或验证的内容]

为研究代理使用 `model: "sonnet"`,命名为:`research-competitors`、`research-market-size`、`research-funding` 等。

Step 1.3 — Compile Research Document

步骤1.3 — 整理研究文档

After all research agents return, compile findings into a single research document. Write to
thoughts/deepthink/YYYY-MM-DD-<slug>-research.md
:
markdown
---
date: <ISO 8601>
idea: "<idea title>"
research_vectors: [list]
key_players: [list of top 5-10 companies]
estimated_tam: "<number>"
---
所有研究代理返回结果后,将发现整理为单个研究文档,写入
thoughts/deepthink/YYYY-MM-DD-<slug>-research.md
markdown
---
date: <ISO 8601格式>
idea: "<想法标题>"
research_vectors: [列表]
key_players: [前5-10名公司列表]
estimated_tam: "<数值>"
---

Market Research: [Idea Title]

市场研究:[想法标题]

Executive Summary

执行摘要

[3-5 sentences synthesizing the research — what the landscape looks like, where the opportunity is, and what the biggest unknowns are]
[3-5句话总结研究内容 — 市场格局如何、机会在哪里、最大的未知因素是什么]

Competitive Landscape

竞争格局

[Compiled from competitor research — table of players, what they do, funding, stage]
[整合竞争对手研究内容 — 参与者表格,包含业务内容、融资情况、发展阶段]

Market Size

市场规模

[TAM/SAM/SOM with sources and methodology notes]
[带来源和方法说明的TAM/SAM/SOM数据]

Funding & Investment Signals

融资与投资信号

[Who's investing, how much, what stage, what it implies]
[谁在投资、金额多少、处于什么阶段、意味着什么]

Adoption & Demand Signals

Adoption与需求信号

[Evidence the market is real — or not]
[市场真实存在(或不存在)的证据]

Technology Readiness

技术成熟度

[Is the underlying tech mature enough? What's the gap?]
[底层技术是否足够成熟?差距在哪里?]

Regulatory & Structural Dynamics

监管与结构性动态

[Barriers, tailwinds, structural factors]
[壁垒、有利条件、结构性因素]

Key Unknowns

关键未知因素

[What we couldn't find — these become research targets for later rounds]

---
[无法找到的内容 — 这些将成为后续轮次的研究目标]

---

Phase 2 — First Intelligence Brief (/think)

第二阶段 — 首次情报简报(/think)

Run the /think skill on the idea, providing the research as context.
针对目标想法运行/think技能,将研究内容作为上下文提供。

Execution

执行方式

Use the
Skill
tool to invoke
/think
with the following argument structure:
Actually — do NOT invoke /think via the Skill tool. Instead, execute the /think workflow directly inline, following the full /think SKILL.md protocol:
  1. Read the think skill from
    .claude/skills/think/SKILL.md
  2. Triage: Select 4-7 frameworks. Include the research document as context for every sub-analyst agent.
  3. Spawn agents per the /think protocol (run_in_background, model: sonnet)
  4. Collect results, surface contradictions, synthesize
  5. Write the think brief to
    thoughts/deepthink/YYYY-MM-DD-<slug>-think-r1.md
    (note: r1 = round 1)
The key difference from a standalone /think: every sub-analyst agent receives the full research document as additional context, so their analysis is grounded in real market data rather than general knowledge.
Agent prompt addition (prepend to each analyst's prompt):
MARKET RESEARCH CONTEXT:
[Include the full compiled research document from Phase 1]

Use this research as ground truth. Reference specific companies, numbers, and
findings in your analysis. Do not make claims that contradict the research unless
you have a specific reason to disagree (and state that reason).

不要通过Skill工具调用/think,而是直接内联执行/think工作流,遵循完整的/think SKILL.md协议:
  1. 读取
    .claude/skills/think/SKILL.md
    中的think技能
  2. 筛选框架: 选择4-7个框架。为每个子分析师代理提供研究文档作为上下文。
  3. 启动代理 遵循/think协议(run_in_background,model: sonnet)
  4. 收集结果,梳理矛盾点,进行整合
  5. 写入 分析简报至
    thoughts/deepthink/YYYY-MM-DD-<slug>-think-r1.md
    (注:r1 = 第一轮)
与独立运行/think的关键区别:每个子分析师代理都会收到完整的研究文档作为额外上下文,因此他们的分析基于真实市场数据,而非通用知识。
代理提示补充内容(添加到每个分析师的提示开头):
市场研究上下文:
[包含第一阶段整理的完整研究文档]

将此研究作为事实依据。在分析中引用具体公司、数字和研究发现。除非有特定理由不同意研究结果(并说明理由),否则不要提出与研究矛盾的观点。

Phase 3 — First Red Team (/red-team)

第三阶段 — 首次红队测试(/red-team)

Run the /red-team protocol on the Phase 2 think brief.
针对第二阶段的分析简报运行/red-team协议。

Execution

执行方式

Execute the /red-team workflow directly inline, following the full /red-team SKILL.md protocol:
  1. Read the red-team skill from
    .claude/skills/red-team/SKILL.md
  2. Extract the target from the Phase 2 think brief
  3. Select 5-7 prosecutors, always including Feynman, Kahneman, Tetlock, Munger
  4. Spawn prosecutor agents with the full think brief AND research document
  5. Compile kill sheet with ratings
  6. Write to
    thoughts/deepthink/YYYY-MM-DD-<slug>-redteam-r1.md
Record the verdict, kill shots, wounds, and revised failure probabilities.

直接内联执行/red-team工作流,遵循完整的/red-team SKILL.md协议:
  1. 读取
    .claude/skills/red-team/SKILL.md
    中的red-team技能
  2. 提取目标 从第二阶段的分析简报中提取分析目标
  3. 选择5-7名检察官,必须包含Feynman、Kahneman、Tetlock、Munger
  4. 启动检察官代理,提供完整的分析简报和研究文档
  5. 整理 包含评级的漏洞清单
  6. 写入
    thoughts/deepthink/YYYY-MM-DD-<slug>-redteam-r1.md
记录verdict、kill shots、wounds以及修正后的失败概率。

Phase 4 — Gap Research

第四阶段 — 漏洞研究

The red team found weaknesses. Some are speculative, some may have real evidence behind them. Research the gaps.
红队测试发现了弱点。有些是推测性的,有些可能有实际证据支撑。针对这些漏洞开展研究。

Step 4.1 — Identify Research Gaps

步骤4.1 — 识别研究漏洞

From the red team report, extract:
  • Kill shots that hinge on unverified claims (can we verify or refute them?)
  • Wounds that reference competitors or market dynamics we haven't researched
  • Historical analogues the red team cited (are they accurate?)
  • Base rates the red team claimed (can we find the actual data?)
从红队测试报告中提取:
  • 基于未验证主张的kill shots(能否验证或反驳?)
  • 涉及未研究的竞争对手或市场动态的wounds
  • 红队测试引用的历史类比(是否准确?)
  • 红队测试声称的基准率(能否找到实际数据?)

Step 4.2 — Targeted Research

步骤4.2 — 针对性研究

Spawn 2-4 research agents focused specifically on the gaps the red team identified. These are targeted, not broad — each agent investigates one specific claim or gap.
Agent prompt template:
You are a fact-checker and research analyst. The red team made a specific claim
that needs verification.

THE CLAIM: [specific red team attack or assertion]
CONTEXT: [relevant section of the think brief and red team report]

YOUR JOB: Search for evidence that either supports or refutes this claim.
- Run 2-3 targeted web searches
- Look for: specific data, historical examples, counterexamples
- Be honest about what you find — don't try to confirm or deny, just report

OUTPUT:
启动2-4个研究代理,专门针对红队测试发现的漏洞。这些研究是针对性的,而非宽泛的——每个代理调查一个特定主张或漏洞。
代理提示模板:
你是一名事实核查员和研究分析师。红队测试提出了一个需要验证的特定主张。

主张:[红队测试的具体攻击或断言]
上下文:[分析简报和红队测试报告的相关部分]

你的任务:搜索支持或反驳该主张的证据。
- 进行2-3次针对性网络搜索
- 重点查找:具体数据、历史案例、反例
- 如实报告发现结果——不要试图确认或否认,只做客观报告

输出:

Claim: [restate the claim]

主张:[重述主张]

Verdict: CONFIRMED / REFUTED / MIXED / INSUFFICIENT DATA Evidence:
  • [Finding 1 — source]
  • [Finding 2 — source] Implication: [What this means for the original analysis]
undefined
Verdict: CONFIRMED(确认)/ REFUTED(反驳)/ MIXED(混合)/ INSUFFICIENT DATA(数据不足) 证据:
  • [发现1 — 来源]
  • [发现2 — 来源] 影响: [这对原始分析意味着什么]
undefined

Step 4.3 — Update Research Document

步骤4.3 — 更新研究文档

Append gap research findings to the research document:
markdown
undefined
将漏洞研究发现追加到研究文档中:
markdown
undefined

Gap Research (Round 1)

漏洞研究(第一轮)

[Findings from targeted research on red team claims]

---
[针对红队测试主张的针对性研究结果]

---

Phase 5 — Re-Think (Round 2)

第五阶段 — 重新分析(第二轮)

Run /think again, but now with THREE sources of context:
  1. The original research (Phase 1)
  2. The red team findings (Phase 3)
  3. The gap research (Phase 4)
再次运行/think,但现在包含三个上下文来源:
  1. 原始研究(第一阶段)
  2. 红队测试结果(第三阶段)
  3. 漏洞研究(第四阶段)

Execution

执行方式

Execute the /think workflow again with enriched context:
  1. Triage: May select different frameworks this round based on what the red team found
  2. Every sub-analyst receives: original research + red team report + gap research
  3. Analysts should explicitly address the kill shots and wounds from the red team
  4. Write to
    thoughts/deepthink/YYYY-MM-DD-<slug>-think-r2.md
Agent prompt addition:
RED TEAM FINDINGS (Round 1):
[Include the full red team report]

GAP RESEARCH:
[Include gap research findings]

IMPORTANT: The red team found these specific weaknesses in the previous analysis.
Your job is NOT to ignore them or defend against them — it's to produce the most
accurate analysis possible given ALL available information, including the adversarial
findings. If the red team was right about a weakness, incorporate that reality.
If you have evidence they were wrong, state it with the evidence.

结合丰富的上下文再次执行/think工作流:
  1. 筛选框架:根据红队测试发现,本轮可能选择不同的框架
  2. 每个子分析师收到:原始研究 + 红队测试报告 + 漏洞研究
  3. 分析师应明确回应红队测试发现的kill shots和wounds
  4. 写入至
    thoughts/deepthink/YYYY-MM-DD-<slug>-think-r2.md
代理提示补充内容:
红队测试结果(第一轮):
[包含完整的红队测试报告]

漏洞研究:
[包含漏洞研究发现]

重要提示:红队测试在之前的分析中发现了这些特定弱点。你的任务不是忽略或防御这些弱点,而是基于所有可用信息(包括对抗性发现)生成最准确的分析。如果红队测试对某个弱点的判断正确,就将该现实纳入分析。如果有证据表明他们的判断错误,请附上证据说明。

Phase 6 — Re-Red-Team (Round 2)

第六阶段 — 再次红队测试(第二轮)

Run /red-team on the Round 2 think brief.
针对第二轮分析简报运行/red-team。

Execution

执行方式

Same protocol as Phase 3, but prosecutors receive:
  • The Round 2 think brief (primary target)
  • The Round 1 red team report (so they know what was already found)
  • The gap research (so they know what was verified/refuted)
Write to
thoughts/deepthink/YYYY-MM-DD-<slug>-redteam-r2.md
与第三阶段协议相同,但检察官收到:
  • 第二轮分析简报(主要目标)
  • 第一轮红队测试报告(了解已发现的问题)
  • 漏洞研究(了解已验证/反驳的内容)
写入至
thoughts/deepthink/YYYY-MM-DD-<slug>-redteam-r2.md

Convergence Check

收敛检查

After the Round 2 red team completes, check convergence:
CONVERGENCE CHECK:
- Round 1 verdict: [SURVIVES/WOUNDED/DEAD]
- Round 2 verdict: [SURVIVES/WOUNDED/DEAD]
- New kill shots in Round 2: [count]
- Max failure probability delta: [X]%
- Verdict stability: [STABLE/UNSTABLE]
If converged (same verdict, no new kill shots, deltas < 5%): proceed to Phase 7 If not converged AND iterations < 3: go back to Phase 4 (Gap Research) for another round If max iterations reached: proceed to Phase 7 with a note about remaining instability
Present the convergence status to the user:
undefined
第二轮红队测试完成后,检查收敛情况:
收敛检查:
- 第一轮verdict:[SURVIVES/WOUNDED/DEAD]
- 第二轮verdict:[SURVIVES/WOUNDED/DEAD]
- 第二轮新发现的kill shots:[数量]
- 最大失败概率变化幅度:[X]%
- Verdict稳定性:[STABLE(稳定)/UNSTABLE(不稳定)]
如果已收敛(相同verdict,无新kill shots,变化幅度<5%):进入第七阶段 如果未收敛且迭代次数<3:回到第四阶段(漏洞研究)进行下一轮迭代 如果达到最大迭代次数:进入第七阶段,并注明剩余不稳定性
向用户呈现收敛状态:
undefined

Convergence Status

收敛状态

Round 1: [verdict] | Round 2: [verdict] New kill shots: [count] Max probability delta: [X]% Status: [CONVERGED — proceeding to report / ITERATING — round 3 / MAX ITERATIONS — proceeding with caveats]

---
第一轮: [verdict] | 第二轮: [verdict] 新kill shots数量: [数量] 最大概率变化幅度: [X]% 状态: [已收敛 — 进入报告阶段 / 迭代中 — 第三轮 / 达到最大迭代次数 — 附带说明进入报告阶段]

---

Phase 7 — Generate HTML Report

第七阶段 — 生成HTML报告

This is the final deliverable. Generate a comprehensive, single-file HTML report that presents ALL findings from the pipeline.
这是最终交付成果。生成一份综合性单文件HTML报告,展示流程中的所有发现。

Report Structure

报告结构

Write to
thoughts/deepthink/YYYY-MM-DD-<slug>-report.html
The report has these sections:
  1. Hero Section — Title, one-line recommendation, conviction badge, date
  2. Executive Summary — 3-5 paragraphs synthesizing everything
  3. The Idea — What we analyzed, restated clearly
  4. Market Landscape — Competitive landscape table, market size, funding data, key players (from Phase 1 research)
  5. Intelligence Brief — The final think brief (from the last round), including:
    • Frameworks applied
    • Core Argument
    • Key Insight
    • Load-bearing conditions
    • Failure modes with probabilities
  6. Red Team Findings — The final red team results:
    • Kill shots (if any survived)
    • Wounds
    • Compounding attacks
    • Survival verdict
    • Revised failure probabilities table
  7. Convergence Analysis — How the analysis evolved across rounds:
    • What changed between rounds
    • What stabilized
    • Remaining uncertainties
  8. How We Win — The specific path to winning this market:
    • First move
    • Defensibility strategy (which Powers to build)
    • Timeline
    • Key milestones
  9. How We Lose — The specific ways this fails:
    • Top failure modes (with probabilities)
    • Competitor responses that kill us
    • Market dynamics that work against us
    • The "nightmare scenario"
  10. What to Validate First — Prioritized list of assumptions to test
  11. Appendix: Research Sources — All sources cited across the research phases
写入至
thoughts/deepthink/YYYY-MM-DD-<slug>-report.html
报告包含以下部分:
  1. Hero区域 — 标题、一句话建议、可信度徽章、日期
  2. 执行摘要 — 3-5段整合所有内容
  3. 目标想法 — 清晰重述分析对象
  4. 市场格局 — 竞争格局表格、市场规模、融资数据、关键参与者(来自第一阶段研究)
  5. 情报简报 — 最新一轮的分析简报,包括:
    • 使用的框架
    • 核心论点
    • 关键洞察
    • 关键假设条件
    • 带概率的失败模式
  6. 红队测试结果 — 最终红队测试结果:
    • 留存的kill shots(如有)
    • wounds
    • 复合攻击
    • 存活verdict
    • 修正后的失败概率表格
  7. 收敛分析 — 分析结果在各轮迭代中的演变:
    • 各轮间的变化
    • 趋于稳定的内容
    • 剩余不确定性
  8. 取胜路径 — 赢得市场的具体路径:
    • 第一步行动
    • 防御策略(需要构建的能力)
    • 时间线
    • 关键里程碑
  9. 失败风险 — 可能失败的具体方式:
    • 主要失败模式(带概率)
    • 致命的竞争对手回应
    • 不利的市场动态
    • "噩梦场景"
  10. 优先验证项 — 按优先级排列的待验证假设列表
  11. 附录:研究来源 — 所有研究阶段引用的来源

HTML Design System

HTML设计规范

Use this design system (matches existing reports in this project):
css
:root {
  --bg: #0a0a0f;
  --surface: #12121a;
  --surface2: #1a1a26;
  --border: #2a2a3a;
  --text: #e8e8f0;
  --text-dim: #888899;
  --text-muted: #555566;
  --accent: #7c6ff7;
  --accent-glow: rgba(124,111,247,0.15);
  --green: #4ade80;
  --green-dim: rgba(74,222,128,0.12);
  --amber: #fbbf24;
  --amber-dim: rgba(251,191,36,0.12);
  --red: #f87171;
  --red-dim: rgba(248,113,113,0.12);
  --blue: #60a5fa;
  --blue-dim: rgba(96,165,250,0.12);
  --cyan: #22d3ee;
  --cyan-dim: rgba(34,211,238,0.12);
}
Design principles:
  • Dark mode, professional, presentation-ready
  • Hero section with gradient background (purple/cyan for think reports, red/amber for red team)
  • Use the deepthink accent: gradient from purple (#7c6ff7) to cyan (#22d3ee) — represents the blend of analytical thinking and adversarial testing
  • Conviction badges: GREEN for HIGH, AMBER for MEDIUM, RED for LOW
  • Verdict badges: GREEN for SURVIVES, AMBER for WOUNDED, RED for DEAD
  • Section-alternating backgrounds (--bg and --surface)
  • Framework cards in a responsive grid
  • Competitive landscape as a styled table
  • Failure probabilities in a comparison table (original vs. red team vs. final)
  • Typography: system font stack, 15px body, 1.6 line-height
  • Max width: 960px centered
  • All styles embedded (single-file report)
  • Print-friendly
  • Responsive layout
  • Semantic HTML
Key visual components:
Hero:
  • Eyebrow: "DEEP THINK INTELLIGENCE REPORT"
  • Title: The idea/market name with accent-colored emphasis
  • Subtitle: The one-line recommendation
  • Badge row: conviction level + iterations completed + frameworks used count
Market Landscape Section:
  • Competitive grid or table with player cards
  • Funding totals visualization
  • Market size callout box
Intelligence Section:
  • Framework selection cards (showing which were used and why)
  • Core argument in a highlighted blockquote
  • Key insight in an accent-bordered callout
  • Conditions and failure modes as structured lists
Red Team Section:
  • Kill shots in red-bordered cards
  • Wounds in amber-bordered cards
  • Bruises as a compact list
  • Failure probability comparison table with color-coded deltas
  • Verdict banner
Convergence Section:
  • Round-by-round comparison showing evolution
  • Probability convergence visualization (simple table showing how estimates changed)
How We Win / How We Lose:
  • Two-column layout where possible
  • Win path as a numbered timeline
  • Lose scenarios as severity-rated cards
使用以下设计规范(与项目中现有报告一致):
css
:root {
  --bg: #0a0a0f;
  --surface: #12121a;
  --surface2: #1a1a26;
  --border: #2a2a3a;
  --text: #e8e8f0;
  --text-dim: #888899;
  --text-muted: #555566;
  --accent: #7c6ff7;
  --accent-glow: rgba(124,111,247,0.15);
  --green: #4ade80;
  --green-dim: rgba(74,222,128,0.12);
  --amber: #fbbf24;
  --amber-dim: rgba(251,191,36,0.12);
  --red: #f87171;
  --red-dim: rgba(248,113,113,0.12);
  --blue: #60a5fa;
  --blue-dim: rgba(96,165,250,0.12);
  --cyan: #22d3ee;
  --cyan-dim: rgba(34,211,238,0.12);
}
设计原则:
  • 深色模式,专业风格,可直接用于展示
  • Hero区域使用渐变背景(分析报告用紫色/青色,红队报告用红色/琥珀色)
  • 使用deepthink主题色:从紫色(#7c6ff7)到青色(#22d3ee)的渐变——代表分析思维与对抗性测试的结合
  • 可信度徽章:绿色代表高可信度,琥珀色代表中等可信度,红色代表低可信度
  • Verdict徽章:绿色代表SURVIVES,琥珀色代表WOUNDED,红色代表DEAD
  • 交替背景色的章节(--bg和--surface)
  • 响应式网格布局的框架卡片
  • 样式化的竞争格局表格
  • 失败概率对比表格(原始值 vs 红队测试值 vs 最终值)
  • 排版:系统字体栈,15px正文字号,1.6行高
  • 最大宽度:960px居中显示
  • 所有样式内嵌(单文件报告)
  • 支持打印
  • 响应式布局
  • 语义化HTML
关键视觉组件:
Hero区域:
  • 小标题:"DEEP THINK INTELLIGENCE REPORT"
  • 标题:想法/市场名称,重点内容使用主题色高亮
  • 副标题:一句话建议
  • 徽章栏:可信度等级 + 完成的迭代次数 + 使用的框架数量
市场格局章节:
  • 参与者卡片组成的竞争网格或表格
  • 融资总额可视化
  • 市场规模标注框
情报分析章节:
  • 框架选择卡片(展示使用的框架及原因)
  • 高亮引用块中的核心论点
  • 主题色边框标注的关键洞察
  • 结构化列表呈现的假设条件和失败模式
红队测试章节:
  • 红色边框卡片展示kill shots
  • 琥珀色边框卡片展示wounds
  • 紧凑列表展示bruises
  • 带颜色编码变化的失败概率对比表格
  • Verdict横幅
收敛分析章节:
  • 逐轮对比展示演变过程
  • 概率收敛可视化(简单表格展示估算值的变化)
取胜路径/失败风险:
  • 尽可能采用双栏布局
  • 取胜路径为编号时间线
  • 失败场景为按严重程度评级的卡片

HTML Generation

HTML生成

Generate the full HTML inline. Do NOT use a template file or external tool. The HTML should be complete, valid, and render beautifully when opened in a browser.
The report should be substantial — typically 800-1500 lines of HTML including embedded CSS. This is the final deliverable and should feel polished.

直接内联生成完整HTML。不要使用模板文件或外部工具。HTML应完整、有效,在浏览器中打开时呈现精美效果。
报告内容应充实——通常包含800-1500行HTML(含内嵌CSS)。这是最终交付成果,需具备专业质感。

Final Presentation

最终展示

After writing all files, present a summary to the user:
undefined
所有文件写入完成后,向用户呈现摘要:
undefined

Deep Think Complete: [Idea Title]

深度研究完成:[想法标题]

Iterations: [N] rounds of think/red-team Convergence: [CONVERGED / MAX ITERATIONS] Final verdict: [SURVIVES / WOUNDED / DEAD] Final conviction: [LOW / MEDIUM / HIGH]

Core Argument: [3-5 sentences]
Key Insight: [1-2 sentences — the non-obvious finding]
How We Win: [1-2 sentences — the path]
How We Lose: [1-2 sentences — the biggest risk]

Files generated:
  • Research:
    thoughts/deepthink/YYYY-MM-DD-<slug>-research.md
  • Think R1:
    thoughts/deepthink/YYYY-MM-DD-<slug>-think-r1.md
  • Red Team R1:
    thoughts/deepthink/YYYY-MM-DD-<slug>-redteam-r1.md
  • [Gap Research, Think R2, Red Team R2 if applicable]
  • Report:
    thoughts/deepthink/YYYY-MM-DD-<slug>-report.html
Open the HTML report in your browser for the full analysis.

---
迭代次数: [N]轮分析/红队测试 收敛状态: [已收敛 / 达到最大迭代次数] 最终Verdict: [SURVIVES / WOUNDED / DEAD] 最终可信度: [低 / 中 / 高]

核心论点: [3-5句话]
关键洞察: [1-2句话 — 非显而易见的发现]
取胜路径: [1-2句话 — 具体路径]
失败风险: [1-2句话 — 最大风险]

生成的文件:
  • 研究文档:
    thoughts/deepthink/YYYY-MM-DD-<slug>-research.md
  • 分析简报R1:
    thoughts/deepthink/YYYY-MM-DD-<slug>-think-r1.md
  • 红队测试R1:
    thoughts/deepthink/YYYY-MM-DD-<slug>-redteam-r1.md
  • [如适用:漏洞研究、分析简报R2、红队测试R2]
  • 报告:
    thoughts/deepthink/YYYY-MM-DD-<slug>-report.html
在浏览器中打开HTML报告查看完整分析内容。

---

Quality Standards

质量标准

Research must be real. Every market claim, competitor mention, and funding number should come from actual web searches, not general knowledge. Cite sources.
Numeric probabilities everywhere. No "likely" or "possible" — specific percentages with stated assumptions. This applies to the think briefs, red team reports, AND the final HTML report.
The convergence loop is the differentiator. The value of /deepthink over just running /think then /red-team is that findings from each round inform the next. The final analysis should be materially better than what a single pass would produce.
The HTML report must be beautiful. This is the deliverable the user will share. It should look like it came from a top-tier strategy consultancy, not a markdown renderer. Invest in the CSS and structure.
How We Win and How We Lose must be specific. Not generic strategy advice — specific moves, specific competitors, specific timelines, specific failure modes tied to the research and analysis conducted.
Take positions. The report should have a clear recommendation with conviction level. The user wants to know what to do, not a balanced overview of possibilities.

研究必须真实。 每个市场主张、竞争对手提及和融资金额都应来自实际网络搜索,而非通用知识。注明来源。
所有内容需包含数值概率。 不要使用"可能"或"大概"——需提供具体百分比及假设说明。这适用于分析简报、红队测试报告以及最终HTML报告。
收敛循环是核心差异。 /deepthink相比单独运行/think再运行/red-team的价值在于,每一轮的发现会为下一轮提供信息。最终分析结果应比单次分析显著更优。
HTML报告必须精美。 这是用户将分享的交付成果。它应看起来来自顶级战略咨询公司,而非markdown渲染器。投入精力优化CSS和结构。
取胜路径和失败风险必须具体。 不要提供通用战略建议——需提供与开展的研究和分析相关的具体行动、具体竞争对手、具体时间线、具体失败模式。
明确立场。 报告应包含清晰的建议及可信度等级。用户需要知道该做什么,而非对各种可能性的平衡概述。

Cost & Timing Notes

成本与时间说明

  • This skill spawns many agents across multiple phases. It's the most expensive skill in the toolkit — use it for ideas that warrant serious analysis.
  • Typical run: 15-30 minutes depending on iteration count
  • Agent model: sonnet for all sub-agents (research, think analysts, red team prosecutors)
  • Expected output: 5-8 markdown files + 1 HTML report
  • 该技能在多个阶段启动大量代理。它是工具包中成本最高的技能——仅用于值得深入分析的想法。
  • 典型运行时间:15-30分钟,取决于迭代次数
  • 代理模型:所有子代理(研究、分析分析师、红队检察官)均使用sonnet
  • 预期输出:5-8个markdown文件 + 1个HTML报告