deep-research

name	deep-research
description	Comprehensive research framework that combines web search, content analysis, source verification, and iterative investigation to conduct in-depth research on any topic. Use when you need to perform thorough research with multiple sources, cross-validation, and structured findings.

Deep Research Framework

Overview

The Deep Research skill provides a systematic approach to conducting thorough investigations on any topic. It combines multiple tools and methodologies to gather, analyze, verify, and synthesize information.

Core Components

1. Research Planning

• Define research objectives
• Identify key questions
• Establish search criteria
• Determine validation requirements

2. Information Gathering

• Multi-source web search
• Content extraction from various formats
• Source diversity verification
• Temporal relevance assessment

3. Analysis & Synthesis

• Cross-reference multiple sources
• Identify patterns and contradictions
• Evaluate source credibility
• Organize findings systematically

4. Validation & Verification

• Fact-checking against authoritative sources
• Cross-validation of claims
• Identify potential biases
• Assess information reliability

Research Workflow

Phase 1: Initial Investigation

0. Perspective Alignment (视角模式, optional)

⚠️ This step only executes when a perspective/analytical framework is specified. Skip entirely when no perspective is given.

When the user specifies a perspective (e.g., "用 Serenity 的框架", "chokepoint 视角", "用某投资人的视角"):

1. Load perspective framework:

   • If referencing an installed persona Skill (e.g., serenity-reply): read its SKILL.md, extract mental models and decision heuristics
   • If referencing a built-in framework: use the pre-defined core questions
   • If custom: distill 3-5 core questions from user description

2. Translate framework into research dimensions: For each mental model, define:

   • Core question it asks
   • Specific search terms it suggests
   • What findings it would prioritize

3. Perspective-driven search strategy: Augment broad search with perspective-specific queries:

   • Example (chokepoint perspective): add "monopoly chokepoint", "sole supplier", "irreplaceable", "barrier to entry"
   • Example (NVIDIA signal perspective): add "NVIDIA investment", "NVIDIA roadmap", "NVIDIA supply chain"
   • Example (info asymmetry perspective): add "institutional buying", "small cap undervalued", "overlooked"

4. Output: Internal perspective config (not shown to user):

   Perspective: [name]
   Core questions: [2-5 questions]
   Search dimensions: [custom search terms per dimension]
   Priority rules: [which findings rank higher]
   Confidence labels: [4-tier system definitions]
   

5. Topic Analysis

   • Clarify research scope
   • Identify key concepts and terms
   • Define specific questions to answer
   • If perspective mode: align questions with perspective's core questions

6. Broad Search

   • Use web_search to identify major sources
   • Gather diverse perspectives
   • Map the landscape of available information
   • If perspective mode: supplement broad search with perspective-specific search terms (see Step 0)

7. Source Prioritization

   • Rank sources by authority and relevance
   • Identify primary vs. secondary sources
   • Note publication dates and context

Phase 2: Deep Dive

1. Detailed Content Extraction

   • Use web_fetch to retrieve full articles/pages
   • Extract key information systematically
   • Maintain source attribution

2. Cross-Reference Analysis

   • Compare claims across multiple sources
   • Identify agreements and disagreements
   • Note inconsistencies for further investigation

3. Expert Sources

   • Seek academic papers, expert opinions
   • Look for peer-reviewed sources
   • Identify recognized authorities on the topic

Phase 3: Synthesis & Validation

1. Pattern Recognition

   • Identify consistent themes across sources
   • Highlight areas of disagreement
   • Note gaps in available information

2. Fact Verification

   • Cross-check claims against authoritative sources
   • Verify dates, statistics, and attributions
   • Identify potential misinformation

3. Bias Assessment

   • Evaluate source objectivity
   • Identify potential conflicts of interest
   • Consider temporal context of information

Phase 4: Report Generation — ⚠️ MANDATORY POLISHER + VISUALIZATION STEP

⚠️ CRITICAL — Two mandatory steps before final output:

1. report-polisher-zh 研报润色 — MUST run before Mermaid conversion
2. Mermaid 可视化 — ASCII diagrams MUST be converted to Mermaid Both steps are mandatory. Do not skip.

---

Step 1: report-polisher-zh 研报润色（必须在 Mermaid 之前执行）

在生成 Mermaid 图表之前，先对报告全文进行专业研报润色与 AI 痕迹清除。具体流程：

1. 加载 report-polisher-zh skill：读取 ~/.qclaw/workspace/skills/report-polisher-zh/SKILL.md
2. 研报结构检查（按清单逐项核查）：
   • 标题规范性（含公司/行业名称、核心观点或投资评级）
   • 摘要完整性（核心逻辑、关键数据、投资建议）
   • 章节结构（行业概况、公司分析、财务分析、风险提示等）
   • 数据引用规范（来源标注、时间节点、数值单位统一）
   • 图表标注规范（编号连续、标题清晰、来源标注）
   • 风险提示完整性（主要风险、具体描述、风险等级）
3. 朱雀检测对抗三步法（针对统计概率分布，非口语化）：
   • 术语注入：用低频专业术语替代高频通俗词（"很多"→"众多"、"但是"→"然而"），使用行业特有表达（"赛道"、"估值锚"、"业绩兑现"）
   • 信息压缩：提高信息密度，删除冗余修饰词，合并同类信息，每句话必须承载有效信息
   • 句式打散：长短句极端混合，打破节奏规律性，避免"首先...其次...最后..."套路结构
4. 改写强度选择（根据 AI 痕迹程度）：
   • 轻度（10-20%）：术语替换 + 句式微调
   • 中度（30-50%）：段落重组 + 信息压缩
   • 深度（60-80%）：结构重构 + 逻辑重排
5. 保留 Mermaid 代码块：润色过程中不修改 Mermaid 图表代码块，只处理文字描述部分

⚠️ 无效方法（已验证失败，禁止使用）：

• ❌ 口语化改写
• ❌ 情感注入
• ❌ 简单同义词替换
• ❌ 增加"我认为"等主观表达

有效策略：

• ✅ 术语注入
• ✅ 信息压缩
• ✅ 句式打散
• ✅ 适度瑕疵注入
• ✅ 个人表达习惯注入

⚠️ 注意：研报润色和 Mermaid 优化是两个独立步骤。润色处理文字内容；Mermaid 优化处理图表。二者顺序不能颠倒。

---

When the research involves any of the following, you MUST invoke the mermaid-diagrams skill and convert ASCII/text diagrams into proper Mermaid syntax:

• 📊 Architecture diagrams (公司架构、业务结构、股权结构)
• 🔄 Process flows (业务流程、重整理赔流程、重组时间线)
• 🏢 Organizational charts (股权结构图、集团子公司关系图)
• 📈 Market comparison tables (竞争格局矩阵)
• 🔗 Relationship diagrams (供应链关系、上下游关系、概念股图谱)
• 📋 Timeline diagrams (发展历程、重大事件时间轴)

Conversion rules (ASCII → Mermaid):

ASCII 类型	Mermaid 类型	理由
树状/层级文本图	flowchart TD/LR	展示层级关系和决策路径
股权结构文字	flowchart TB + 子图 subgraph	清晰展示控制链
业务流程步骤	flowchart LR + 决策节点	展示流程与分支
公司沿革时间线	flowchart LR 或 gantt	展示时间序列
竞争格局对比	quadrantChart 或表格	不适合 Mermaid → 保留 Markdown 表格
架构分层图	C4 diagram 或 flowchart	系统/业务架构

⚠️ 关键约束（Mermaid 图表规则）：

1. 节点数 ≤ 15 个（超出则拆分为多个图）
2. 每条连线必须有文字标签
3. 必须有标题/图注
4. 禁止用 Mermaid 画数据图表（柱状图/折线图等）
5. 产出为 .mmd 文件或嵌入 Markdown 的 Mermaid 代码块

🎨 公众号风格配置（柔和马卡龙版）—— 强制应用

所有 Mermaid 图表必须使用以下配置，确保颜色温柔、排版舒展，完全适配公众号阅读体验：

%%{init: {
  'theme': 'base',
  'themeVariables': {
    'primaryColor': '#FFE5D9',
    'primaryTextColor': '#5D4E37',
    'primaryBorderColor': '#E8C4B8',
    'lineColor': '#B8A99A',
    'secondaryColor': '#D4E7D4',
    'secondaryTextColor': '#4A6741',
    'secondaryBorderColor': '#A8C8A8',
    'tertiaryColor': '#E8DFF5',
    'tertiaryTextColor': '#5D4E6D',
    'tertiaryBorderColor': '#C8B8D8',
    'background': '#FFFBF5',
    'mainBkg': '#FFF5EE',
    'nodeBorder': '#E8D5C4',
    'clusterBkg': '#F5F0EB',
    'clusterBorder': '#D4C4B8',
    'titleColor': '#6B5B4F',
    'edgeLabelBackground': '#FFF9F0',
    'fontFamily': 'PingFang SC, Microsoft YaHei, sans-serif',
    'fontSize': '15px'
  },
  'flowchart': {
    'curve': 'basis',
    'padding': 20,
    'nodeSpacing': 50,
    'rankSpacing': 80,
    'diagramPadding': 30
  }
}}%%

马卡龙色板（节点专用）：

色块	色值	适用场景
🍑 蜜桃粉	#FFE5D9	核心节点、重要结论
🌿 抹茶绿	#D4E7D4	产业链上游、供给端
💜 薰衣紫	#E8DFF5	科技创新、AI相关
🍋 柠檬黄	#FFF3CD	数据指标、业绩表现
🌊 天空蓝	#D6EAF8	需求端、下游应用
🌸 樱花粉	#FADBD8	风险提示、负面因素

公众号适配排版规范：

1. 节点文字：中文优先，单节点≤20字，分行显示
2. 连线标签：必须添加，说明关系（如"驱动"、"传导"、"受益"）
3. 子图分组：使用 subgraph 划分逻辑区域，背景色用 #F8F4F0
4. 整体布局：优先 TD（上下）或 LR（左右），避免斜线交叉
5. 节点形状：
   • 矩形 [] → 常规节点
   • 圆角 () → 起点/终点
   • 菱形 {} → 判断/决策
   • 圆柱 [()] → 数据库/存储

Step-by-step conversion workflow:

Step 1: 识别报告中的 ASCII 图/文字架构图
         ↓
Step 2: 判断最适合的 Mermaid 类型 (flowchart / C4 / gantt / etc.)
         ↓
Step 3: 读取 mermaid-diagrams skill 获取该类型的标准语法
         ↓
Step 4: 逐节点转换（节点 label 保持中文，≤ 40字符）
         ↓
Step 5: 验证 Mermaid 语法（无断行语法错误）
         ↓
Step 6: 嵌入 Markdown 报告（或输出独立 .mmd 文件）

📋 公众号风格 Mermaid 示例（完整模板）：

%%{init: {
  'theme': 'base',
  'themeVariables': {
    'primaryColor': '#FFE5D9',
    'primaryTextColor': '#5D4E37',
    'primaryBorderColor': '#E8C4B8',
    'lineColor': '#B8A99A',
    'secondaryColor': '#D4E7D4',
    'secondaryTextColor': '#4A6741',
    'secondaryBorderColor': '#A8C8A8',
    'tertiaryColor': '#E8DFF5',
    'tertiaryTextColor': '#5D4E6D',
    'tertiaryBorderColor': '#C8B8D8',
    'background': '#FFFBF5',
    'mainBkg': '#FFF5EE',
    'nodeBorder': '#E8D5C4',
    'clusterBkg': '#F5F0EB',
    'clusterBorder': '#D4C4B8',
    'titleColor': '#6B5B4F',
    'edgeLabelBackground': '#FFF9F0',
    'fontFamily': 'PingFang SC, Microsoft YaHei, sans-serif',
    'fontSize': '15px'
  },
  'flowchart': {
    'curve': 'basis',
    'padding': 20,
    'nodeSpacing': 50,
    'rankSpacing': 80,
    'diagramPadding': 30
  }
}}%%
flowchart TD
    subgraph 上游供给["🏭 上游供给端"]
        A["原材料\n涨价压力"] --> B["产能出清\n供给收缩"]
    end
    
    subgraph 中游传导["⚙️ 中游制造端"]
        B --> C["成本传导\n提价落地"]
        C --> D["毛利修复\n业绩改善"]
    end
    
    subgraph 下游需求["🛒 下游需求端"]
        E["AI算力爆发\n需求激增"] --> C
        F["地缘冲突\n避险需求"] --> G["资源品\n价格飙升"]
    end
    
    G --> A
    
    subgraph A股机会["📈 A股投资机会"]
        D --> H["涨价链龙头\n业绩兑现"]
        G --> I["资源股\n通胀受益"]
    end
    
    style A fill:#FFE5D9,stroke:#E8C4B8,color:#5D4E37
    style E fill:#E8DFF5,stroke:#C8B8D8,color:#5D4E6D
    style F fill:#D6EAF8,stroke:#A8C8D8,color:#4A678D
    style H fill:#D4E7D4,stroke:#A8C8A8,color:#4A6741
    style I fill:#FFF3CD,stroke:#E8D8A8,color:#6B5B4F

⚠️ 每个图表开头必须包含完整的 %%{init: {...}}%% 配置块，否则无法应用马卡龙风格。

Phase 4 完整输出清单：

1. ✅ Humanizer-zh 去AI化（文字内容已去AI味，保留 Mermaid 代码块）
2. ✅ Executive Summary（执行摘要，2-3句，人性化表达）
3. ✅ Mermaid 可视化图表（所有 ASCII 架构图已转换）
4. ✅ Structured findings（按主题组织，去AI化）
5. ✅ Source evaluation（来源可信度评估）
6. ✅ Limitations（研究局限性，去AI化）
7. ✅ Remaining questions（待深入研究的问题）

1. Structured Summary

• Executive summary of key findings
• Detailed findings organized by theme
• Supporting evidence for each claim

2. Mermaid Visualization (MANDATORY)

• Convert ALL ASCII/text architecture diagrams to Mermaid
• Include at minimum: corporate structure, business overview, key timelines
• Use appropriate diagram type per content (see conversion table above)

3. Source Evaluation

• Assessment of source credibility
• Identification of limitations
• Confidence levels for different claims

4. Remaining Questions

• Areas requiring further investigation
• Conflicting information needing resolution
• Gaps in current knowledge

---

Phase 5: Mermaid → Instagram 信息图导出（报告完成后执行）

触发条件：报告已通过 Phase 4 完成润色 + Mermaid 嵌入，Markdown 文件已保存到磁盘后执行。 核心原则：只生成图片，严禁修改报告 Markdown 原文（不使用 --update 参数）。

Step 1: 定位报告文件与输出目录

1. 确认最终报告的 .md 文件路径（Phase 4 保存位置）
2. 图片输出目录 = 报告文件所在目录（同级目录，不另建子文件夹）
   • 例如：报告路径为 ~/Desktop/A股深度产研报告/XX报告-20260412.md
   • 则图片输出到 ~/Desktop/A股深度产研报告/
   • 通过 -o <报告所在目录> 参数指定

Step 2: 执行 Mermaid 批量转图

cd ~/projects/flowchart-to-instagram

# 从 Markdown 中提取所有 Mermaid 代码块，批量生成 PNG
# ⚠️ 不使用 --update，保证报告 Markdown 原文不被改动
python scripts/md2images.py "<报告.md完整路径>" -o "<报告所在目录>" --prefix fig_

参数说明：

参数	作用	注意
<报告.md完整路径>	指定要处理的 Markdown 文件	绝对路径或相对路径均可
-o "<报告所在目录>"	图片输出到报告同级目录	与报告同目录，方便查找
--prefix fig_	图片文件名前缀（如 fig_1.png、fig_2.png）	可自定义，默认为 chart_

⚠️ 禁止事项：

• ❌ 禁止使用 --update 参数（会将 Mermaid 代码块替换为图片链接，破坏报告原文）
• ❌ 禁止修改报告 Markdown 文件（图片是报告的附属产物，报告原文保持不变）

预期输出：

• 报告同级目录下生成 fig_1.png、fig_2.png、... 等图片文件
• 图片数量 = 报告中 Mermaid 代码块的数量
• 图片内容为 Instagram 风格信息图，适合公众号投放

Step 3: 验证输出

1. 检查输出目录下是否生成了预期数量的 PNG 文件
2. 确认报告 Markdown 原文未被修改（Mermaid 代码块完整保留）
3. 向用户报告：图片数量、文件名、存放路径

报告最终交付物清单（Phase 4 + Phase 5）

#	交付物	格式	路径
1	完整研究报告（含 Mermaid 代码块）	.md	报告所在目录
2	Mermaid 信息图（Instagram 风格）	.png	与报告同目录（fig_N.png）

---

Tools Integration

Web Research

• web_search: Initial broad search to identify sources
• web_fetch: Retrieve detailed content from specific URLs
• browser: For complex sites or when web_fetch fails

Content Processing

• read: Process downloaded content or documents
• write: Create structured research notes
• edit: Refine and organize findings

Mermaid Visualization (MANDATORY — Phase 4)

⚠️ Use mermaid-diagrams skill for ALL diagram generation. ASCII diagrams in reports are a quality failure. Every architecture, process, or relationship diagram must be rendered as proper Mermaid.

• mermaid-diagrams skill: Read the skill file to get standard syntax for flowchart, C4, gantt, sequenceDiagram, classDiagram, erDiagram
• Output: .mmd files or mermaid code blocks embedded in Markdown
• See Phase 4 rules for node count limit (≤ 15), labeling rules, and type selection

🎨 公众号风格（强制应用）：

• 所有图表必须使用 马卡龙色系（蜜桃粉 #FFE5D9、抹茶绿 #D4E7D4、薰衣紫 #E8DFF5 等）
• 配置块 %%{init: {...}}%% 必须置于图表最开头
• 字体：PingFang SC, Microsoft YaHei, sans-serif，字号 15px
• 布局舒展：nodeSpacing: 50, rankSpacing: 80, padding: 20
• 节点分行显示，连线必须有中文标签

Memory & Organization

• memory_get / memory_search: Reference previous research
• write: Create persistent research records
• Structured file organization for findings

Research Quality Standards

Source Diversity

• Include multiple perspectives on controversial topics
• Balance popular and academic sources
• Include international viewpoints when relevant
• Seek primary sources when possible

Temporal Relevance

• Prioritize recent information for fast-changing topics
• Consider historical context for trend analysis
• Note when information was published
• Flag potentially outdated information

Authority Assessment

• Prioritize peer-reviewed academic sources
• Consider author credentials and institutional affiliation
• Check for potential conflicts of interest
• Verify organizational reputation

Iterative Research Approach

Cycle 1: General Overview

• Broad search to understand the topic landscape
• Identify key terms, concepts, and stakeholders
• Establish initial research questions

Cycle 2: Focused Investigation

• Targeted searches based on initial findings
• Deep dive into specific aspects
• Begin synthesis of information

Cycle 3: Validation & Refinement

• Verify key claims across multiple sources
• Resolve contradictions
• Refine understanding based on evidence

Cycle 4: Synthesis & Reporting

• Combine findings into coherent narrative
• 🔴 Humanizer-zh 去AI化（MUST run before Mermaid, text content only）
• 🔴 Convert ALL ASCII diagrams to Mermaid (mermaid-diagrams skill is MANDATORY)
• Identify remaining uncertainties
• Prepare final research report
• Quality gate: (1) Has text been humanized? (2) Does the report contain any ASCII art/box diagrams that should be Mermaid?

Cycle 5: Mermaid 信息图导出

• 报告保存到磁盘后，使用 flowchart-to-instagram skill 将 Mermaid 代码块转为 PNG
• 图片输出到报告同目录，不修改报告原文（见 Phase 5 规范）
• 验证 PNG 生成数量与 Mermaid 代码块数量一致

Output Structure

Research Report Template

# [Research Topic] - Deep Research Report

> **研报润色说明**：本报告已通过 report-polisher-zh 润色，文字表达符合专业研报规范，
> Mermaid 图表代码块在润色过程中已保留原样。

## Executive Summary
[2-3 sentence summary of key findings — humanized, natural tone]

## Research Questions
[Specific questions investigated]

## Methodology
[Description of research approach and tools used]

## Key Findings
[Main discoveries organized by theme — humanized language]

## [REQUIRED] Mermaid Visualizations
[Convert all ASCII diagrams to Mermaid code blocks.
 At minimum include: corporate structure, business overview, key event timeline.
 Mermaid code blocks go in this section. See Phase 4 Mermaid rules.]

## Supporting Evidence
[Evidence supporting each finding with sources]

## Contradictions/Debates
[Areas of disagreement among sources]

## Source Credibility Assessment
[Evaluation of information sources]

## Limitations
[Identified limitations in research — humanized language]

## Perspective Review (视角审查, perspective mode only)
> ⚠️ This section only appears when a perspective/analytical framework was specified.

### Framework-by-Framework Review
[For each mental model in the perspective framework:]
- What findings does this model explain?
- What questions does this model raise that the report didn't cover?
- What are the limitations of applying this model here?

### Conclusion Confidence Table
| Conclusion | Confidence Tier | Notes |
|-----------|----------------|-------|
| [conclusion] | Direct Evidence / Pattern Induction / Framework Extrapolation / Insufficient Evidence | [source/logic] |

> ⚠️ Investment disclaimer: Framework perspective, not investment advice. DYOR.

## Further Research Needed
[Questions requiring additional investigation]

📦 最终交付物（Phase 5 补充）： 报告 Markdown 保存到磁盘后，自动执行 Phase 5，将报告中的所有 Mermaid 代码块导出为 PNG 图片：

• 图片格式：fig_1.png、fig_2.png、...
• 图片路径：与报告 .md 文件同目录
• 报告 Markdown 原文不被修改（Mermaid 代码块保留在报告中）

⚠️ REMINDER: Before finalizing the report:

1. ALWAYS invoke report-polisher-zh skill and polish text content first (preserve Mermaid code blocks)
2. THEN invoke mermaid-diagrams skill and convert any ASCII/text architecture diagrams to proper Mermaid syntax.
3. FINALLY invoke Phase 5 — use flowchart-to-instagram skill to export Mermaid diagrams as PNG images to the same directory as the report, without modifying the report markdown. See Phase 4 → report-polisher-zh + Mermaid Visualization sections for full rules. See Phase 5 → Mermaid → Instagram 信息图导出 for image generation rules.

Use Cases

Academic Research

• Literature reviews
• Topic exploration
• Source verification

Business Intelligence

• Market analysis
• Competitive research
• Technology trends

Fact Checking

• Claim verification
• Misinformation identification
• Source credibility assessment

Personal Learning

• Deep topic exploration
• Concept clarification
• Question resolution

Quality Assurance

• Always verify critical claims against multiple sources
• Flag information that seems unreliable
• Maintain skepticism toward sensational claims
• Prioritize authoritative sources over anonymous ones
• Document all sources for verification purposes