Professional Research Report Skill

Overview

This skill produces professional, consulting-grade research reports in Markdown format, covering domains such as market analysis, consumer insights, brand strategy, financial analysis, industry research, competitive intelligence, investment research, and macroeconomic analysis. It operates across two distinct phases:

1. Phase 1 — Analysis Framework Generation: Given a research subject, produce a rigorous analysis framework including chapter skeleton, per-chapter data requirements, analysis logic, and visualization plan.
2. Phase 2 — Report Generation: After data has been collected by other skills, synthesize all inputs into a final polished report.

The output adheres to McKinsey/BCG consulting voice standards. The report language follows the output_locale setting (default: zh_CN for Chinese).

Data Authenticity Protocol

Strict Adherence Rule: All data presented in the report and visualized in charts MUST be derived directly from the provided Data Summary or External Search Findings.

• NO Hallucinations: Do not invent, estimate, or simulate data. If data is missing, state "Data not available" rather than fabricating numbers.
• Traceable Sources: Every major claim and chart must be traceable back to the input data package.

Core Capabilities

• Design analysis frameworks from scratch given only a research subject and scope
• Transform raw data into structured, high-depth research reports
• Follow the "Visual Anchor → Data Contrast → Integrated Analysis" flow per sub-chapter
• Produce insights following the "Data → User Psychology → Strategy Implication" chain
• Embed pre-generated charts and construct comparison tables
• Generate inline citations formatted per GB/T 7714-2015 standards
• Output reports in the language specified by output_locale with professional consulting tone
• Adapt analytical depth and structure to domain (marketing, finance, industry, etc.)

When to Use This Skill

Always load this skill when:

• User asks for a market analysis, consumer insight report, financial analysis, industry research, or any consulting-grade analytical report
• User provides a research subject and needs a structured analysis framework before data collection
• User provides data summaries, analysis frameworks, or chart files to be synthesized into a report
• User needs a professional consulting-style research report
• The task involves transforming research findings into structured strategic narratives

Phase 1: Analysis Framework Generation

Purpose

Given a research subject (e.g., "Gen-Z Skincare Market Analysis", "NEV Industry Competitive Landscape", "Brand X Consumer Profiling"), produce a complete analysis framework that serves as the blueprint for downstream data collection and final report generation.

Phase 1 Inputs

Phase 1 Workflow

Step 1.1: Understand the Research Subject

• Parse the research subject to identify the core entity (market, brand, product, industry, consumer segment, financial instrument, etc.)
• Identify the analytical domain (marketing, finance, industry, competitive, consumer, investment, macro, etc.)
• Determine the natural analytical dimensions based on domain:

Step 1.2: Select Analysis Frameworks & Models

Based on the identified domain and research subject, select one or more professional analysis frameworks to structure the reasoning in each chapter. The chosen frameworks guide the Analysis Logic in the chapter skeleton (Step 1.3).

Strategic & Environmental Analysis

Market & Growth Analysis

Consumer & Behavioral Analysis

Financial & Valuation Analysis

Competitive & Strategic Positioning

Industry & Supply Chain Analysis

Selection Principles

1. Domain-First: Based on the domain identified in Step 1.1, select 2-4 most relevant frameworks from the toolkit above
2. Complementary: Choose complementary rather than overlapping frameworks (e.g., macro-level with PESTEL + micro-level with Porter's Five Forces)
3. Depth over Breadth: Better to deeply apply 2 frameworks than superficially stack 6
4. Data-Feasible: Selected frameworks must be supportable by downstream data collection skills — if the data required by a framework cannot be reasonably obtained, downgrade or substitute
5. Explicit Mapping: In the chapter skeleton, explicitly annotate which framework each chapter uses and how it is applied

Framework Selection Output Format

## Framework Selection

| Chapter | Selected Framework(s) | Application |
|---------|----------------------|-------------|
| Market Size & Growth Trends | TAM-SAM-SOM + Product Life Cycle | TAM-SAM-SOM to quantify market space, PLC to determine market stage |
| Competitive Landscape Assessment | Porter's Five Forces + Strategic Group Mapping | Five Forces to assess industry competition intensity, Group Mapping to visualize competitive positioning |
| Consumer Profiling | RFM + Consumer Decision Journey | RFM to segment customer value, Decision Journey to identify key conversion nodes |
| Brand Strategy Recommendations | SWOT + Blue Ocean Strategy | SWOT to summarize overall landscape, Blue Ocean to guide differentiation direction |

Step 1.3: Design Chapter Skeleton

Produce a hierarchical chapter structure. Each chapter must include:

1. Chapter Title — Professional, concise, subject-based (follow titling constraints in Formatting section)
2. Analysis Objective — What this chapter aims to reveal
3. Analysis Logic — The reasoning chain or framework (must reference the frameworks selected in Step 1.2)
4. Core Hypothesis — Preliminary hypotheses to be validated or refuted by data

Chapter Skeleton Output Format

## Analysis Framework

### Chapter 1: [Title]
- **Analysis Objective**: [This chapter aims to...]
- **Analysis Logic**: [Framework or reasoning chain used]
- **Core Hypothesis**: [Hypotheses to validate]
- **Data Requirements**: (see Step 1.4)
- **Visualization Plan**: (see Step 1.5)

### Chapter 2: [Title]
...

Step 1.4: Define Data Query Requirements Per Chapter

For each chapter, specify exactly what data needs to be collected. This is the bridge to downstream data collection skills.

Each data requirement entry must include:

Data Requirements Output Format (per chapter)

#### Data Requirements

| # | Data Metric | Data Type | Suggested Sources | Search Keywords | Priority | Time Range |
|---|-------------|-----------|-------------------|-----------------|----------|------------|
| 1 | Market size (billion CNY) | Quantitative | Industry reports, government statistics | "China skincare market size 2024" | P0 | 2020-2025 |
| 2 | CAGR | Quantitative | Industry reports | "skincare CAGR growth rate" | P0 | 2020-2025 |
how to use consulting-analysis
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How to use consulting-analysis on Cursor
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1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add consulting-analysis
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/bytedance/deer-flow --skill consulting-analysiscopy
The skills CLI fetches consulting-analysis from GitHub repository bytedance/deer-flow and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/consulting-analysis
Reload or restart Cursor to activate consulting-analysis. Access the skill through slash commands (e.g., /consulting-analysis) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
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