Use this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This skill operates in two phases — (1) generating a structured analysis framework with chapter skeleton, data query requirements, and analysis logic, and (2) after data collection by other skills, producing the final consulting-grade report with structured narratives, embedded charts, and strategic insights.
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Use this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This skill operates in two phases — (1) generating a structured analysis framework with chapter skeleton, data query requirements, and analysis logic, and (2) after data collection by other skills, producing the final consulting-grade report with structured narratives, embedded charts, and strategic insights.
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:
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.
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.
: Every major claim and chart must be traceable back to the input data package.
Traceable Sources
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
Input
Description
Required
Research Subject
The topic or question to be analyzed
Yes
Scope / Constraints
Geographic scope, time range, industry segment, target audience, etc.
Optional
Specific Angles
Any particular angles or hypotheses the user wants explored
Optional
Domain
The analytical domain: market, finance, industry, brand, consumer, investment, etc.
Inferred
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.)
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).
Value curve, four-action framework (Eliminate-Reduce-Raise-Create)
Differentiated innovation, new market space creation
Perceptual Mapping
Plot brand positions along two consumer-perceived dimensions
Brand positioning analysis, market gap discovery
Industry & Supply Chain Analysis
Framework
Description
Best For
Industry Value Chain
Upstream → Midstream → Downstream decomposition
Industry structure understanding, profit distribution analysis
Gartner Hype Cycle
Technology Trigger → Peak of Inflated Expectations → Trough of Disillusionment → Slope of Enlightenment → Plateau of Productivity
Emerging technology maturity assessment
GE-McKinsey Matrix
Industry Attractiveness × Competitive Strength
Business portfolio prioritization, investment decisions
Selection Principles
Domain-First: Based on the domain identified in Step 1.1, select 2-4 most relevant frameworks from the toolkit above
Complementary: Choose complementary rather than overlapping frameworks (e.g., macro-level with PESTEL + micro-level with Porter's Five Forces)
Depth over Breadth: Better to deeply apply 2 frameworks than superficially stack 6
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
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:
Chapter Title — Professional, concise, subject-based (follow titling constraints in Formatting section)
Analysis Objective — What this chapter aims to reveal
Analysis Logic — The reasoning chain or framework (must reference the frameworks selected in Step 1.2)
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:
Field
Description
Data Metric
The specific metric or data point needed (e.g., "China skincare market size 2020-2025 (in billion CNY)")
Data Type
Quantitative, Qualitative, or Mixed
Suggested Sources
Suggested source categories: Industry reports, financial statements, government statistics, social media, e-commerce platforms, survey data, news
Search Keywords
Suggested search queries for data collection agents
#### 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 |
| 3 | Sub-category share | Quantitative | E-commerce platforms, industry reports | "skincare category share cream serum sunscreen" | P1 | Latest |
| 4 | Policy & regulatory updates | Qualitative | Government announcements, news | "cosmetics regulation 2024" | P2 | Past 1 year |
Step 1.5: Define Visualization & Content Structure Per Chapter
For each chapter, specify the planned visualization and content structure for the final report:
Field
Description
Visualization Type
Chart type: Line chart, bar chart, pie chart, scatter plot, radar chart, heatmap, Sankey diagram, comparison table, etc.
Visualization Title
Descriptive title for the chart
Visualization Data Mapping
Which data indicators map to X/Y axes or segments
Comparison Table Design
Column headers and comparison dimensions for the data contrast table
Argument Structure
The planned "What → Why → So What" narrative outline
Visualization Plan Output Format (per chapter)
#### Visualization & Content Plan**Chart 1**: [Type] — [Title]
- X-axis: [Dimension], Y-axis: [Metric]
- Data source: Corresponds to Data Requirement #1, #2
**Comparison Table**:
| Dimension | Item A | Item B | Item C |
|-----------|--------|--------|--------|
**Argument Structure**:
1.**Observation (What)**: [Surface phenomenon revealed by data]
2.**Attribution (Why)**: [Driving factors or underlying causes]
3.**Implication (So What)**: [Strategic implications or recommended actions]
Step 1.6: Output Complete Analysis Framework
Assemble all outputs into a single, structured Analysis Framework Document:
# [Research Subject] Analysis Framework## Research Overview-**Research Subject**: [...]
-**Scope**: [Geography, time range, industry segment]
-**Analysis Domain**: [Market / Finance / Industry / Brand / Consumer / ...]
-**Core Research Questions**: [1-3 key questions]
## Framework Selection
| Chapter | Selected Framework(s) | Application |
|---------|----------------------|-------------|
| ... | ... | ... |
## Chapter Skeleton### 1. [Chapter Title]-**Analysis Objective**: [...]
-**Analysis Logic**: [...]
-**Core Hypothesis**: [...]
#### Data Requirements
| # | Data Metric | Data Type | Suggested Sources | Search Keywords | Priority | Time Range |
|---|-------------|-----------|-------------------|-----------------|----------|------------|
| ... | ... | ... | ... | ... | ... | ... |
#### Visualization & Content Plan
[Chart plan + Comparison table design + Argument structure]
### 2. [Chapter Title]
...
### N. [Chapter Title]
...
## Data Collection Task List
[Consolidate all P0/P1 data requirements across chapters into a structured task list for downstream data collection skills to execute]
Phase 1 Quality Checklist
Analysis framework covers all natural dimensions for the identified domain
2-4 professional analysis frameworks are selected and explicitly mapped to chapters
Selected frameworks are complementary (not overlapping) and data-feasible
Each chapter has clear Analysis Objective, Analysis Logic (referencing chosen framework), and Core Hypothesis
Data requirements are specific, measurable, and include search keywords
Every chapter has at least one visualization plan
Data priorities (P0/P1/P2) are assigned realistically
The framework is actionable — a data collection agent can execute on the Search Keywords directly
Data Collection Task List is comprehensive and deduplicated
Phase 1→2 Handoff: Data Collection & Chart Generation
After the analysis framework is generated, it is handed off to other data collection skills (e.g., deep-research, data-analysis, web search agents) to:
Execute the Search Keywords from each chapter's data requirements
Collect quantitative data, qualitative insights, and source URLs
Generate charts based on the Visualization & Content Plan
Return a Data Package containing:
Data Summary: Raw numbers, metrics, and qualitative findings per chapter
Chart Files: Generated chart images with local file paths
External Search Findings: Source URLs and summaries for citations
This skill does NOT perform data collection. It only produces the framework (Phase 1) and the final report (Phase 2).
Chart Generation: If a visualization/charting skill is available (e.g., data-analysis, image-generation), chart generation can be deferred to the beginning of Phase 2 — see Step 2.3.
Phase 2: Report Generation
Purpose
Receive the completed Analysis Framework and Data Package from upstream, and synthesize them into a final consulting-grade report.
Phase 2 Inputs
Input
Description
Required
Analysis Framework
The framework document produced in Phase 1
Yes
Data Summary
Collected data organized per chapter from the data collection phase
Yes
Chart Files
Local file paths for generated chart images. If not provided, will be generated in Step 2.3 using available visualization skills
Optional
External Search Findings
URLs and summaries for inline citations
Optional
Phase 2 Workflow
Step 2.1: Receive and Validate Inputs
Verify that all required inputs are present:
Analysis Framework — Confirm it contains chapter skeleton, data requirements, and visualization plans
Data Summary — Confirm it contains data organized per chapter, cross-reference against P0 requirements
Chart Files — Confirm file paths are valid local paths
If any P0 data is missing, note it in the report and flag for the user.
Step 2.2: Map Report Structure
Map the final report structure from the Analysis Framework:
Before writing the report, generate all planned charts from the Analysis Framework's Visualization & Content Plan. This step ensures every sub-chapter has its "Visual Anchor" ready before narrative writing begins.
When to Execute This Step
Chart Files already provided: Skip this step — proceed directly to Step 2.4.
Chart Files NOT provided but a visualization skill is available: Execute this step to generate all charts first.
No Chart Files and no visualization skill available: Skip this step — use comparison tables as the primary visual anchor in Step 2.4, and note the absence of charts.
Chart Generation Workflow
Extract Chart Tasks: Parse all Visualization & Content Plan entries from the Analysis Framework to build a chart generation task list:
#
Chapter
Chart Type
Chart Title
Data Mapping
Data Source
1
2.1
Line chart
Market Size Trend 2020-2025
X: Year, Y: Market Size (billion CNY)
Data Requirement #1, #2
2
3.1
Pie chart
Consumer Age Distribution
Segments: Age groups, Values: Share %
Data Requirement #5
...
...
...
...
...
...
Prepare Chart Data: For each chart task, extract the corresponding data points from the Data Summary.
CRITICAL: Use ONLY the numbers provided in the Data Summary. Do NOT invent or "smooth" data to make charts look better. If data points are missing, the chart must reflect that reality (e.g., broken line or missing bar), or the chart type must be adjusted.
Delegate to Visualization Skill: Invoke the available visualization/charting skill (e.g., data-analysis) for each chart task with:
Validate: Confirm all P0-priority charts have been generated. If any chart generation fails, note it and fall back to comparison tables for that sub-chapter.
Principle: Complete ALL chart generation before starting report writing. This ensures a consistent visual narrative and avoids interleaving generation with writing.
Step 2.4: Write the Report
For each sub-chapter, follow the "Visual Anchor → Data Contrast → Integrated Analysis" flow:
Visual Evidence Block: Embed charts using  — use the file paths collected in Step 2.3
Data Contrast Table: Create a Markdown comparison table for key metrics
Source Rule: Every number in the table must come from the Data Summary. No hallucinations.
Integrated Narrative Analysis: Write analytical text following "What → Why → So What"
Narrative Rule: Narrative must explain the provided data. Do not make claims unsupported by the inputs.
Each sub-chapter must end with a robust analytical paragraph (min. 200 words) that:
Synthesizes conflicting or reinforcing data points
Reveals the underlying user tension or opportunity
Optionally ends with a punchy "One-Liner Truth" in a blockquote (>)
Step 2.5: Final Structure Self-Check
Before outputting, confirm the report contains all sections in order:
Requirement: End each sub-chapter with a robust analytical paragraph (min. 200 words).
Narrative Flow: This paragraph must look like a natural continuation of the text. It must synthesize the section's findings into a strategic judgment.
Content Logic:
Synthesize the conflicting or reinforcing data points above.
Reveal the underlying user tension or opportunity.
Key Insight: Optional: Only if you have a concise, punchy "One-Liner Truth", place it at the very end using a Blockquote (>) to anchor the section.
Insight Depth (The "So What" Chain)
Every insight must connect Data → User Psychology → Strategy Implication:
❌ Bad: "Females are 60%. Strategy: Target females."
✅ Good: "Females constitute 60% with a high TGI of 180. **This suggests**
the purchase decision is driven by aesthetic and social validation
rather than pure utility. **Consequently**, media spend should pivot
towards visual-heavy platforms (e.g., RED/Instagram) to maximize CTR,
treating male audiences only as a secondary gift-giving segment."
References
Inline: Use markdown links for sources (e.g. [Source Title](URL)) when using External Search Findings
References section: Formatted strictly per GB/T 7714-2015
Markdown Rules
Immediate Start: Begin directly with # Report Title — no introductory text
No Separators: Do NOT use horizontal rules (---)
Report Structure Template
# [Report Title]## Abstract
[Executive summary with key takeaways]
## 1. Introduction
[Background, objectives, methodology]
## 2. [Body Chapter Title]### 2.1 [Sub-chapter Title]
| Metric | Brand A | Brand B |
|--------|---------|--------|
| ... | ... | ... |
[Integrated narrative analysis: What → Why → So What, min. 200 words]
> [Optional: One-liner strategic truth]### 2.2 [Sub-chapter Title]
...
## N+1. Conclusion
[Pure objective synthesis, NO bullet points, neutral tone]
[Para 1: The fundamental nature of the group/market]
[Para 2: Core tension or behavior pattern]
[Final: One or two sentences stating the objective truth]
## N+2. References
[1] Author. Title[EB/OL]. URL, Date.
[2] ...
Complete Example
Phase 1 Example: Framework Generation
User provides: Research subject "Gen-Z Skincare Market Analysis"
Phase 1 output (Analysis Framework):
# Gen-Z Skincare Market Analysis Framework## Research Overview-**Research Subject**: Gen-Z Skincare Market Deep Analysis
-**Scope**: China market, 2020-2025, consumers aged 18-27
-**Analysis Domain**: Market Analysis + Consumer Insights
-**Core Research Questions**:
1. What is the size and growth momentum of the Gen-Z skincare market?
2. What is unique about Gen-Z consumer skincare behavior patterns?
3. How can brands effectively reach and convert Gen-Z consumers?
## Chapter Skeleton### 1. Market Size & Growth Trends-**Analysis Objective**: Quantify Gen-Z skincare market size and identify growth drivers
-**Analysis Logic**: Total market → Segmentation → Growth rate → Driver decomposition
-**Core Hypothesis**: Gen-Z is becoming the core engine of skincare consumption growth
#### Data Requirements
| # | Data Metric | Data Type | Suggested Sources | Search Keywords | Priority | Time Range |
|---|-------------|-----------|-------------------|-----------------|----------|------------|
| 1 | China skincare market total size | Quantitative | Industry reports | "China skincare market size 2024 2025" | P0 | 2020-2025 |
| 2 | Gen-Z skincare spending share | Quantitative | Industry reports, e-commerce platforms | "Gen-Z skincare spending share youth" | P0 | Latest |
#### Visualization & Content Plan**Chart 1**: Line chart — China Skincare Market Size Trend 2020-2025
**Argument Structure**:
1. What: Quantified status of market size and Gen-Z share
2. Why: Consumption upgrade, ingredient-conscious consumers, social media driven
3. So What: Brands should prioritize building youth-oriented product lines
### 2. Consumer Profiling & Behavioral Insights
...
## Data Collection Task List
[Consolidated P0/P1 tasks]
Phase 2 Example: Report Generation
After data collection, user provides: Analysis Framework + Data Summary with brand metrics + chart file paths.
Phase 2 output (Final Report) follows this flow:
Start with # Gen-Z Skincare Market Deep Analysis Report
Abstract — 3-5 key takeaways in executive summary form
Introduction — Market context, research scope, data sources
Conclusion — Objective synthesis in flowing prose (no bullets)
References — GB/T 7714-2015 formatted list
Quality Checklists
Phase 1 Quality Checklist (Analysis Framework)
Framework covers all natural analytical dimensions for the identified domain
Each chapter has clear Analysis Objective, Analysis Logic, and Core Hypothesis
Data requirements are specific, measurable, and include actionable Search Keywords
Every chapter has at least one visualization plan with chart type and data mapping
Data priorities (P0/P1/P2) are assigned — P0 items are essential for core arguments
Data Collection Task List is comprehensive, deduplicated, and ready for downstream execution
Framework adapts to the correct domain (market/finance/industry/consumer/etc.)
Phase 2 Quality Checklist (Final Report)
NO HALLUCINATION: All numbers and charts are verified against the input Data Summary
All planned charts generated before report writing (Step 2.3 completed first)
All sections present in correct order (Abstract → Introduction → Body → Conclusion → References)
Every sub-chapter follows "Visual Anchor → Data Contrast → Integrated Analysis"
Every sub-chapter ends with a min. 200-word analytical paragraph
All insights follow the "Data → User Psychology → Strategy Implication" chain
All headings use proper numbering (no "Chapter/Part/Section" prefixes)
Charts are embedded with  syntax
Numbers use English commas for thousands separators
Inline references use markdown links where applicable
References section follows GB/T 7714-2015
No horizontal rules (---) in the document
Conclusion uses flowing prose — no bullet points
Report starts directly with # title — no preamble
Missing P0 data is explicitly flagged in the report
Output Format
Phase 1: Output the complete Analysis Framework in Markdown format
Phase 2: Output the complete Report in Markdown format
Settings
output_locale = zh_CN # configurable per user request
reasoning_locale = en
Notes
This skill operates in two phases of a multi-step agentic workflow:
Phase 1 produces the analysis framework and data collection requirements
Data collection is performed by other skills (deep-research, data-analysis, etc.)
Phase 2 receives the collected data and produces the final report
Dynamic titling: Rewrite topics from the Framework into professional, concise subject-based headers
The Conclusion section must contain NO detailed recommendations — those belong in the preceding body chapters
ZERO HALLUCINATION POLICY: Each statement, chart, and number in the report must be supported by data points from the input Data Summary. If data is missing, admit it.
Traceability: If requested, you must be able to point to the specific line in the Data Summary or External Search Findings that supports a claim.
The framework should adapt its analytical dimensions and depth to the specific domain (financial analysis uses different frameworks than consumer insights)
When the research subject is ambiguous, default to the broadest reasonable scope and note assumptions
