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 |
| 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:

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:

1. Execute the Search Keywords from each chapter's data requirements
2. Collect quantitative data, qualitative insights, and source URLs
3. Generate charts based on the Visualization & Content Plan
4. 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

Phase 2 Workflow

Step 2.1: Receive and Validate Inputs

Verify that all required inputs are present:

1. Analysis Framework — Confirm it contains chapter skeleton, data requirements, and visualization plans
2. Data Summary — Confirm it contains data organized per chapter, cross-reference against P0 requirements
3. 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:

1. Abstract — Executive summary with key takeaways
2. Introduction — Background, objectives, methodology
3. Main Body Chapters (2...N) — Mapped from the Framework's chapter skeleton
4. Conclusion — Pure, objective synthesis
5. References — GB/T 7714-2015 formatted references

Step 2.3: Generate Chapter Charts (Pre-Report Visualization)

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

1. Extract Chart Tasks: Parse all Visualization & Content Plan entries from the Analysis Framework to build a chart generation task list:

1. 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.

1. Delegate to Visualization Skill: Invoke the available visualization/charting skill (e.g., data-analysis) for each chart task with:

• Chart type and title
• Structured data
• Axis labels and formatting preferences
• Output file path convention: charts/chapter_{N}_{chart_index}.png

1. Collect Chart File Paths: Record all generated chart file paths for embedding in Step 2.4:

## Generated Charts
| # | Chapter | Chart Title | File Path |
|---|---------|-------------|-----------|
| 1 | 2.1 | Market Size Trend 2020-2025 | charts/chapter_2_1.png |
| 2 | 3.1 | Consumer Age Distribution | charts/chapter_3_1.png |

1. 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:

1. Visual Evidence Block: Embed charts using ![Image Description](Actual_File_Path) — use the file paths collected in Step 2.3
2. 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.

1. 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:

Abstract → 1. Intro