Deep Research Skill

Core Principles

Every research task MUST follow:

Plan first, execute later - Don't start collecting data immediately
Multi-dimensional exploration - Go deep, don't stay shallow
Verify data - Sources must be cited, important data cross-verified
Dig into anomalies - Unusual points are often the most valuable
Maintain explorer's mindset - Curiosity-driven, always ask "why"

Auto-Trigger Conditions

MUST use this skill when user message involves:

Any "research X", "analyze X", "investigate X" requests
Questions about a topic/industry/company/technology/person
Data-driven decision making
Comparison and evaluation tasks
Deep understanding of a field
Trends, outlook, development direction topics

Research Execution Flow

Phase 1: Research Planning (REQUIRED)

Before any actual research, MUST complete these steps:

1.1 Clarify Research Objectives

Ask yourself:

What does the user really want to know? (beyond the surface question)
What is the ultimate value of this research?
What form should the output be? (report/data/recommendations)

1.2 Brainstorm Research Dimensions (MUST list at least 5)

Think systematically about these aspects:

Dimension Type	Example Questions
Core Direct	What data is needed to directly answer the user's question?
Background Context	What's the history? What's the development timeline?
Related Factors	What are the influencing factors? What are the relationships?
Different Perspectives	How do different viewpoints see this issue?
Risks & Limitations	What are the risks? What are the limitations?
Unique Angles	What unusual points are worth exploring deeply?

Important: The 6th "Unique Angles" dimension is key to creating differentiated value - don't skip it.

1.3 Determine Priorities

Categorize:

Must Have: Core questions that must be answered
Should Have: Value-adding supplementary analysis
Nice to Have: Unique insights and extended thinking

1.4 Define Quality Criteria

Auto-generate review_criteria for subsequent review, including:

Which dimensions must be covered for completeness
What data quality standards to meet
What depth of analysis is expected
What sections the report must contain

Phase 2: Data Collection & Verification

Universal Verification Rules

Prioritize reliable data sources
- Professional tools (e.g., akshare-stocks, akshare-a-shares)
- Official websites and documentation
- Authoritative media and institutions
Cross-verify important data
- At least 2 sources for confirmation
- If conflicts exist, analyze reasons
MUST cite data sources and timestamps
- Source: Where the data came from
- Time: Data timeliness
- If historical data, clearly mark it

Domain-Specific Rules

Domain	Recommended Sources	Verification Method
Finance/Stocks	akshare-stocks + akshare-a-shares + web-research	Dual-source comparison, note trading day
Tech/Products	Official docs + tech blogs + community discussions	Version number verification, release date
News/Events	Multiple media + official statements	Timeline comparison, source tracing
Academic/Professional	Papers + authoritative institution reports	Cite original sources
Companies/Organizations	Official site + financial reports + news	Multi-dimensional cross-reference

Phase 3: Deep Exploration (Core Value Phase)

For each research dimension, execute this flow:

┌─────────────────────────────────────┐
│  1. Basic Information Collection    │
│     - Gather fundamental data/facts │
└─────────────┬───────────────────────┘
              ▼
┌─────────────────────────────────────┐
│  2. Anomaly Identification (KEY)    │
│     - What data looks unusual?      │
│     - What trends deserve attention?│
│     - What's commonly overlooked?   │
└─────────────┬───────────────────────┘
              ▼
┌─────────────────────────────────────┐
│  3. Deep Investigation (anomalies)  │
│     - Why is this happening?        │
│     - What's the underlying cause?  │
│     - What impacts/chain effects?   │
└─────────────┬───────────────────────┘
              ▼
┌─────────────────────────────────────┐
│  4. Form Insights                   │
│     - What does this mean?          │
│     - What value for the user?      │
│     - What actionable suggestions?  │
└─────────────────────────────────────┘

Phase 4: Comprehensive Report

Report Structure (MUST include)

Executive Summary
- 2-3 sentences summarizing core conclusions
- Let readers quickly grasp key points
Key Findings
- Each finding must have data support
- Cite data sources and timestamps
- Distinguish facts from inferences
Deep Analysis
- In-depth exploration of anomalies
- Analysis from different angles
- Unique insights and observations
Data Appendix
- Sources of all cited data
- Retrieval timestamps
- Data limitations explained
Risk Alerts/Limitations
- Data limitations
- Analysis assumptions
- Possible biases
Conclusions & Recommendations
- Clear recommendations based on research
- Directions for further exploration
- Points requiring ongoing attention

Review Guidelines (For Main Agent)

When reviewing research results, check these dimensions:

Coverage Check

Does it answer the user's core question?
Does it cover all planned dimensions?
Are there obvious missing important aspects?

Depth Check

Does analysis stay at surface-level data?
Are anomalies/interesting points explored deeply?
Are there unique insights and observations?
Does it ask "why"?

Data Quality Check

Are data sources cited?
Is important data cross-verified?
Are timestamps clear?
Is historical data clearly marked?

Logic Check

Are conclusions supported by data?
Is the reasoning sound?
Does it distinguish facts from inferences?

When Rejecting, MUST Provide

If deciding to reject, must give:

Specific Issue: Which dimension is insufficient/inaccurate
Missing Content: What else should be analyzed
Exploration Directions: 2-3 specific improvement directions
Improvement Guidance: Tell Sub Agent exactly how to improve

Example rejection feedback:

REJECT

Issue: Analysis stays at surface level, only lists data without analyzing reasons.

Missing dimensions:
- No comparison with industry averages
- No analysis of historical trend changes
- No exploration of reasons for anomalous data points

Improvement directions:
1. Compare with data from other companies in the same industry
2. Analyze trends over the past 3 years
3. Deeply explore why XXX metric is abnormally high

Specific suggestion: XXX data is significantly higher than industry average,
this is worth exploring from market positioning, cost structure,
and competitive advantage perspectives.

Quality Criteria Template

When using delegate_and_review, reference this quality criteria template:

Research report must satisfy:

1. Structural Completeness
   - Contains summary, findings, analysis, data appendix, conclusions
   - Each section has substantial content, not empty

2. Data Quality
   - All data has cited sources
   - Key data verified from at least 2 sources
   - Data timeliness is clear

3. Analysis Depth
   - Not just listing data, must have analysis
   - Anomalies are explored in depth
   - Has unique insights, not generic statements

4. Practical Value
   - Conclusions are clear and actionable
   - Recommendations are specific and implementable
   - Risk alerts are explicit

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