Deep-Research Mode

Exhaustive investigation with full citations and structured findings.

Core Philosophy

"Thorough beats fast. Citations beat claims. Structured beats stream-of-consciousness."

This mode is for when surface-level understanding isn't enough. You're building a complete, citable reference that others can verify.

When to Use

• Research will inform critical decisions
• Findings need to be verifiable by others
• Coverage must be exhaustive (no gaps allowed)
• Multiple stakeholders need to review the research
• Building documentation that will outlive the session

Output Structure

Every deep-research output must include:

1. Executive Summary

2-3 sentences covering:

• What was investigated
• Key finding (one sentence)
• Confidence level (High/Medium/Low)

2. Scope Definition

Included	Excluded
[What was researched]	[What was intentionally skipped]

3. Findings

Each finding must have:

#### Finding: [Title]

**Confidence:** High | Medium | Low

**Evidence:**

- [file.py#L42](file.py#L42) - [what this shows]
- [config.yaml#L15](config.yaml#L15) - [what this shows]

**Analysis:**
[Interpretation of the evidence]

**Implications:**
[What this means for the task at hand]

4. Coverage Report

Area	Files Checked	Confidence
[Component A]	12	High
[Component B]	5	Medium
[Component C]	0	Not investigated

5. Open Questions

• [Question that couldn't be answered with available information]
• [Area that needs human clarification]

Research Techniques

Breadth-First Scan

Before going deep, establish the landscape:

1. File search - Find all files matching patterns
2. Grep for patterns - Key terms, class names, function names
3. Directory structure - Understand organization
4. Entry points - Main files, index files, configs

Depth-First Trace

For each important area:

1. Start at entry point - Where execution begins
2. Follow all branches - Don't skip conditionals
3. Document dependencies - What does this call/import?
4. Note side effects - File writes, API calls, state changes

Cross-Reference

Connect findings across areas:

• Same pattern used differently in different places?
• Inconsistencies between documentation and code?
• Dead code paths?
• Hidden coupling between components?

Citation Standards

Always Cite

• Specific line numbers when referencing code
• File paths for configuration claims
• Test names when citing expected behavior
• Commit hashes for historical claims (if relevant)

Citation Format

[path/to/file.py#L42-L50](path/to/file.py#L42-L50) - Description

Confidence Levels

Level	Meaning	Citation Requirement
High	Verified in code, tests pass	Direct code citation
Medium	Inferred from patterns	Multiple supporting citations
Low	Speculation based on naming/structure	Clearly marked as inference

Quality Checklist

Before completing research:

• All claims have citations
• Coverage report shows no critical gaps
• Confidence levels are assigned to each finding
• Open questions are explicitly listed
• Executive summary captures the essence
• Another agent could verify findings from citations

Anti-Patterns

❌ Don't	✅ Do
"The codebase uses React"	"package.json#L15 lists react@18.2.0 as dependency"
"This probably handles auth"	"Auth handling uncertain - no direct evidence found (Low confidence)"
"I looked at the files"	"Examined 23 files in src/services/, found 4 relevant"
"Everything seems fine"	"No issues found in [scope]. Coverage: [X] files, [Y] functions"

Integration with Explorer Agent

When spawned as a subagent from Explorer:

1. Receive the investigation topic from parent
2. Perform exhaustive research using techniques above
3. Return structured findings in the output format
4. Parent agent incorporates summary, not full investigation trace