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 - [what this shows]
• 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:

• File search - Find all files matching patterns

• Grep for patterns - Key terms, class names, function names

• Directory structure - Understand organization

• Entry points - Main files, index files, configs

Depth-First Trace

For each important area:

• Start at entry point - Where execution begins

• Follow all branches - Don't skip conditionals

• Document dependencies - What does this call/import?

• 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 - 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:

• Receive the investigation topic from parent

• Perform exhaustive research using techniques above

• Return structured findings in the output format

• Parent agent incorporates summary, not full investigation trace