research

name	research
description	Deep research with parallel sub-agents, query classification, and filesystem artifact passing
allowed-tools	Task, Read, Write, Bash(git log:*), Bash(git diff:*), WebSearch, WebFetch, Glob, Grep
argument-hint	<question or topic to investigate>

Research

Conduct deep, parallel research on a topic using multiple specialized sub-agents.

Research Query

$ARGUMENTS

Phase 1: Query Classification

This is the critical first step. Classify before doing anything else.

Query Types

Type	Characteristics	Sub-agents	Depth per agent
Breadth-first	Multiple independent aspects, surveys, comparisons	5-10	5-10 searches each
Depth-first	Single topic requiring thorough understanding, technical deep-dives	2-4	10-15 searches each
Simple factual	Single fact, specific data point, quick lookup	1-2	3-5 searches each

Classification Decision

After reading the query, determine:

1. Query type: breadth / depth / simple
2. Resource allocation: how many sub-agents to spawn
3. Search domains: codebase, academic, technical docs, news, general web
4. Scope: codebase-only, web-only, or hybrid

Codebase vs. Web Research

• If the query is about this repository (architecture, code patterns, dependencies, history), use codebase tools (Grep, Glob, Read, git commands)
• If the query is about external topics (technologies, standards, comparisons), use web tools (WebSearch, WebFetch)
• Many queries require both: spin up sub-agents for each domain

Phase 2: Parallel Research Execution

Spawn all sub-agents in a single message for true parallelization using the Task tool.

Sub-agent Prompt Protocol

Each Task prompt MUST begin with a depth-mode trigger phrase:

Mode	Trigger prefix	Expected effort
Quick verification	"Quick check:", "Verify:", "Confirm:"	3-5 searches
Focused investigation	"Investigate:", "Explore:", "Find details about:"	5-10 searches
Deep research	"Deep dive:", "Comprehensive:", "Thorough research:"	10-15 searches

Sub-agent Output Protocol (Filesystem Artifacts)

Each sub-agent MUST:

1. Write its full report to /tmp/research_[timestamp]_[topic_slug].md
2. Return ONLY:
   • File path to the full report
   • 2-3 sentence summary
   • Key topics covered
   • Number of sources found

This reduces token usage by ~90% compared to passing full reports inline.

Example Dispatch Patterns

Breadth-first: "Compare Tauri vs Electron vs Neutralino for desktop apps":

Task 1: "Investigate: Tauri's architecture, performance characteristics, and ecosystem maturity"
Task 2: "Investigate: Electron's architecture, performance characteristics, and ecosystem maturity"
Task 3: "Investigate: Neutralino's architecture, performance characteristics, and ecosystem maturity"
Task 4: "Explore: Performance benchmarks comparing Tauri, Electron, and Neutralino"
Task 5: "Investigate: Developer experience, tooling, and community size for each framework"
Task 6: "Quick check: Latest release dates and roadmap status for each framework"

Depth-first: "How does the spec compiler validation pipeline work?":

Task 1: "Deep dive: Trace the spec compiler entry point through all validation phases (codebase research using Grep/Read)"
Task 2: "Comprehensive: Map all error codes and validation rules in the spec compiler"
Task 3: "Thorough research: Document the data flow and intermediate representations used during compilation"

Simple factual: "What license does this project use?":

Task 1: "Quick check: Find the LICENSE file and any license declarations in package manifests"

Phase 3: Synthesis from Filesystem Artifacts

After all sub-agents complete:

1. Collect file references: gather all /tmp/research_*.md paths from sub-agent responses
2. Read reports: use Read to access each research artifact
3. Merge findings:
   • Identify common themes across reports
   • Deduplicate overlapping information
   • Preserve unique insights from each report
   • Flag contradictions between sources
4. Consolidate sources:
   • Merge all cited sources and references
   • Remove duplicates
   • Organize by relevance and credibility
5. Write final report: save to /tmp/research_final_[timestamp].md

Phase 4: Deliver Final Report

Report Structure

# Research Report: [Query Topic]

## Executive Summary
[3-5 paragraph overview synthesizing all findings]

## Key Findings
1. **[Finding 1]**: synthesized from multiple sub-agent reports
2. **[Finding 2]**: cross-referenced and verified
3. **[Finding 3]**: with supporting evidence

## Detailed Analysis

### [Theme 1]
[Comprehensive synthesis from all relevant sub-agent findings]

### [Theme 2]
[Comprehensive synthesis from all relevant sub-agent findings]

## Sources & References
[Consolidated list organized by type: codebase files, documentation, web sources]

## Research Metadata
- Query classification: [breadth / depth / simple]
- Sub-agents deployed: [count and focus areas]
- Total sources analyzed: [count]
- Research artifacts: [list of /tmp/research_*.md files]

Presentation to User

1. Display the Executive Summary and Key Findings directly in conversation
2. Provide the path to the full report file
3. List individual sub-agent report paths so the user can drill into any area
4. Highlight any contradictions or gaps found during synthesis

Research Quality Principles

• Prefer authoritative sources: official docs, academic papers, primary sources over blog posts
• Cross-reference claims: no single-source conclusions for important findings
• Identify gaps: explicitly state what could NOT be determined
• Distinguish fact from inference: clearly label speculation or extrapolation
• Recency matters: prefer recent sources; flag outdated information

Execution

Now classify the query and launch parallel research sub-agents.