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Request Research Task

Purpose

This task provides a unified interface for any agent to request specialized research from the Research Coordinator, which can spawn up to 3 domain-specific researcher agents to attack problems from different angles.

Key Features

Multi-Perspective Analysis: Coordinator spawns specialized researchers with different domain expertise
Web Search Capabilities: Researchers have access to current information and data
Adaptive Specialization: Research agents adapt to specific domains as needed by the requesting context
Research Logging: All synthesis results stored in indexed research log to avoid duplicate work
Configurable Team Size: Default 3 researchers, configurable based on complexity

Usage Scenarios

From Any Agent

Any agent can call this task to get specialized research assistance:

*task request-research

Common Use Cases

Analyst: Competitive analysis, market research, industry trends
Architect: Technology assessment, scalability analysis, security research
PM: Market validation, user research, feasibility studies
Dev: Technical implementation research, library comparisons, best practices
QA: Testing methodologies, quality standards, compliance requirements

Task Process

1. Research Request Specification

The task will elicit a structured research request with these components:

Research Context

Requesting Agent: Which agent is making the request
Project Context: Current project phase and relevant background
Previous Research: Check research log for related prior work
Urgency Level: Timeline constraints and priority

Research Objective

Primary Goal: What specific question or problem needs researching
Success Criteria: How to measure if research achieved its objective
Scope Boundaries: What to include/exclude from research
Decision Impact: How results will be used

Domain Specialization Requirements

Primary Domain: Main area of expertise needed (technical, market, user, etc.)
Secondary Domains: Additional perspectives required
Specific Expertise: Particular skills or knowledge areas
Research Depth: High-level overview vs deep technical analysis

Output Requirements

Format: Executive summary, detailed report, comparison matrix, etc.
Audience: Who will consume the research results
Integration: How results feed into next steps
Documentation: Level of source citation needed

2. Research Coordination

The Research Coordinator will:

Check Research Log: Review docs/research/research-index.md for prior related work
Design Research Strategy: Plan multi-perspective approach
Spawn Researcher Agents: Deploy 1-3 specialized researchers with distinct angles
Monitor Progress: Coordinate between researchers to avoid overlap
Synthesize Results: Combine findings into coherent analysis

3. Research Execution

Each Researcher Agent will:

Adapt Domain Expertise: Configure specialization based on assigned perspective
Conduct Web Research: Use search capabilities to gather current information
Analyze and Synthesize: Process information through domain-specific lens
Generate Findings: Create structured report for their perspective
Cite Sources: Document credible sources and evidence

4. Result Delivery

To Requesting Agent

Executive Summary: Key findings and recommendations
Detailed Analysis: Comprehensive research results
Source Documentation: Links and citations for verification
Next Steps: Recommended actions or follow-up research

To Research Log

Research Entry: Concise summary stored in docs/research/YYYY-MM-DD-research-topic.md
Index Update: Add entry to docs/research/research-index.md
Tag Classification: Add searchable tags for future reference

5. Quality Assurance

Source Credibility: Verify information from reputable sources
Cross-Perspective Validation: Ensure consistency across researcher findings
Bias Detection: Identify and flag potential biases or limitations
Completeness Check: Confirm all research objectives addressed

Research Request Template

When executing this task, use this structure for research requests:

research_request:
  metadata:
    requesting_agent: '[agent-id]'
    request_date: '[YYYY-MM-DD]'
    priority: '[high|medium|low]'
    timeline: '[timeframe needed]'

  context:
    project_phase: '[planning|development|validation|etc]'
    background: '[relevant project context]'
    related_docs: '[PRD, architecture, stories, etc]'
    previous_research: '[check research log references]'

  objective:
    primary_goal: '[specific research question]'
    success_criteria: '[how to measure success]'
    scope: '[boundaries and limitations]'
    decision_impact: '[how results will be used]'

  specialization:
    primary_domain: '[technical|market|user|competitive|regulatory|etc]'
    secondary_domains: '[additional perspectives needed]'
    specific_expertise: '[particular skills required]'
    research_depth: '[overview|detailed|comprehensive]'

  team_config:
    researcher_count: '[1-3, default 3]'
    perspective_1: '[domain and focus area]'
    perspective_2: '[domain and focus area]'
    perspective_3: '[domain and focus area]'

  output:
    format: '[executive_summary|detailed_report|comparison_matrix|etc]'
    audience: '[who will use results]'
    integration: '[how results feed into workflow]'
    citation_level: '[minimal|standard|comprehensive]'

Integration with Existing Agents

Adding Research Capability to Agents

To add research capabilities to existing agents, add this dependency:

dependencies:
  tasks:
    - request-research.md

Then add a research command:

commands:
  - research {topic}: Request specialized research analysis using task request-research

Research Command Examples

*research "competitor API pricing models" (from PM)
*research "microservices vs monolith for our scale" (from Architect)
*research "React vs Vue for dashboard components" (from Dev)
*research "automated testing strategies for ML models" (from QA)

Research Log Structure

Research Index (`docs/research/research-index.md`)

# Research Index

## Recent Research

- [2024-01-15: AI Model Comparison](2024-01-15-ai-model-comparison.md) - Technical analysis of LLM options
- [2024-01-12: Payment Gateway Analysis](2024-01-12-payment-gateway-analysis.md) - Market comparison of payment solutions

## Research by Category

### Technical Research

- AI/ML Models
- Architecture Decisions
- Technology Stacks

### Market Research

- Competitive Analysis
- User Behavior
- Industry Trends

Individual Research Files (`docs/research/YYYY-MM-DD-topic.md`)

# Research: [Topic]

**Date**: YYYY-MM-DD
**Requested by**: [agent-name]
**Research Team**: [perspectives used]

## Executive Summary

[Key findings and recommendations]

## Research Objective

[What was being researched and why]

## Key Findings

[Main insights from all perspectives]

## Recommendations

[Actionable next steps]

## Research Team Perspectives

### Perspective 1: [Domain]

[Key insights from this angle]

### Perspective 2: [Domain]

[Key insights from this angle]

### Perspective 3: [Domain]

[Key insights from this angle]

## Sources and References

[Credible sources cited by research team]

## Tags

[Searchable tags for future reference]

Important Notes

Research Log Maintenance: Research Coordinator automatically maintains the research index
Duplicate Prevention: Always check existing research before launching new requests
Source Quality: Prioritize credible, recent sources with proper attribution
Perspective Diversity: Ensure research angles provide genuinely different viewpoints
Synthesis Quality: Coordinator must reconcile conflicting findings and highlight uncertainties
Integration Focus: All research should provide actionable insights for decision-making

Error Handling

Web Search Failures: Graceful degradation to available information
Conflicting Research: Document disagreements and uncertainty levels
Incomplete Coverage: Flag areas needing additional research
Source Quality Issues: Clearly mark uncertain or low-confidence findings

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