---

name: research-executor description: Execute complete 7-phase deep research workflow by delegating to the research-orchestrator-agent. Thin wrapper skill that ensures proper agent invocation with structured research prompts.

Research Executor

Overview

The Research Executor is a thin wrapper skill that delegates research execution to the research-orchestrator-agent. It validates inputs, prepares the execution context, and invokes the autonomous orchestrator agent to handle the complete 7-phase deep research workflow.

When to Use

• User provides a structured research prompt (from question-refiner)
• Need to execute systematic research with multiple agents
• Require comprehensive report with verified citations
• Research involves 3+ subtopics requiring parallel investigation

Core Responsibilities

1. Input Validation: Verify structured prompt completeness
2. Agent Invocation: Deploy research-orchestrator-agent with proper context
3. Progress Monitoring: Track agent execution and report status
4. Result Delivery: Return final research package to user

Architecture (Post-Refactoring)

CopyUser Request
     ↓
research-executor skill (this skill - thin wrapper)
     ↓
research-orchestrator-agent (autonomous agent)
     ↓
├── Phase 1: Question Refinement
├── Phase 2: Research Planning
├── Phase 3: Multi-Agent Deployment
├── Phase 4: Source Triangulation
├── Phase 5: Knowledge Synthesis
├── Phase 6: Quality Assurance
└── Phase 7: Output Generation

Key Change: All orchestration logic has been moved to research-orchestrator-agent. This skill only handles:

• Input validation
• Agent deployment
• Error handling at skill level

Quick Start

CopyExecute research using structured prompt:
[STRUCTURED_PROMPT]

The executor will:
1. Validate prompt structure
2. Invoke research-orchestrator-agent
3. Monitor progress
4. Return results from RESEARCH/[topic]/

Input Requirements

Required: Structured research prompt with:

• TASK: Clear research objective
• CONTEXT: Background and significance
• SPECIFIC_QUESTIONS: 3-7 concrete sub-questions
• KEYWORDS: Search terms
• CONSTRAINTS: Timeframe, geography, sources
• OUTPUT_FORMAT: Deliverable specifications

Optional:

• Research type (deep/quick/custom)
• Quality threshold (default: 8.0)
• Max agents (default: 8)
• Token budget per agent (default: 15k)

Output Structure

CopyRESEARCH/[topic]/
├── README.md
├── executive_summary.md
├── full_report.md
├── data/
│   ├── statistics.md
│   └── ontology/
├── sources/
│   ├── bibliography.md
│   └── source_quality_table.md
├── research_notes/
│   └── agent_findings_summary.md
└── appendices/
    ├── methodology.md
    └── limitations.md

Error Handling

Safety Limits

Integration with Agents

Primary Agent: research-orchestrator-agent

• Handles all 7 phases
• Manages agent deployment
• Enforces quality gates
• Coordinates synthesis and validation

Supporting Agents (invoked by orchestrator):

• got-agent: For complex research optimization
• synthesizer-agent: For findings aggregation
• red-team-agent: For quality validation
• ontology-scout-agent: For domain reconnaissance
• Multiple research agents (web, academic, verification)

Key Features

• Simplified Design: ~95% of logic moved to orchestrator agent
• Backwards Compatible: Same interface for users
• Better Error Recovery: Agent-level autonomy improves resilience
• Clearer Separation: Skill = invocation, Agent = execution

Examples

See examples.md for usage scenarios.

Detailed Instructions

See instructions.md for implementation guide.