| name | market-research-agent |
| description | Analyze markets, competitors, user segments, and trends to produce evidence-based business intelligence. Use when evaluating market opportunities, pricing strategy research, or due diligence for investments. Use when analyzeing markets, competitors, user segments, and trends to produce evidence-based. |
| domain | agents |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | ai-agents |
| tags | ["agent","ai-agent","automation","market","orchestration","research"] |
| version | 1.0.0 |
Market Research Agent
When to Use
Trigger phrases:
-
"market research agent"
-
"Analyze markets, competitors, user segments, and trends to produce evidence-base"
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Evaluating market opportunity before building a product
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Analyzing competitors before launching or pivoting
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Pricing strategy research for new or existing products
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Understanding user segments and their needs
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Tracking market trends and technology shifts
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Due diligence for investment or acquisition decisions
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Go-to-market planning for new features or products
When NOT to Use
- When the task is simple enough for a single command
- When real-time human judgment is required
- When the agent lacks access to required tools or data
Overview
Market Research Agent is an AI agent skill for agent orchestration. It enables autonomous execution of complex tasks with minimal human intervention.
Capabilities
- Autonomous operation — Execute multi-step market research agent workflows independently
- Context awareness — Adapt behavior based on current state and history
- Error recovery — Handle failures gracefully with retry and fallback logic
- Integration — Connect with external tools and services as needed
Workflow
from dataclasses import dataclass
@dataclass
class Task:
name: str
priority: int
assigned_agent: str
def orchestrate(tasks: list[Task]) -> dict:
results = {}
for task in sorted(tasks, key=lambda t: t.priority):
results[task.name] = execute(task)
return results
- Initialize — Set up the agent context and load required resources
- Plan — Break down the task into executable steps
- Execute — Run each step, monitoring for errors and adapting as needed
- Verify — Validate results against acceptance criteria
- Report — Summarize outcomes and suggest next steps
Configuration
- Define task objectives and constraints clearly
- Set appropriate timeout and retry limits
- Configure tool access and permissions
- Enable logging for debugging and audit
Anti-Rationalization Table
| Rationalization | Reality |
|---|
| "I will just do it manually" | Agents automate repetitive tasks — manual work does not scale |
| "The agent will figure it out" | Without clear instructions, agents hallucinate. Give explicit context. |
| "One agent is enough" | Complex tasks benefit from specialized agents working in parallel |
Process
- Scope — Define research questions, identify data sources, set time boundaries
- Gather — Collect data from primary sources, APIs, and public records
- Synthesize — Analyze findings, identify patterns, produce actionable report
Verification
