Market Research Skill: A Consulting-style Market Research Workflow Based on Multi-agent Architecture

This article introduces the market-research-skill project, a market research skill designed specifically for Claude Code that uses a multi-agent architecture to implement a consulting-style product decision research workflow. It addresses the time-consuming and labor-intensive issues of traditional market research, helps teams conduct systematic market analysis and product decision-making, and enables non-professional decision-makers to obtain structured support.

Claude Code is an AI programming assistant launched by Anthropic, which expands its capabilities through a "skill" mechanism. market-research-skill is a member of this ecosystem, focusing on the field of market research and product decision-making. The skill-based design allows market research capabilities to be easily reused; after installation, professional-level processes can be quickly launched without having to build methodologies from scratch.

The core is a multi-agent architecture that imitates the division of labor in a consulting team: market analysts handle macro trends, competitor researchers focus on competitor strategies, user researchers analyze demand pain points, technical evaluators pay attention to feasibility, and business analysts are responsible for profit forecasting. It adopts classic consulting methodologies, including SWOT, Porter's Five Forces, TAM/SAM/SOM analysis, etc., and emphasizes the output of actionable recommendations.

The complete workflow includes stages of problem definition, data collection, analysis and synthesis, solution generation, and evaluation and recommendation; multi-agents can execute in parallel or iteratively. It is suitable for entrepreneurs' market validation, product managers' roadmap planning, investors' project evaluation, and feasibility studies by corporate strategy departments. The value lies in compressing weeks of work into hours and lowering the professional threshold.

Technical key points include prompt engineering (defining agent roles), context management (coherent information sharing), tool integration (web search, etc.), and output formatting (professional reports). Compared to single-agent systems: it offers specialization (deeply optimized for the field), parallelization (reduces time), quality assurance (cross-validation), and scalability (adding new agents), but the coordination mechanism is a challenge.

Limitations: Data freshness is limited by the model's knowledge cutoff date; it cannot conduct in-depth interviews with real users; supplementary knowledge is needed for highly specialized fields. Improvement directions: Enhance real-time data acquisition, integrate external tools (databases/APIs), support custom analysis frameworks, and provide human-machine collaboration interfaces.

This project represents the direction of AI-assisted decision-making: it does not replace humans but provides systematic support for decision-making. The multi-agent model can be extended to scenarios such as technology selection and investment decision-making. Conclusion: It democratizes professional market research capabilities; although it cannot replace consultants, as a fast and low-cost starting point, it provides value for product decision-making and has great future potential.