Local Market Research Skill for Agents

Where this fits

A founder wants to open a second cafe in a specific neighborhood and needs to know whether locals complain about competitor service, what the area trends are, and how the brand is perceived there. Instead of manually reading dozens of review pages and social posts, an agent using local-market-research can pull and synthesize those signals into a location brief.

Why agents benefit

• Collects customer sentiment across multiple competitor locations using google_maps_bulk_reviews, no manual scraping required.
• Searches Xiaohongshu notes for local lifestyle content and Weibo discussions for public sentiment, giving context beyond review scores.
• Uses Tavily web search to fill in recent local news, regulations, or neighborhood shifts.
• Forces schema verification via sandbase_describe_tool before every call, reducing integration errors.
• Produces a structured output by location, covering sentiment, competition, trends, and brand perception in one pass.

Practical scenarios

Coffee brand expansion

An agent compares Google Maps reviews of existing coffee shops in three neighborhoods, then checks Xiaohongshu posts tagged with those districts to understand what young locals value: quiet space, oat milk options, pet-friendly seating. Result: a ranked list of neighborhoods by demand and competitor weakness.

Retail pop-up planning

A retail team wants to test a one-week pop-up. The agent uses Weibo search to gauge local sentiment around a shopping street, web search for planned events, and Google Maps reviews for foot-traffic proxies by venue. It returns a recommendation with supporting evidence.

Restaurant rebrand check

A restaurant chain tests whether its brand has a different reputation in two cities. The agent collects reviews and social mentions by city, compares recurring keywords, and flags location-specific issues like delivery complaints or pricing perception.

Add it to your agent workflow

Install the skill:

npx skills add sandbaseai/sandbase-skills --skill local-market-research

Then instruct your agent to follow the skill workflow:

Use the local-market-research skill to analyze the target neighborhood. Read references/sandbase-api-map.md first, call sandbase_describe_tool for each selected tool, then call sandbase_call_tool with the exact tool_name.

Compare before adopting

Compare this skill against a generic web-research plugin or a custom script calling these APIs directly. Look at quality signals: whether Google Maps and Xiaohongshu data remain reliable over time. Check maintenance freshness through recent commits and issue responses. Consider alternatives and workflow fit: does the structured multi-step process integrate cleanly with your existing agent framework?

Why it is worth tracking

At 68 stars and maintained by sandbaseai, this is early but practical. The multi-source local research pattern is genuinely hard to build, and this skill packages it behind a simple interface. Track its evolution at OpenAgentSkill and re-evaluate it whenever you need location-specific market intelligence in agent workflows.