Market Research Skill: Turn Raw Data into Agent-Driven Insights

Where this fits

You're building an agent that must answer "Is there a market for X?" or "Who are the key players in Y?" — but you don't want to hard-code a single search API or manually stitch together news, academic papers, and company databases. The market-research skill from sandbase gives your agent a structured workflow to combine web search, scholarly sources, news coverage, and industry data into a single evidence-backed assessment.

Why agents benefit

• Multi-source triangulation — The skill orchestrates tavily_search, exa_search, scholar_search_mixed, and google_news_bulk_articles so your agent can cross-check claims across web, academic, and news domains.
• Structured tool discovery — It forces the agent to call sandbase_describe_tool first, ensuring it uses the current input schema and never guesses parameters — a common failure point in agent tool use.
• Company and industry enrichment — apollo_company_search and akta_industry_search add B2B company profiles and industry-level data, which are missing from generic search results.
• Workflow-first design — The skill defines a clear sequence: understand the question, confirm tool schemas, then execute. This reduces hallucinated steps and keeps the research reproducible.
• Evidence-backed output — The skill's description emphasizes producing "market sizing signals" and "opportunity assessments" — not just raw data dumps, but synthesized conclusions with citations.

Practical scenarios

Pre-seed founder validating a niche

Your agent helps a founder assess "Is there a market for AI-powered contract review for small law firms?" The skill pulls academic papers on legal tech adoption, news mentions of competitors, and Apollo company data for existing legal software vendors — then summarizes market size signals and competitive gaps.

Competitive intelligence for a product launch

Your agent monitors a new entrant in the project management space. It uses google_news_bulk_articles to gather recent coverage, exa_search for product reviews, and scholar_search_mixed for industry trends. The output is a brief that highlights strengths, weaknesses, and market positioning — ready to feed into a go-to-market plan.

Quarterly market landscape review for an investor

Your agent compiles a report on the AI observability sector. It combines akta_industry_search for sector data, apollo_company_search for funding and employee counts, and web search for recent analyst reports. The result is a structured overview with sources, saving hours of manual research.

Add it to your agent workflow

Install the skill directly into your agent project:

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

Then configure your agent to call the skill with a research question. For example, in a Claude or GPT tool-calling loop, you'd first invoke sandbase_describe_tool for tavily_search, then call sandbase_call_tool with the exact parameters. The skill's README provides the full workflow — see the skill page on OpenAgentSkill for a quick overview.

Compare before adopting

Before you commit, evaluate how this skill stacks up against alternatives:

• Quality signals — Check if the underlying APIs (Tavily, Exa, Apollo) provide current, relevant data for your domain. Test with a few edge cases.
• Maintenance freshness — Look at the GitHub repo's last commit date and issue resolution speed. A skill that relies on external APIs can break if schemas change.
• Alternatives — You could build a custom research pipeline using direct API calls, but you'd lose the structured workflow and schema validation. Compare effort vs. benefit.
• Workflow fit — Does your agent already have a research step? If it uses a different tooling style (e.g., MCP), see if this skill can be adapted or if a native alternative exists.

Why it is worth tracking

With 68 stars and active development (as of this writing), market-research is a solid example of a domain-specific agent skill. Its multi-source approach and emphasis on schema validation address real pain points in agent reliability. If you're building agents that need to produce investor-grade market assessments, this skill is worth evaluating now — especially since the underlying APIs are well-known and the skill's workflow is transparent enough to audit. Track it when you next iterate on your agent's research capabilities.