Map Any Industry with the industry-landscape Skill

Where this fits

You're building a research agent that answers questions like "Who are the top 10 players in the EV charging market, and who's disrupting them?" Without a structured approach, your agent ends up scraping random websites, missing key signals, and producing a shallow list. The industry-landscape skill gives your agent a repeatable workflow to combine company databases, academic papers, news, LinkedIn job posts, and market signals into a cohesive competitive map.

Why agents benefit

• Multi-source triangulation: The skill pulls from Apollo (company data), Exa (web search), Google News, academic search, and LinkedIn jobs—so your agent cross-verifies findings instead of trusting one source.
• Structured output: It produces segments, key players, growth indicators, and disruptors—ready to feed into a report or decision framework.
• Job-market as a signal: LinkedIn job postings reveal hiring trends, expansion areas, and tech stacks—an undervalued indicator of market momentum.
• Schema-aware tool calls: The workflow forces sandbase_describe_tool before each call, preventing argument mismatches and making your agent robust to API changes.
• Context-aware research: The skill asks you to define the research question and target, so the agent narrows scope rather than boiling the ocean.

Practical scenarios

Competitive intelligence for a startup pitch

Your agent needs to answer "Who are the incumbents and challengers in AI infrastructure?" The skill combines Apollo's company search with news and academic papers to identify both established players and emerging startups—then uses job postings to spot which companies are scaling aggressively.

Market entry analysis for a new product

Before launching in a niche like "vertical SaaS for dental clinics," your agent maps the landscape: key players, their market segments, and gaps. The skill's akta_industry_search and exa_search tools surface competitors you might miss in a simple web query.

Tracking emerging disruptors in a sector

The skill's Google News bulk articles and LinkedIn job data let your agent monitor shifts over time—like detecting when a new player starts hiring for a novel tech stack or when a research paper signals a breakthrough. This is ideal for a weekly market-scan agent.

Add it to your agent workflow

Install the skill via:

npx skills add sandbaseai/sandbase-skills --skill industry-landscape

Then, in your agent prompt, specify the industry and the output format. For example:

Use the industry-landscape skill to map the electric vehicle battery recycling market. Provide key players, segments, growth indicators, and emerging disruptors.

The skill will guide the agent through the tool selection and execution process, ensuring it calls sandbase_describe_tool first and uses only valid arguments.

Compare before adopting

Before integrating, evaluate:

• Quality signals: Are the underlying tools reliable? Check rate limits and data freshness (Apollo, Google News, etc.).
• Maintenance freshness: The repo has 67 stars and is actively maintained—check recent commits.
• Alternatives: You could build a custom scraper, but you'd lose the structured workflow and multi-source coverage.
• Workflow fit: Does the skill's output format align with your agent's downstream tasks? It's designed for research-heavy agents, not real-time decision systems.

Why it is worth tracking

This skill is a strong example of combining multiple data sources into a single research capability—a pattern that will become standard in agent tooling. Its growing star count and active GitHub suggest community adoption, and the underlying SandBase API is evolving. Keep it on your radar if you're building agents that need competitive intelligence, market research, or due-diligence features. For a closer look, see the skill page on OpenAgentSkill to monitor updates and community feedback.