exa-search
by K-Dense-AIexa-search is a web research skill powered by Exa for finding current information and extracting content from URLs. Use it for search, source discovery, article and PDF extraction, and technical or scientific research with semantic retrieval, academic-style filtering, and clear install and usage guidance.
This skill scores 84/100, which means it is a solid listing candidate for directory users. The repository gives a clear install decision signal: it is a real Exa-backed web search/extraction skill with specific routing, setup, and usage guidance, so agents can understand when to use it with less guesswork than a generic prompt.
- Explicit triggerability for web search and URL extraction, including scientific/technical research use cases and a research-paper filter.
- Strong operational clarity in the SKILL.md frontmatter and routing table, which tells agents which capability to choose before acting.
- Substantial, non-placeholder content with headings, code fences, and repository/file references, indicating a real workflow rather than a stub.
- No install command and no support files, so users may need to infer setup steps from prose and external docs.
- The listing evidence shows good intent but limited visible detail from the excerpt, so some edge-case behavior and exact execution steps may still require reading the full skill text.
Overview of exa-search skill
What exa-search does
The exa-search skill is a web research tool built on Exa for finding current information and extracting content from URLs. It is best for users who need the exa-search skill to look up topics, verify facts, pull source text from pages, or gather material from articles and PDFs without hand-curating every result.
When it is the right fit
Use exa-search for Web Research when the job is discovery-plus-reading: “find the best sources, then extract the relevant text.” It is especially useful for technical, scientific, and concept-heavy queries where semantic search beats a plain keyword prompt.
What makes it different
The main differentiator is routing: exa-search splits web search and URL extraction into separate workflows, so the agent can choose the right path instead of treating every request like a generic browse task. It also supports research-oriented filtering, including academic-style source targeting when the query calls for it.
How to Use exa-search skill
Install and prerequisites
For exa-search install, the skill expects the exa-py Python SDK, an EXA_API_KEY, and internet access. If either the API key or network access is missing, the skill cannot complete web search or extraction reliably.
How to prompt it well
A strong exa-search usage prompt states the research target, freshness needs, and source preference. For example: “Use exa-search to find recent sources on lithium iron phosphate battery recycling, prioritize primary and technical sources, then extract the most relevant pages.” That gives the skill a clear search target and a clear extraction goal.
Read these files first
Start with SKILL.md, then follow the linked reference files for the capability you need:
references/web-search.mdfor search strategy and result selectionreferences/web-extract.mdfor fetching page, article, or PDF content- the setup section in
SKILL.mdfor install and authentication details
Practical workflow
A good exa-search guide workflow is: define the question, decide whether you need search or extraction first, run the narrowest query that can still surface strong sources, then extract only the URLs that matter. If you are doing Web Research, include terms for topic, audience, and source type so the skill does not return broad or noisy results.
exa-search skill FAQ
Is exa-search better than a normal prompt?
Usually yes when the task depends on current web sources, source extraction, or finding the right page before reading it. A normal prompt can summarize known information, but exa-search is built to retrieve evidence first.
Does exa-search work for academic or technical research?
Yes. The skill is tuned for scientific and technical content, and it is a good fit when you need source discovery plus extraction from papers, articles, or documentation. If you need broad consumer web results only, it may be more capability than you need.
When should I not use exa-search?
Do not use it if the task is offline-only, if you already have the exact text in hand, or if browsing is blocked in your environment. It is also a poor fit when the goal is opinion writing with no source lookup.
Is it beginner-friendly?
Yes, as long as you can describe what you want found. The main failure mode is being too vague; exa-search works best when the request names the topic, recency, and source quality you want.
How to Improve exa-search skill
Give the skill a sharper research brief
The biggest improvement comes from narrowing the query before search starts. Instead of “research batteries,” use “find recent peer-reviewed or manufacturer sources on sodium-ion battery cycle life, then extract claims with numbers.” That better matches how exa-search returns and filters evidence.
Specify source rules and output shape
If you care about source quality, say so directly: “prefer primary sources,” “include academic domains,” or “exclude blogs and press releases.” For exa-search usage, also say whether you want a ranked source list, extracted passages, or a short synthesis after extraction.
Watch for over-broad retrieval
Common failure modes are searching too widely, extracting irrelevant pages, or mixing search and reading into one underspecified request. If that happens, split the task: first ask exa-search to find sources, then ask it to extract only the best matches.
Iterate with better constraints
After the first pass, refine with what was missing: date range, region, document type, or technical depth. For exa-search for Web Research, adding one or two constraints usually improves precision more than adding more general context.