Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch/extract URL content. Covers: web search (semantic lookups, research, current info — with optional research-paper category and academic domain filtering) and URL extra

Exa Web Toolkit A skill for web powered research tasks backed by [Exa](https://exa.ai): web search and URL extraction. Exa's index combines high quality keyword and semantic retrieval, which makes it well suited to scientific, technical, and conceptual queries. Routing — pick the right capability Read the user's request and match it to one of the capabilities below. Read the corresponding reference file for detailed instructions before running commands. User wants to... Capability Where Look something up, research a topic, find current info Web Search references/web search.md Fetch content from a specific URL (webpage, article, PDF) Web Extract references/web extract.md Install or authenticate Setup Below Decision guide Default to Web Search for topic lookups, research questions, or "what is X?" queries. When the topic is scientific or technical, pass category publication to bias toward scholarly sources, and/or an academic include domains allowlist. See references/web search.md for the two pass academic strategy. Use Web Extract when the user provides a URL or asks you to read/fetch a specific page. Prefer this over the built in WebFetch for batch extraction (multiple URLs in one call) and for academic PDFs. Academic source priority For technical or scientific queries, prefer academic and scientific sources: Peer reviewed journal articles and conference proceedings over blog posts or news Preprints (arXiv, bioRxiv, medRxiv) when peer reviewed versions aren't available Institutional and government sources (NIH, WHO, NASA, NIST) over commercial sites Primary research over secondary summaries Two levers to steer Exa toward scholarly content: 1. category publication biases retrieval toward scholarly sources. 2. include domains with a scholarly allowlist (arxiv.org, nature.com, pubmed.ncbi.nlm.nih.gov, etc.) restricts the domain pool. Combine both to narrow the source pool; verify publication type and peer review status on each source. See references/web search.md for the full pattern. When citing academic sources, include author names and publication year where available (e.g., [Smith et al., 2025](url)) in addition to the standard citation format. If a DOI is present, prefer the DOI link. Setup This skill uses the [ exa py ](https://github.com/exa labs/exa py) Python SDK. The scripts in scripts/ declare their dependencies via PEP 723 inline metadata, so you can run them directly with uv run without a separate install step: If you prefer a persistent install: Authentication All commands read the API key from the EXA API KEY environment variable. Get your Exa API key at [dashboard.exa.ai/api keys](https://dashboard.exa.ai/api keys). First, check if a .env file exists in the project root and contains EXA API KEY . If so, load it: If dotenv isn't available, install it: uv pip install 'python dotenv[cli]' . If there's no .env , export the key for the session: Use help to verify installation and CLI parsing; it does not require an API key or validate authentication. Authentication is checked only when a real query is made. Extraction limits and freshness The extractor accepts 1–100 URLs per POST https://api.exa.ai/contents call and exports each SDK status ( id , status , source ). It rejects larger batches. Use max age hours 0 to request fresh content, 1 for cache only, or a positive age up to 720 hours. Omission uses Exa's cache/fallback policy. published date is estimated publication metadata, not retrieval time. The tested SDK drops per URL error details, so report failures without inventing a cause. See [URL Extraction](references/web extract.md). Verified API and SDK scope Reviewed the [Search API](https://exa.ai/docs/reference/search), [Contents API](https://exa.ai/docs/reference/get contents), and [Exa changelog](https://exa.ai/docs/changelog) on 2026 09 30. Both routes use JSON POST requests and x api key authentication (the SDK sets this header). Search is POST https://api.exa.ai/search ; its SDK kwargs use snake case and are serialized to camelCase. The current scholarly category is publication ; research paper is a compatibility alias in this wrapper. No offset or cursor pagination is documented for these endpoints. Helpers are tested with exa py==2.23.0 , including mocked HTTP transport through the real SDK. Search content flags are explicit: no flags means metadata only; highlights does not implicitly fetch text. Authenticated search/extraction examples below and in the references are illustrative; no paid live calls were made during this review. Tracking header Every script in this skill sets the x exa integration request header to k dense ai scientific agent skills so Exa can attribute usage from the K Dense AI scientific agent skills repo to this integration. Do not remove or rename this header when adapting the scripts. Files in this skill SKILL.md — this file (routing and setup) references/web search.md — detailed web search reference with academic strategy references/web extract.md — URL content extraction reference scripts/exa search.py — CLI wrapper around client.search scripts/exa extract.py — CLI wrapper around client.get contents

exa-search