Exa Search

Exa Web Toolkit

A skill for web-powered research tasks backed by Exa: 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 鈥?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.

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 "research paper" 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 "research paper" 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 for strictly academic results. 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) in addition to the standard citation format. If a DOI is present, prefer the DOI link.

Setup

This skill uses the 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:

uv run --with exa-py python "$SKILL_PATH/scripts/exa_search.py" --help

If you prefer a persistent install:

uv pip install "exa-py>=1.14.0"

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.

First, check if a .env file exists in the project root and contains EXA_API_KEY. If so, load it:

dotenv -f .env run -- uv run --with exa-py python "$SKILL_PATH/scripts/exa_search.py" "your query"

If dotenv isn't available, install it: pip install python-dotenv[cli] or uv pip install python-dotenv[cli].

If there's no .env, export the key for the session:

export EXA_API_KEY="your-key"

Verify by running any script with --help 鈥?it will exit cleanly if the key is set and auth-check runs only when a real query is made.

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 鈥?this file (routing and setup)
• references/web-search.md 鈥?detailed web search reference with academic strategy
• references/web-extract.md 鈥?URL content extraction reference
• scripts/exa_search.py 鈥?CLI wrapper around client.search_and_contents
• scripts/exa_extract.py 鈥?CLI wrapper around client.get_contents