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 extraction (fetching pages, articles, academic PDFs in batch). Use this skill for web-related tasks when the user wants high-quality search or scholarly filtering via category=research paper. Triggers o...
Scanned 9/2/2026
Install to Claude Code
npx -y skills add majiayu000/claude-skill-registry --skill exa-search-k-dense-ai-scientific-agent-ski --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: exa-search
description: "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 extraction (fetching pages, articles, academic PDFs in batch). Use this skill for web-related tasks when the user wants high-quality search or scholarly filtering via category=research paper. Triggers on requests to search, look up, fetch a page, or extract an article."
compatibility: Requires exa-py Python SDK, an EXA_API_KEY, and internet access.
license: MIT
metadata:
version: "1.0"
skill-author: Exa
website: https://exa.ai
docs: https://exa.ai/docs
---
# 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 "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](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:
```bash
uv run --with exa-py python "$SKILL_PATH/scripts/exa_search.py" --help
```
If you prefer a persistent install:
```bash
uv pip install "exa-py>=1.14.0"
```
### Authentication
All commands read the API key from the `EXA_API_KEY` environment variable. Get your Exa API key at [dashboard.exa.ai/api-keys](https://dashboard.exa.ai/api-keys).
First, check if a `.env` file exists in the project root and contains `EXA_API_KEY`. If so, load it:
```bash
dotenv -f .env run -- uv run --with exa-py python "$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:
```bash
export EXA_API_KEY="your-key"
```
Verify by running any script with `--help` — 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` — 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_and_contents`
- `scripts/exa_extract.py` — CLI wrapper around `client.get_contents`
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