Skill detail
exa-search
Searches scientific and technical web content with Exa and extracts page or PDF text in batches, with publication-category and academic-domain filters.
Why we recommend it: Web and scholarly search with PDF extraction is broadly useful for student research.
Needs (stated): Network access · Python
Inspect before use
Read the source instructions and check the requirements before you install this skill.
SKILL.md
The saved excerpt is a snapshot from when this skill was listed. The external source remains the complete and most current version.
---
name: exa-search
description: "Searches scientific and technical web content with Exa and extracts page or PDF text from URLs in batches. Supports scholarly discovery with the publication category and academic domain filters. Applies to requests to search the web, look up current research, fetch a page, or extract an article using Exa."
compatibility: Requires Python 3.11+, exa-py>=2.23.0,<3, an EXA_API_KEY, and internet access.
license: MIT
metadata:
version: "1.4"
last-reviewed: "2026-09-30"
skill-author: Exa
website: https://exa.ai
docs: https://exa.ai/docs
openclaw:
primaryEnv: EXA_API_KEY
envVars:
- name: EXA_API_KEY
required: true
description: Exa search API key.
---
# 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:
```bash
uv run "$SKILL_PATH/scripts/exa_search.py" --help
```
If you prefer a persistent install:
```bash
uv pip install "exa-py>=2.23.0,<3"
```
### Authentication
All commands read the API key from the `EXA_API_KEY` environment variable. Get your Exa API key at [dashboard.exa.ai/api-keys](https://dashboard.exa.ai/api-keys).
First, check if a `.env` file exists in the project root and contains `EXA_API_KEY`. If so, load it:
```bash
dotenv -f .env run -- uv run "$SKILL_PATH/scripts/exa_search.py" "your query"
```
If `dotenv` isn't available, install it: `uv pip install 'python-dotenv[cli]'`.
If there's no `.env`, export the key for the session:
```bash
exRead the full source on GitHub (opens external page)