exa-search

Files (5)

• SKILL.md
• web-extract.md · references/
• web-search.md · references/
• exa_extract.py · scripts/
• exa_search.py · scripts/

SKILL.md (first lines)

# 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
export EXA_API_KEY="your-key"
```

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`

From K-Dense-AI/scientific-agent-skills, MIT.

More from K-Dense-AI/scientific-agent-skills