What it does. 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. Part of K-Dense-AI/scientific-agent-skills (AI Scientist skills) (K-Dense-AI/scientific-agent-skills).


Upstream	K-Dense-AI/scientific-agent-skills
Skill file	skills/exa-search/SKILL.md
License	MIT
Author	K-Dense Inc.
Fetched	2026-09-10

Install

npx skills add K-Dense-AI/scientific-agent-skills --skill exa-search, or copy the skill folder into ~/.claude/skills/exa-search/.
Raw file: curl -sL https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/exa-search/SKILL.md

SKILL.md (verbatim)

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: "1.2"
  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: 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:

--category "research paper" biases retrieval toward scholarly sources.
--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: 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 — 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

Other files in this skill

references/web-extract.md (verbatim)

URL Extraction

Extract content from: $ARGUMENTS

Command

Choose a short, descriptive filename based on the URL or content (e.g., alphafold-paper, nature-editorial). Use lowercase with hyphens, no spaces.

uv run --with exa-py python "$SKILL_PATH/scripts/exa_extract.py" "$ARGUMENTS" \
  --text \
  -o "$FILENAME.json"

You can pass multiple URLs as positional arguments — the script batches them in a single /contents call, which is faster and cheaper than looping.

Content modes:

--text (default if nothing else is passed) returns full-text content
--highlights returns extracted passages instead of full text

Academic content handling

When extracting from academic sources (arXiv, PubMed, journal sites, conference proceedings), use --text to get the full paper text:

uv run --with exa-py python "$SKILL_PATH/scripts/exa_extract.py" "$URL" \
  --text \
  -o "$FILENAME.json"

For arXiv, either the /abs/ page URL or the raw PDF URL works. Prefer /abs/ when available — it has cleaner metadata (title, authors, published date) attached to the result.

Response format

Return content as:

Page Title

For academic papers, include structured metadata when available:

Authors: list of authors (from the author field)
Published: from published_date

Then the extracted content, with these rules:

Keep content verbatim — do not paraphrase or summarize
Parse lists exhaustively — extract EVERY numbered/bulleted item
Strip only obvious noise: nav menus, footers, ads
Preserve all facts, names, numbers, dates, quotes
For academic papers, preserve figure/table captions and references

Partial-result handling — when batching multiple URLs, one or more may fail (paywall, robots.txt, timeout). Report which URLs extracted successfully and which failed, rather than silently dropping failures.

After the response, mention the output file path ($FILENAME.json) so the user knows it's available for follow-up questions.

references/web-search.md (verbatim)

Web Search

Search the web for: $ARGUMENTS

Command

Choose a short, descriptive filename based on the query (e.g., ai-chip-news, crispr-off-target). Use lowercase with hyphens, no spaces.

uv run --with exa-py python "$SKILL_PATH/scripts/exa_search.py" "$ARGUMENTS" \
  --text --highlights \
  -o "$FILENAME.json"

$SKILL_PATH is the path to this skill directory. The -o flag saves the full results to a JSON file so follow-up questions can reuse them without re-querying.

Search type selection — --type controls retrieval mode:

Mode	When to use
`auto` (default)	Exa's general-purpose search. Use this unless you have a reason not to.
`fast`	Lowest latency. Use for simple lookups where speed matters more than nuance.
`deep`	Slowest but highest quality. Use for hard, conceptual, or exhaustive research queries where recall matters more than latency.

Content modes — add any combination:

--text returns full-text content per result
--highlights returns the most relevant passages (good signal-to-noise, lower token cost than full text)

Default to --highlights for broad searches (cheaper, more skimmable). Add --text only when you need to quote or extract in detail.

Filtering options — Exa supports rich filtering via the SDK:

--start-published-date YYYY-MM-DD / --end-published-date YYYY-MM-DD for time-sensitive queries
--include-domains domain1.com,domain2.com to restrict to an allowlist
--exclude-domains spam.com,low-quality.com to drop a blocklist
--category "research paper" to bias toward scholarly content (also: company, news, github, personal site, financial report, people)
--user-location US for locale-specific results

Academic source strategy

For scientific or technical queries, Exa has two strong levers:

1. Use `--category "research paper"`

uv run --with exa-py python "$SKILL_PATH/scripts/exa_search.py" "$ARGUMENTS" \
  --category "research paper" \
  --text --highlights \
  -o "$FILENAME-academic.json"

This biases retrieval toward papers indexed as scholarly content (journals, preprint servers, conference proceedings) rather than blogs or news coverage.

2. Restrict to scholarly domains

For stricter academic filtering, combine the category with an explicit domain allowlist:

uv run --with exa-py python "$SKILL_PATH/scripts/exa_search.py" "$ARGUMENTS" \
  --category "research paper" \
  --include-domains "arxiv.org,biorxiv.org,medrxiv.org,pubmed.ncbi.nlm.nih.gov,nature.com,science.org" \
  --text --highlights \
  -o "$FILENAME-academic.json"

Two-pass pattern for comprehensive coverage

Run both an academic-focused search and an unrestricted one, then merge with academic sources first:

Academic pass: --category "research paper" with the scholarly domain allowlist above.
General pass: the standard command without --category or --include-domains, to catch relevant non-academic sources (news coverage, lab blogs, institutional pages).

Merge results, leading with academic sources. If the query is clearly non-scientific, skip the academic pass.

When to use the two-search pattern: Any query involving scientific claims, medical information, research findings, technical mechanisms, statistical data, or anything where primary literature would be more reliable than secondary reporting.

Parsing results

Parse the JSON output. Each result includes:

title, url, published_date, author
score — Exa's relevance score for the query
text (if --text), highlights + highlight_scores (if --highlights)

Snippet fallback — any combination of content fields may be present. Cascade through them: prefer highlights (tight, pre-selected passages), fall back to a truncated slice of text. Never assume exactly one is present.

Response format

CRITICAL: Every claim must have an inline citation. Use markdown links pulling only from the JSON output. Never invent or guess URLs.

For academic sources, use author-year citation style where metadata is available:

Academic: Smith et al., 2025 or Smith & Jones, 2024
Non-academic: Source Title

Synthesize a response that:

Leads with findings from peer-reviewed or preprint sources when available
Clearly distinguishes between claims backed by primary research vs. secondary reporting
Includes specific facts, names, numbers, dates
Cites every fact inline — do not leave any claim uncited
Organizes by theme if multiple topics
Notes the evidence quality (e.g., "a randomized controlled trial found..." vs. "a blog post reports...")

End with a Sources section listing every URL referenced, grouped by type:

Sources:

Academic / Peer-reviewed:
- [Smith et al., 2025 — Title of Paper](https://doi.org/...) (Nature, 2025)
- [Jones & Lee, 2024 — Title of Paper](https://arxiv.org/...) (arXiv preprint)

Other:
- [Source Title](https://example.com/article) (Feb 2026)

This Sources section is mandatory. Do not omit it. If no academic sources were found, note that and explain why (e.g., the topic is too recent, not yet studied, or inherently non-academic).

After the Sources section, mention the output file path ($FILENAME.json) so the user knows it's available for follow-up questions.

Back to K-Dense-AI/scientific-agent-skills (AI Scientist skills) or Agent skills.

exa-search skill (K-Dense scientific-agent-skills)

Install

SKILL.md (verbatim)

Exa Web Toolkit

Routing — pick the right capability

Decision guide

Academic source priority

Setup

Authentication

Tracking header

Files in this skill

Other files in this skill

references/web-extract.md (verbatim)

URL Extraction

Command

Academic content handling

Response format

references/web-search.md (verbatim)

Web Search

Command

Academic source strategy

1. Use --category "research paper"

2. Restrict to scholarly domains

Two-pass pattern for comprehensive coverage

Parsing results

Response format

1. Use `--category "research paper"`