exa-search

name	exa-search
description	AI-powered web search via Exa with content extraction. Use when user says "exa search", "web search with content", "find similar pages", or needs broad web results beyond academic databases (arXiv, Semantic Scholar).
argument-hint	[search-query-or-url]
allowed-tools	Bash(*), Read, Write
metadata	{"version":"0.1.0","last_updated":"2026-05-31","summary":"AI-powered web search via Exa with content extraction."}

Exa AI-Powered Web Search

Search query: $ARGUMENTS

Role & Positioning

Exa is the broad web search source with built-in content extraction:

Skill	Best for
/arxiv	Direct preprint search and PDF download
/semantic-scholar	Published venue papers (IEEE, ACM, Springer), citation counts
/deepxiv	Layered reading: search, brief, section map, section reads
/exa-search	Broad web search: blogs, docs, news, companies, research papers — with content extraction

Use Exa when you need results beyond academic databases, or when you want content (highlights, full text, summaries) extracted alongside search results.

Constants

• FETCH_SCRIPT — tools/exa_search.py relative to the current project.
• MAX_RESULTS = 10 — Default number of results to return.

Overrides (append to arguments):

• /exa-search "RAG pipelines" — max: 5 — top 5 results
• /exa-search "diffusion models" — category: research paper — research papers only
• /exa-search "startup funding" — category: news, start date: 2025-01-01 — recent news
• /exa-search "transformer" — content: text, max chars: 8000 — full text mode
• /exa-search "transformer" — content: summary — LLM-generated summaries
• /exa-search "transformer" — domains: arxiv.org,huggingface.co — domain filter
• /exa-search "https://arxiv.org/abs/2301.07041" — similar — find similar pages

Setup

Exa requires the exa-py SDK and an API key:

pip install exa-py

Set your API key:

export EXA_API_KEY=your-key-here

Get a key from exa.ai.

Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS for:

• query: The search query (required) or a URL (for find-similar mode)
• similar: If present, use find-similar mode instead of search
• max: Override MAX_RESULTS
• category: research paper, news, company, personal site, financial report, people
• content: highlights (default), text, summary, none
• max chars: Max characters for content extraction
• type: Search type — auto (default), neural, fast, instant
• domains: Comma-separated include domains
• exclude domains: Comma-separated exclude domains
• include text: Phrase that must appear in results
• exclude text: Phrase to exclude from results
• start date: ISO 8601 date — only results after this
• end date: ISO 8601 date — only results before this
• location: Two-letter ISO country code

Step 2: Locate Script

SCRIPT=$(find tools/ -name "exa_search.py" 2>/dev/null | head -1)

If not found, tell the user:

exa_search.py not found. Make sure tools/exa_search.py exists and exa-py is installed:
pip install exa-py

Step 3: Execute Search

Standard search:

python3 "$SCRIPT" search "QUERY" --max 10 --content highlights

With filters:

python3 "$SCRIPT" search "QUERY" --max 10 \
  --category "research paper" \
  --start-date 2025-01-01 \
  --content text --max-chars 8000

Find similar pages:

python3 "$SCRIPT" find-similar "URL" --max 5 --content highlights

Get content for known URLs:

python3 "$SCRIPT" get-contents "URL1" "URL2" --content text

Step 4: Present Results

Format results as a numbered list, ONE RESULT PER ENTRY (never a table — result tables are unreadable):

1. <Authors> (<Date>). <Title>.
   <Venue/Publisher> · <URL>
   <one-line key content>
2. ...

For each result:

• Show title and URL
• Show published date if available
• Show highlights, text excerpt, or summary depending on content mode
• Flag particularly relevant results
• For category: "research paper" hits only — also record authors (from Exa's author/authors fields, or fallback: parse from the result snippet) and venue/publisher (from publisher, source, or the domain hosting the paper). These are needed by Step 6's wiki hook; if either is unavailable for a given hit, skip wiki ingest for that one hit and log a note.

Step 5: Offer Follow-up

After presenting results, suggest:

• Deepen: "I can fetch full text for any of these results"
• Find similar: "I can find pages similar to any result"
• Narrow: "I can re-search with domain/date/text filters"

Step 6: Update Research Wiki (if active, research-paper results only)

Required when research-wiki/ exists AND the search returned results of category: "research paper"; skip silently otherwise. General web results (blog posts, docs, news) are not ingested — the wiki is for papers only.

For each research paper hit, try to recover an arXiv ID from the URL (arxiv.org/abs/<id>); if present, use --arxiv-id. Otherwise fall back to manual metadata:

if [ -d research-wiki/ ] and query category was "research paper":
    for each research-paper hit in results:
        if URL matches arxiv.org/abs/<id>:
            python3 tools/research_wiki.py ingest_paper research-wiki/ \
                --arxiv-id "<id>"
        else:
            python3 tools/research_wiki.py ingest_paper research-wiki/ \
                --title "<title>" --authors "<authors joined by , >" \
                --year <year> --venue "<venue or publisher>"

The helper handles slug / dedup / page / index / log — do not handwrite papers/<slug>.md. See shared-references/integration-contract.md.

Key Rules

• Always check that EXA_API_KEY is set before searching
• Default to highlights content mode for a good balance of speed and context
• Use category: "research paper" when the user is clearly looking for academic content
• Use text content mode when the user needs full page content
• Combine with /arxiv or /semantic-scholar for comprehensive literature coverage