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:

bash

pip install exa-py

Set your API key:

bash

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

bash

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:

bash

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

With filters:

bash

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

Find similar pages:

bash

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

Get content for known URLs:

bash

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

Step 4: Present Results

Format results as a structured table:

| # | Title | Authors | Venue/Publisher | URL | Date | Key Content |
|---|-------|---------|-----------------|-----|------|-------------|

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

exa-search

NPX Install

Tags

SKILL.md Content

Exa AI-Powered Web Search

Role & Positioning

Constants

Setup

Workflow

Step 1: Parse Arguments

Step 2: Locate Script

Step 3: Execute Search

Step 4: Present Results

Step 5: Offer Follow-up

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

Key Rules