Auto-claude-code-research-in-sleep exa-search

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).

install

source · Clone the upstream repo

git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/exa-search" ~/.claude/skills/wanshuiyin-auto-claude-code-research-in-sleep-exa-search && rm -rf "$T"

manifest: skills/exa-search/SKILL.md

source content

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 structured table:

| # | Title | 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

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"

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