Exa Search

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 | 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.

When the predicates hold, resolve $WIKI_SCRIPT per the canonical chain at [shared-references/wiki-helper-resolution.md](../shared-references/wiki-helper-resolution.md) (Variant B — warn-and-skip). For each research paper hit, try to recover an arXiv ID from the URL (arxiv.org/abs/); if present, use --arxiv-id. Otherwise fall back to manual metadata:

if [ -d research-wiki/ ] and query category was "research paper":
    cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
    ARIS_REPO="${ARIS_REPO:-$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null)}"
    WIKI_SCRIPT=".aris/tools/research_wiki.py"
    [ -f "$WIKI_SCRIPT" ] || WIKI_SCRIPT="tools/research_wiki.py"
    [ -f "$WIKI_SCRIPT" ] || { [ -n "${ARIS_REPO:-}" ] && WIKI_SCRIPT="$ARIS_REPO/tools/research_wiki.py"; }
    [ -f "$WIKI_SCRIPT" ] || {
      echo "WARN: research_wiki.py not found; exa-search results delivered, wiki ingest skipped. Fix: bash tools/install_aris.sh, export ARIS_REPO, or cp /tools/research_wiki.py tools/." >&2
      WIKI_SCRIPT=""
    }
    [ -n "$WIKI_SCRIPT" ] && for each research-paper hit in results:
        if URL matches arxiv.org/abs/:
            python3 "$WIKI_SCRIPT" ingest_paper research-wiki/ \
                --arxiv-id ""
        else:
            python3 "$WIKI_SCRIPT" ingest_paper research-wiki/ \
                --title "" --authors "" \
                --year  --venue ""

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: raja21068
• Source: raja21068/AutoResearch
• License: MIT

Install and usage instructions live in the source repository linked above.