exa-search skill

Exa AI-Powered Web Search

Search query: $ARGUMENTS

Role & Positioning

Exa is the broad web search source with built-in 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

shared-references/integration-contract.md §2 (Codex-side chain: $ARIS_REPO/tools/ → tools/ → ~/.codex/skills/exa-search/). Policy D1 — standalone /exa-search has no documented fallback, so unresolved helper terminates with an explicit error.

• EXAFETCHER — canonical name exasearch.py, resolved per

• MAXRESULTS = 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

Step 2: Locate Script

# Resolve $EXA_FETCHER via the canonical strict-safe Codex chain.
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
EXA_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/exa_search.py" ] && EXA_FETCHER="$ARIS_REPO/tools/exa_search.py"
[ -z "$EXA_FETCHER" ] && [ -f tools/exa_search.py ] && EXA_FETCHER="tools/exa_search.py"
[ -z "$EXA_FETCHER" ] && [ -f ~/.codex/skills/exa-search/exa_search.py ] && EXA_FETCHER="$HOME/.codex/skills/exa-search/exa_search.py"
[ -z "$EXA_FETCHER" ] && {
  echo "ERROR: exa_search.py not resolved at \$ARIS_REPO/tools/, tools/, or ~/.codex/skills/exa-search/." >&2
  echo "       Fix: rerun tools/install_aris_codex.sh, export ARIS_REPO, or copy the helper to ~/.codex/skills/exa-search/." >&2
  echo "       Also ensure 'exa-py' is installed: pip install exa-py" >&2
  exit 1
}

If not found, tell the user:

exa_search.py not found. Run install_aris_codex.sh, set ARIS_REPO to your ARIS repo root, or install/copy the helper into the project/global Codex skill path; then install exa-py:
pip install exa-py

Step 3: Execute Search

Standard search:

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

With filters:

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

Find similar pages:

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

Get content for known URLs:

python3 "$EXA_FETCHER" 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:

(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.

• 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

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/); if present, use --arxiv-id. Otherwise fall back to manual metadata:

if [ -d research-wiki/ ] and query category was "research paper":
    WIKI_SCRIPT=""
    [ -n "$ARIS_REPO" ] && [ -f "$ARIS_REPO/tools/research_wiki.py" ] && WIKI_SCRIPT="$ARIS_REPO/tools/research_wiki.py"
    [ -z "$WIKI_SCRIPT" ] && [ -f tools/research_wiki.py ] && WIKI_SCRIPT="tools/research_wiki.py"
    [ -z "$WIKI_SCRIPT" ] && [ -f ~/.codex/skills/research-wiki/research_wiki.py ] && WIKI_SCRIPT="$HOME/.codex/skills/research-wiki/research_wiki.py"
    for each research-paper hit in results:
        if URL matches arxiv.org/abs/<id>:
            [ -n "$WIKI_SCRIPT" ] && python3 "$WIKI_SCRIPT" ingest_paper research-wiki/ \
                --arxiv-id "<id>"
        else:
            [ -n "$WIKI_SCRIPT" ] && python3 "$WIKI_SCRIPT" 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/.md. See shared-references/integration-contract.md.

Key Rules

• Always check that EXAAPIKEY 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