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Exa Search

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wanshuiyin
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).

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill nameexa-search
Stars
16.3K
Forks
1.4K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by wanshuiyin on GitHub. Read the source before you install it.

Installation

Install the Exa Search AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/exa-search .claude/skills/exa-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Exa Search in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Exa Search on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Exa Search is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Exa AI-Powered Web Search

Search query: $ARGUMENTS

Role & Positioning

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

SkillBest for
/arxivDirect preprint search and PDF download
/semantic-scholarPublished venue papers (IEEE, ACM, Springer), citation counts
/deepxivLayered reading: search, brief, section map, section reads
/exa-searchBroad 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

  • EXA_FETCHER — canonical name exa_search.py, resolved per shared-references/integration-contract.md §2 (Policy D1 — standalone /exa-search has no documented fallback, so unresolved helper terminates with an explicit error).
  • 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

Resolve $EXA_FETCHER via the canonical strict-safe chain (see shared-references/integration-contract.md §2). Policy D1 cascade: there is no native inline fallback for Exa (retrieval requires the exa-py SDK + API key, which lives in the fetcher), so unresolved helper means the SKILL cannot produce its primary output — fail with explicit remediation.

bash
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true
fi
if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
    ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
fi
EXA_FETCHER=".aris/tools/exa_search.py"
[ -f "$EXA_FETCHER" ] || EXA_FETCHER="tools/exa_search.py"
[ -f "$EXA_FETCHER" ] || { [ -n "${ARIS_REPO:-}" ] && EXA_FETCHER="$ARIS_REPO/tools/exa_search.py"; }
[ -f "$EXA_FETCHER" ] || {
  echo "ERROR: exa_search.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2
  echo "       Fix: rerun bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or copy the helper to tools/." >&2
  echo "       Also ensure 'exa-py' is installed: pip install exa-py" >&2
  exit 1
}

Step 3: Execute Search

Standard search:

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

With filters:

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

Find similar pages:

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

Get content for known URLs:

bash
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:

  • 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 (Variant B — warn-and-skip). 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:

bash
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 $2; exit}' .aris/installed-skills.txt 2>/dev/null)}"
    if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
      ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
    fi
    WIKI_SCRIPT=".aris/tools/research_wiki.py"
    [ -f "$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 or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or cp <ARIS-repo>/tools/research_wiki.py tools/." >&2
      WIKI_SCRIPT=""
    }
    [ -n "$WIKI_SCRIPT" ] && for each research-paper hit in results:
        if URL matches arxiv.org/abs/<id>:
            python3 "$WIKI_SCRIPT" ingest_paper research-wiki/ \
                --arxiv-id "<id>"
        else:
            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/<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

Frequently asked questions

What does the Exa Search AI skill do?

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).

Why use Exa Search on TypingMind?

Because you install it once and use it with any model. Exa Search is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Exa Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/exa-search. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Exa Search?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Exa Search?

As many as you like. As long as a model supports skills, you can use Exa Search with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Exa Search AI skill free?

Yes. It is published on GitHub by wanshuiyin under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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