Exa Deep Search logo

Exa Deep Search

Organization
sandbaseai
exa-deep-search

Search, extract, and compare high-quality public sources with Exa through SandBase. Use when asked for deep web research, source discovery, current evidence, topic investigation, company research, or citation-ready findings.

Overview

Publishersandbaseai
Repositorysandbase-skills
Skill nameexa-deep-search
Stars
200
Forks
21
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Exa Deep 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.
    https://github.com/sandbaseai/sandbase-skills/tree/main/marketing/exa-deep-search
  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/sandbaseai/sandbase-skills.git /tmp/sandbase-skills
mkdir -p .claude/skills
cp -r /tmp/sandbase-skills/marketing/exa-deep-search .claude/skills/exa-deep-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Exa Deep 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 Deep 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 Deep 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 Deep Search

Turn Exa search into a focused, source-backed research brief. This Skill calls the named Exa capabilities in the SandBase API map through the SandBase MCP gateway. Use an authorized SandBase MCP connection and the discover → inspect → run workflow below; never request, print, or store an API key in the research output.

Read example workflows when the user needs a starting prompt or wants to understand the output.

Operating principles

  • Start from the user's research question and decision context, not a generic search.
  • Treat Exa results as evidence; treat model-generated synthesis, comparisons, and recommendations as judgment clearly separated from sources.
  • Select search depth, time window, domains, and geography deliberately. State any assumption rather than silently defaulting.
  • Optimize for source quality, recency, and relevance — not quantity.
  • Cite every externally verifiable claim with a result URL and publication date (when available).
  • Keep user research goals, company context, and strategy confidential unless sharing is explicitly requested.

Workflow

1. Frame the research question

Collect or infer: the topic or entity, time window, geography, trusted or excluded domains, audience for the deliverable, and how findings will be used. Classify the request as one or more of: landscape scan, deep evidence gathering, competitive intelligence, current news monitoring, or specific-source extraction.

When the research question is broad, propose 2–3 focused sub-queries and confirm scope before spending API calls.

2. Select and call SandBase capabilities

Read the SandBase API map before selecting tools. Treat each listed tool_name as a capability identifier to resolve through the SandBase gateway:

  1. Use sandbase_discover with the provider and capability to find the current endpoint name.
  2. Pass the returned name to sandbase_inspect; read inputSchema, pricing, and execute_as.
  3. Follow execute_as to call sandbase_run using execute_as.arguments.name and schema-defined arguments. If it returns a run_id, poll sandbase_run_get within the task budget until completed or failed; report pending or failed runs without automatically resubmitting them.
  4. Keep the returned endpoint name, query, search parameters, and result metadata with the returned data.

3. Search with Exa

Resolve exa_search with sandbase_discover, then map these research needs to the current inputSchema from sandbase_inspect. Use the discovered endpoint name and its execute_as template for execution:

Research needSearch intent
Current landscapeNews results within a bounded publication window, with relevant highlights
Deep evidenceA supported deep search mode with summaries; request full text only for selected sources
Trusted sources onlyRestrict results to first-party, academic, or approved publisher domains
Competitive researchExclude the target's own domain; use separate queries per competitor
Validation or quick checkA supported fast search mode with 3–5 results

Tips:

  • Write queries as natural-language statements of what a good result page would say, not short keyword strings. Exa responds best to semantic queries.
  • Use the inspected schema’s supported categories to narrow result types.
  • Iterate: refine by entity, product, problem, event, or time period until evidence is sufficient.
  • Request relevant highlights using the inspected schema’s content options, without extracting full text for every result.

4. Extract selected sources

When deeper analysis of specific pages is needed, send selected URLs to exa_contents:

  • Request full page content when analyzing structure or extracting data.
  • Request focused highlights when the inspected extraction schema supports them.
  • Request concise summaries when reviewing many pages, if supported.
  • Include subpages only for explicit documentation, pricing, or API crawl tasks and only when supported.
  • Request a live crawl only when freshness requires it and the inspected schema supports it.

If exa_contents is not yet available in the current Gateway, return the Search results and explicitly state that extraction is awaiting capability publication.

5. Synthesize findings

  • Separate direct observations from interpretation.
  • Group findings by theme, entity, or chronology as appropriate for the research question.
  • Note disagreements between sources and evidence gaps.
  • Propose follow-up queries for unresolved questions.

Query crafting tips

Good Exa queries describe the content of the ideal result page:

Poor queryBetter query
AI agentsHow enterprises evaluate AI agent platforms for production deployment
observability toolsComparison of AI agent observability and tracing solutions 2025
competitor pricingPricing page for enterprise AI agent orchestration platform
  • Add temporal context: "in 2025", "since January", "latest announcement".
  • Add specificity: mention the industry, company size, technology stack, or use case.
  • Use the inspected schema’s domain exclusion option to avoid results you already know about.

Output

Return a structured research brief:

Source map

#TitleURLPublishedRelevance
1............

Key findings

Numbered findings, each citing source(s) by number.

Disagreements and evidence gaps

What sources disagree on, and what questions remain unanswered.

Suggested next queries

Follow-up Exa queries or alternative research paths.

Evidence rules

  • Cite a result URL for every externally verifiable claim.
  • Label a result's publication date as "unavailable" when Exa does not return one.
  • Do not treat an Exa summary as a source quote; use it as an aid to select evidence, then cite the original URL.
  • Do not call Exa Answer or Exa Agent endpoints. The user's Agent/LLM synthesizes the evidence.
  • Do not copy long source passages; paraphrase and cite.
  • Mark clearly when a finding is inferred from multiple sources vs. directly stated in one.

Failure handling

  • If SandBase is unavailable or unauthorized, report the failed capability and ask the user to connect or authorize SandBase; do not silently substitute a direct provider API.
  • If exa_search returns few or no results, try: broader query, a different supported search mode, removed domain filters, or a wider date range. Report if the topic genuinely lacks public coverage.
  • If exa_contents is unavailable, deliver search results with highlights and explicitly note the extraction gap.
  • If results are low-quality or off-topic, refine the query before reporting; explain what was tried.

Example tasks

  • "Find the last 30 days of reliable sources about AI agent observability. Give me a five-source brief with gaps."
  • "Research how enterprise teams evaluate AI agents. Prefer company and academic sources; exclude vendor blogs."
  • "Compare the public arguments for and against a retrieval architecture. Use advanced search and cite each source."
  • "Find recent funding announcements in the AI developer tools space. Only include sources from the last 7 days."
  • "Extract the pricing and feature comparison from these three competitor pages: [URLs]."

Quality gate

Before delivering, verify that:

  • Every finding cites at least one source URL.
  • Observations are separated from model-generated interpretations.
  • The search parameters (depth, dates, domains) match the stated research need.
  • Evidence gaps and low-confidence findings are explicitly labeled.
  • The deliverable format matches what the user requested.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Exa Deep Search AI skill do?

Search, extract, and compare high-quality public sources with Exa through SandBase. Use when asked for deep web research, source discovery, current evidence, topic investigation, company research, or citation-ready findings.

Why use Exa Deep Search on TypingMind?

Because you install it once and use it with any model. Exa Deep 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 Deep Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sandbaseai/sandbase-skills/tree/main/marketing/exa-deep-search. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Exa Deep 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 Deep Search?

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

Is the Exa Deep Search AI skill free?

Yes. It is published on GitHub by sandbaseai under the Apache-2.0 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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