tomevault-io Exa Search Skill
5 specialized scripts for Exa AI search API—neural search, content extraction, similar pages, research with citations, and async pro research.
Prerequisite: EXA_API_KEY environment variable. Get key at https://dashboard.exa.ai
Token-Efficient Search
Inspired by Anthropic's dynamic filtering—always filter before reasoning. ~24% fewer tokens, ~11% better accuracy.
The Principle: Search Cheaply → Filter → Extract Selectively → Reason
DO:
# Step 1: Search with --no-text (titles/URLs only — cheapest)
python3 ~/.claude/skills/exa-search/scripts/exa_search.py "query" -n 20 --no-text
# Step 2: Evaluate titles, pick best 3-5 URLs
# Step 3: Extract only those URLs with bounded content
python3 ~/.claude/skills/exa-search/scripts/exa_contents.py URL1 URL2 --highlights --max-chars 3000
DON'T: Search with full text for 50 results, then reason over all of it.
Use API-Level Filters First (Free Filtering)
These reduce results at the API level before you ever see them:
--must-include "term"— results must contain this string--must-exclude "term"— removes irrelevant results--domains site1.com site2.com— restrict to authoritative sources--category "research paper"— eliminate irrelevant content types--after 2025-01-01/--before— temporal filtering
Use Summaries Over Full Text
When you need the gist, not raw content:
# AI-distilled summaries — much smaller than full text
python3 ~/.claude/skills/exa-search/scripts/exa_search.py "query" --summary "Key findings" -n 5
Use Bounded Context for RAG
# Capped context string — prevents unbounded token usage
python3 ~/.claude/skills/exa-search/scripts/exa_search.py "query" --context --context-chars 5000
Post-Process with filter_web_results.py
Pipe Exa JSON output through the Firecrawl filter script for additional reduction:
python3 ~/.claude/skills/exa-search/scripts/exa_search.py "query" --json | \
python3 ~/.claude/skills/firecrawl/scripts/filter_web_results.py \
--fields "title,url,text" --max-chars 3000
Cost Tiers — Match to Task
| Type | Latency | Cost/1k | When |
|---|---|---|---|
--instant |
<150ms | Cheapest | Real-time lookups, autocomplete |
--fast |
~500ms | Low | Quick checks, confirmations |
auto (default) |
-- | Medium | General search |
--deep |
4-12s | $12 | Comprehensive research |
--deep-reasoning |
12-50s | $15 | Maximum depth + synthesis |
Structured Deep Search (Exa Deep)
Deep and deep-reasoning searches support structured JSON output via outputSchema. The API returns parsed content in output.content with per-field grounding citations and confidence scores.
| Quick Example | Purpose |
|---|---|
... --deep --text-output "Short answer" |
Simple text answer |
... --deep-reasoning --schema-preset company |
Structured company research |
... --deep --output-schema '{"type":"object","properties":{"answer":{"type":"string"}}}' |
Custom schema |
... --deep --schema-file ~/schemas/analysis.json |
Schema from file |
Presets: company, paper-survey, competitor-analysis, person, news-digest
Output includes field-level grounding: per-field citations with [H]igh/[M]edium/[L]ow confidence.
Available Scripts
1. exa_search.py — Neural Web Search
python3 ~/.claude/skills/exa-search/scripts/exa_search.py "query" [options]
| Quick Example | Purpose |
|---|---|
... exa_search.py "AI frameworks" |
Basic search |
... exa_search.py "transformers" --category "research paper" -n 20 |
Academic papers |
... exa_search.py "query" --deep --additional-queries "alt query" |
Deep search |
... exa_search.py "query" --domains docs.python.org |
Domain-filtered |
... exa_search.py "query" --after 2025-01-01 --category news |
Recent news |
... exa_search.py "query" --context --context-chars 10000 |
RAG context |
... exa_search.py "query" --instant -n 5 |
Sub-150ms lookup |
... exa_search.py "Top AI startups" --deep-reasoning --schema-preset company |
Structured company research |
... exa_search.py "Who is CEO of Stripe?" --deep --text-output "Short answer" |
Quick factual answer |
Categories: company, research paper, news, pdf, github, tweet, personal site, people, financial report
2. exa_contents.py — URL Content Extraction
python3 ~/.claude/skills/exa-search/scripts/exa_contents.py URL [URL2...] [options]
| Quick Example | Purpose |
|---|---|
... exa_contents.py "https://arxiv.org/abs/2307.06435" |
Extract paper |
... exa_contents.py URL --summary "Key methods" --highlights |
Summarized extraction |
... exa_contents.py URL --livecrawl always |
Fresh content |
... exa_contents.py URL --max-chars 5000 |
Bounded extraction |
3. exa_similar.py — Find Similar Pages
python3 ~/.claude/skills/exa-search/scripts/exa_similar.py URL [options]
| Quick Example | Purpose |
|---|---|
... exa_similar.py "https://stripe.com" --category company --exclude-source |
Find competitors |
... exa_similar.py "https://arxiv.org/abs/..." -n 15 |
Related papers |
... exa_similar.py URL --summary "How different?" |
Comparison summaries |
4. exa_research.py — AI-Powered Research
python3 ~/.claude/skills/exa-search/scripts/exa_research.py "question" [options]
| Quick Example | Purpose |
|---|---|
... exa_research.py "React vs Vue differences?" --sources |
Research with citations |
... exa_research.py "query" --stream |
Real-time streaming |
... exa_research.py "query" --domains docs.python.org |
Authoritative sources |
... exa_research.py "query" --markdown |
Markdown with citations |
... exa_research.py "query" --answer-only |
Pipe-friendly output |
5. exa_research_async.py — Async Pro Research
python3 ~/.claude/skills/exa-search/scripts/exa_research_async.py "question" [options]
| Quick Example | Purpose |
|---|---|
... exa_research_async.py "Compare AI frameworks" --pro --wait |
Pro model |
... exa_research_async.py "Quick overview" --fast |
Fast model |
... exa_research_async.py "query" --schema '{...}' |
Structured output |
... exa_research_async.py status r_abc123 |
Check job |
... exa_research_async.py list |
List jobs |
Script Selection Guide
| Task | Best Script |
|---|---|
| Web search with filters | exa_search.py |
| Research papers | exa_search.py --category "research paper" |
| Company/startup info | exa_search.py --category company |
| GitHub repos/code | exa_search.py --category github |
| Extract known URL content | exa_contents.py |
| Find competitors | exa_similar.py --exclude-source |
| Quick answers with citations | exa_research.py --sources |
| Complex structured research | exa_research_async.py --pro |
| Real-time search | exa_search.py --instant |
| RAG context building | exa_search.py --context |
| Structured research with grounding | exa_search.py --deep-reasoning --schema-preset company |
| Quick factual answer | exa_search.py --deep --text-output "Short answer" |
Exa vs Firecrawl vs Native Claude Tools
| Need | Best Tool | Why |
|---|---|---|
| Semantic/neural search | Exa exa_search.py |
AI-powered relevance |
| Find research papers | Exa --category "research paper" |
Academic index |
| Quick research answer | Exa exa_research.py |
Citations + synthesis |
| Find similar pages | Exa exa_similar.py |
Semantic similarity |
| Single page → markdown | Firecrawl scrape --only-main-content |
Cleanest output |
| Crawl entire site | Firecrawl crawl --wait --progress |
Link following |
| Autonomous data finding | Firecrawl agent |
No URLs needed |
| Search + scrape combined | Firecrawl search --scrape |
One operation |
| Claude API agent building | Native web_search_20260209 |
Built-in dynamic filtering |
| Twitter/X content | jina URL |
Only tool that works |
Common Workflows
Research a Topic
python3 ~/.claude/skills/exa-search/scripts/exa_research.py "How does RAG work?" --sources --markdown
Literature Review
# Find papers, then find similar to best hit
python3 ~/.claude/skills/exa-search/scripts/exa_search.py "transformer optimization" --category "research paper" -n 20 --summary "Key contributions"
python3 ~/.claude/skills/exa-search/scripts/exa_similar.py "https://arxiv.org/abs/1706.03762" --category "research paper" -n 15
Documentation Research
python3 ~/.claude/skills/exa-search/scripts/exa_search.py "React useEffect cleanup" --domains react.dev developer.mozilla.org --context
Build RAG Context
python3 ~/.claude/skills/exa-search/scripts/exa_search.py "Python async patterns" --context --context-chars 15000 --domains docs.python.org
Reference Documentation
| File | Contents |
|---|---|
references/exa-scripts-reference.md |
Full parameter reference for all 5 scripts, cost table, MCP comparison, test suite |
Test Suite
python3 ~/.claude/skills/exa-search/scripts/test_exa.py --quick # Quick validation
python3 ~/.claude/skills/exa-search/scripts/test_exa.py # Full suite
python3 ~/.claude/skills/exa-search/scripts/test_exa.py --endpoint search # Specific endpoint
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