exa-search

Name: exa-search
Author: tdimino

github.com/tdiminoProductivity4149 installsUpdated 1w ago

About exa-search

A search skill for users of the Exa AI search API, with scripts for web search, content extraction, similar-page discovery, and research. Filtering and structured results support information retrieval and research workflows.

Key features

01Neural web search
02URL content extraction and summaries
03Similar-page discovery
04AI research with citations
05Async pro research and structured output

Use cases

01Filter web results by domain and date
02Extract selected pages with bounded content
03Find research papers and related pages
04Build cited research or RAG context

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/tdimino/claude-code-minoan --skill exa-search

After installing, type/exa-searchin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

codex-agent-guide.md

README.md

references/

exa-scripts-reference.md

scripts/

exa_contents.py

exa_research_async.py

exa_research.py

exa_search.py

exa_similar.py

test_exa.py

Details

From the project README

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, 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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Details

Author: tdimino
Repository: tdimino/claude-code-minoan
Category: Productivity
Installs: 49
GitHub stars: 41

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