Claude-code-minoan exa-search

Search the web with Exa AI — neural search, content extraction, similar page discovery, quick research with citations, and async pro research with structured output. Covers all Exa endpoints (/search, /contents, /findSimilar, /answer, /research/v1). Triggers on web search, find pages, similar pages, research with citations, Exa, neural search.

install

source · Clone the upstream repo

git clone https://github.com/tdimino/claude-code-minoan

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/tdimino/claude-code-minoan "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/research/exa-search" ~/.claude/skills/tdimino-claude-code-minoan-exa-search && rm -rf "$T"

manifest: skills/research/exa-search/SKILL.md

source content

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