Exa Search

Name: Exa Search
Author: mhagrelius

mhagrelius/dotfiles

24 installs
2 repo stars
Updated August 1, 2026
mhagrelius/dotfiles

Helps with ai & agent building tasks.

About

exa-search is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

exa-search
AI & Agent Building
AI-coding skill

Exa Search by the numbers

24 all-time installs (skills.sh)
Ranked #9,912 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
Data as of Aug 4, 2026 (Skillselion catalog sync)

npx skills add https://github.com/mhagrelius/dotfiles --skill exa-search

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

[![Listed on Skillselion](https://skillselion.com/badge/skills/mhagrelius/dotfiles/exa-search.svg)](https://skillselion.com/skills/mhagrelius/dotfiles/exa-search)

Installs	24
repo stars	★ 2
Last updated	August 1, 2026
Repository	mhagrelius/dotfiles ↗

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Exa Semantic Search

Overview

Exa.ai provides neural semantic search optimized for AI consumption. Use when meaning matters more than keywords.

Decision Flowchart

digraph exa_decision {
    rankdir=TB;
    node [shape=box];

    start [label="Need to search the web?" shape=diamond];
    known_url [label="Do you have\na specific URL?" shape=diamond];
    semantic [label="Is this semantic/conceptual?\n(meaning > keywords)" shape=diamond];
    recent [label="Need very recent\nnews/events?" shape=diamond];
    code [label="Is this a coding/\nAPI question?" shape=diamond];

    webfetch [label="WebFetch" shape=box style=filled fillcolor=lightblue];
    websearch [label="WebSearch" shape=box style=filled fillcolor=lightgreen];
    exa_web [label="mcp__exa__web_search_exa" shape=box style=filled fillcolor=lightyellow];
    exa_code [label="mcp__exa__get_code_context_exa" shape=box style=filled fillcolor=lightyellow];

    start -> known_url [label="yes"];
    start -> known_url [label="no" style=invis];
    known_url -> webfetch [label="yes"];
    known_url -> semantic [label="no"];
    semantic -> code [label="yes"];
    semantic -> recent [label="no"];
    code -> exa_code [label="yes"];
    code -> exa_web [label="no"];
    recent -> websearch [label="yes"];
    recent -> websearch [label="no"];
}

Quick Reference

Scenario	Tool	Why
"Find papers on emergent AI behavior"	`mcp__exa__web_search_exa`	Semantic discovery
"Companies similar to Anthropic"	`mcp__exa__web_search_exa`	Similar content
"How to use React hooks"	`mcp__exa__get_code_context_exa`	Coding context
"Latest news on X"	`WebSearch`	Recency matters
"Read this URL: [link]"	`WebFetch`	Known URL
"error: module not found XYZ"	`WebSearch`	Exact keyword match
"CVE-2024-12345"	`WebSearch`	Specific identifier

Tool Usage

mcpexaweb_search_exa

query: "semantic query describing concepts"
numResults: 8 (default, adjust as needed)
type: "auto" | "fast" | "deep"

mcpexaget_code_context_exa

query: "React useState hook examples" | "Express middleware patterns"
tokensNum: 5000 (default, 1000-50000 range)

Integration Patterns

Discovery + Extraction: 1. Exa finds relevant sources semantically 2. WebFetch extracts full content from best URLs

Multi-perspective research: 1. Exa: "academic perspectives on X" 2. Exa: "industry implementation of X" 3. Exa: "critiques of X" 4. Synthesize

Fallback: 1. Try Exa for semantic search 2. If results poor, fall back to WebSearch with keywords

Anti-Patterns

Don't	Do Instead
`"python pandas filter dataframe"`	Use WebSearch (keyword query)
Run 10 similar queries	Consolidate into 2-3 well-crafted queries
`"what is React"`	Use knowledge or WebSearch
`"breaking news today"`	Use WebSearch

When Results Are Poor

1. Switch search type: auto vs fast vs deep 2. Rephrase: more semantic/descriptive 3. Add domain filters via allowed_domains 4. Fall back to WebSearch for keyword matching