Brave Search
badlogic/pi-skills
Web search and content extraction via Brave Search API. An agent skill from badlogic/pi-skills.
A skill your agent uses when researching products, finding academic papers, discovering competitors, reading webpage content, or getting cited answers grounded in real web sources.
Install Exa Research
$ npx skills add BlockRunAI/blockrun-mcp --skill exa-research -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install BlockRunAI/blockrun-mcp exa-research --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/BlockRunAI/blockrun-mcp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/exa-research .claude/skills/exa-research && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "exa-research" agent skill from https://github.com/BlockRunAI/blockrun-mcp/tree/main/skills/exa-research into .claude/skills/exa-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exa-research", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/BlockRunAI/blockrun-mcp/tree/main/skills/exa-researchType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add BlockRunAI/blockrun-mcp --skill exa-research -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install BlockRunAI/blockrun-mcp exa-research --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/BlockRunAI/blockrun-mcp.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/exa-research .agents/skills/exa-research && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "exa-research" agent skill from https://github.com/BlockRunAI/blockrun-mcp/tree/main/skills/exa-research into .agents/skills/exa-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exa-research", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add BlockRunAI/blockrun-mcp --skill exa-research -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install BlockRunAI/blockrun-mcp exa-research --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/BlockRunAI/blockrun-mcp.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/exa-research .cursor/skills/exa-research && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "exa-research" agent skill from https://github.com/BlockRunAI/blockrun-mcp/tree/main/skills/exa-research into .cursor/skills/exa-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exa-research", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/BlockRunAI/blockrun-mcp.git --path skills/exa-research--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add BlockRunAI/blockrun-mcp --skill exa-research -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install BlockRunAI/blockrun-mcp exa-research --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/BlockRunAI/blockrun-mcp.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/exa-research .gemini/skills/exa-research && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "exa-research" agent skill from https://github.com/BlockRunAI/blockrun-mcp/tree/main/skills/exa-research into .gemini/skills/exa-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exa-research", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install BlockRunAI/blockrun-mcp exa-researchInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add BlockRunAI/blockrun-mcp --skill exa-research -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/BlockRunAI/blockrun-mcp.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/exa-research .github/skills/exa-research && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "exa-research" agent skill from https://github.com/BlockRunAI/blockrun-mcp/tree/main/skills/exa-research into .github/skills/exa-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exa-research", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add BlockRunAI/blockrun-mcp --skill exa-research -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install BlockRunAI/blockrun-mcp exa-research --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/BlockRunAI/blockrun-mcp.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/exa-research .opencode/skills/exa-research && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "exa-research" agent skill from https://github.com/BlockRunAI/blockrun-mcp/tree/main/skills/exa-research into .opencode/skills/exa-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exa-research", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
exa-research- GitHub stars
- 392
- Token cost
- ~1.6k tokens
- SKILL.md length
- 347 words
- Files
- 1
- Skills in repo
- 17
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when researching products, finding academic papers, discovering competitors, reading webpage content, or getting cited answers grounded in real web sources.
- Works in 5 steps: Initialize (Python SDK) → Search — Find Relevant URLs → Answer — Cited, Grounded Response → …
- Researching products
- SKILL.md covers How to Call from MCP, Quick Decision Table, Python SDK Instructions and Common Research Workflows, plus 2 more sections
- Calls pip; reaches arxiv.org and polymarket.com
What it does
Exa Research is an agent skill from BlockRunAI/blockrun-mcp. Use when researching products, finding academic papers, discovering competitors, reading webpage content, or getting cited answers grounded in real web sources. Use over generic search when semantic relevance matters.
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Productivity & Automation, covering Web search. The repository describes itself as: Live data for AI agents — search, research, markets, crypto, X/Twitter. Pay-per-call via x402 micropayments. The licence is MIT.
When your agent uses it
- Researching products
- Finding academic papers
- Discovering competitors
- Reading webpage content
Example prompts
- “/exa-research”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit e9b2bd5. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
arxiv.orgpolymarket.comtarget-company.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Exa Research loads about 1.6k tokens when it runs. Until then it costs about 58 tokens; SKILL.md has 347 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from BlockRunAI/blockrun-mcp at commit e9b2bd5, republished under its MIT licence (© BlockRunAI). 347 words, ~1,567 tokens.
Download SKILL.mdSave it as
.claude/skills/exa-research/SKILL.md (or your agent's skills folder).- name
- exa-research
- description
- Use when researching products, finding academic papers, discovering competitors, reading webpage content, or getting cited answers grounded in real web sources. Use over generic search when semantic relevance matters.
- triggers
- research, web research, find papers, academic papers, competitor discovery, find similar sites, exa search, cited answer, scrape webpage, neural search…
Exa Research
Neural web search via BlockRun. Understands meaning, not keywords. Four distinct actions for different research modes.
How to Call from MCP
As of v0.14.1 the blockrun_exa tool is path-based. Pass the endpoint name as path and the request as body:
blockrun_exa({ path: "search", body: { query: "AI agent frameworks 2026", numResults: 10 } })
blockrun_exa({ path: "answer", body: { query: "What is speculative decoding?" } })
blockrun_exa({ path: "contents", body: { urls: ["https://example.com/a", "https://example.com/b"] } })
blockrun_exa({ path: "find-similar", body: { url: "https://arxiv.org/abs/2401.12345", numResults: 5 } })Quick Decision Table
Costs below are what you are actually CHARGED — the $0.001 transaction fee is already included (it applies once per call, not per result).
| User wants... | Path | Body | Cost |
|---|---|---|---|
| Relevant URLs on a topic | search | { query, numResults?, category? } | $0.0110/call |
| Cited answer to a question | answer | { query } | $0.0110/call |
| Full text of URLs | contents | { urls: [...] } | $0.002/URL + $0.001 → 1 URL $0.0030, 3 URLs $0.0070 |
| Pages like a given URL | find-similar | { url, numResults? } | $0.0110/call |
| Recent news | search + category: "news" | – | $0.0110/call |
| Academic papers | search + category: "research paper" | – | $0.0110/call |
| Company info | search + category: "company" | – | $0.0110/call |
contents bills per URL, so batching URLs into ONE call is markedly cheaper than
one call each: 3 URLs together cost $0.0070, but three separate calls cost
$0.0090 — you pay the flat fee three times instead of once.
Valid category values for search: "news", "research paper", "company", "tweet", "github", "pdf".
Python SDK Instructions
1. Initialize (Python SDK)
from blockrun_llm import setup_agent_wallet
chain = open(os.path.expanduser("~/.blockrun/.chain")).read().strip() if os.path.exists(os.path.expanduser("~/.blockrun/.chain")) else "base"
if chain == "solana":
from blockrun_llm import setup_agent_solana_wallet
client = setup_agent_solana_wallet()
else:
from blockrun_llm import setup_agent_wallet
client = setup_agent_wallet()2. Search — Find Relevant URLs
# Basic search
result = client._request_with_payment_raw("/v1/exa/search", {
"query": "AI agent frameworks 2025",
"numResults": 10,
})
for r in result.get("results", []):
print(f"{r['title']} — {r['url']}")
# Filter by category
result = client._request_with_payment_raw("/v1/exa/search", {
"query": "transformer architecture improvements",
"numResults": 10,
"category": "research paper",
})
# Restrict to specific domains
result = client._request_with_payment_raw("/v1/exa/search", {
"query": "prediction market regulation",
"numResults": 10,
"includeDomains": ["reuters.com", "bloomberg.com", "wsj.com"],
})Categories: "news", "research paper", "company", "tweet", "github", "pdf"
3. Answer — Cited, Grounded Response
Use when the user asks a factual question and needs reliable sources (not Claude's training data).
result = client._request_with_payment_raw("/v1/exa/answer", {
"query": "What is the current market cap of Polymarket?",
})
print(result.get("answer", ""))
for c in result.get("citations", []):
print(f" [{c.get('title')}] {c.get('url')}")4. Contents — Fetch URL Text
Use when you have URLs and need their full text for LLM context (scraping without a browser).
urls = [
"https://example.com/article-1",
"https://example.com/article-2",
]
result = client._request_with_payment_raw("/v1/exa/contents", {
"urls": urls,
})
for item in result.get("results", []):
print(f"=== {item['url']} ===")
print(item.get("text", "")[:500])Up to 100 URLs per call. Returns Markdown-ready text.
5. Similar — Find Related Pages
Use to discover competitors, related research, or sites with similar content.
result = client._request_with_payment_raw("/v1/exa/find-similar", {
"url": "https://polymarket.com",
"numResults": 10,
})
for r in result.get("results", []):
print(f"{r['title']} — {r['url']}")Common Research Workflows
Competitor discovery:
# 1. Find similar companies
similar = client._request_with_payment_raw("/v1/exa/find-similar", {"url": "https://target-company.com", "numResults": 15})
urls = [r["url"] for r in similar.get("results", [])]
# 2. Fetch their about pages
contents = client._request_with_payment_raw("/v1/exa/contents", {"urls": urls[:10]})Research synthesis:
# 1. Find papers
papers = client._request_with_payment_raw("/v1/exa/search", {
"query": "your topic",
"category": "research paper",
"numResults": 20,
})
# 2. Get answer with citations
answer = client._request_with_payment_raw("/v1/exa/answer", {
"query": "What are the key findings on your topic?",
})When to Use Exa vs client.search()
Use blockrun_exa / _request_with_payment_raw | Use client.search() |
|---|---|
| Finding specific URLs and fetching content | Getting a summarized answer with citations |
| Semantic similarity search | Web + news combined |
| Academic paper discovery | Cheaper per call for simple lookups |
| Domain-filtered research | Already returns a SearchResult object |
Requirements
- BlockRun SDK:
pip install blockrun-llm - USDC wallet funded (see
client.get_balance()) _request_with_payment_rawis the Python SDK entry point for Exa (no dedicated method yet)
© BlockRunAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/exa-research of BlockRunAI/blockrun-mcp.
Open the folder on GitHubat commit e9b2bd5
Compare with similar skills
Exa Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Exa Research this skillBlockRunAI/blockrun-mcp | 392 | — | ~1.6k | Automated safety check: Pass | MIT | |
| Brave Searchbadlogic/pi-skills | 2.6k | 6 repos | ~592 | Automated safety check: Pass | MIT | |
| Enterprise AI Scenario MapMetaInFLow/Enterprise-ai-scenario-map-skill | 632 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Web Searchjjyaoao/HelloAgents | 3.2k | 1 repos | ~5.6k | Automated safety check: Pass | MIT | |
| Ddg SearchTheSyart/claude-agent-examples | 405 | 1 repos | ~493 | Automated safety check: Pass | None | |
| Local Web SearchuluckyXH/OpenMOSS | 1.3k | — | ~392 | Automated safety check: Notes | MIT |
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Enterprise AI Scenario Map
MetaInFLow/Enterprise-ai-scenario-map-skill
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Web Search
jjyaoao/HelloAgents
Implement web search capabilities using the z-ai-web-dev-sdk.
Ddg Search
TheSyart/claude-agent-examples
Web search without an API key using DuckDuckGo Lite via webfetch.
Local Web Search
uluckyXH/OpenMOSS
A skill your agent uses when the user asks for web search that should run via the local-160 Responses API with websearch tool (base URL like https://proxy.example.com, model gpt-5.2-codex(xhigh)).
Web Search
EXboys/skilllite
Web search and content extraction with Tavily and Exa via inference.sh CLI.
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Categories
Questions about Exa Research
What does Exa Research do?
A skill your agent uses when researching products, finding academic papers, discovering competitors, reading webpage content, or getting cited answers grounded in real web sources. Exa Research is an agent skill from BlockRunAI/blockrun-mcp. Use when researching products, finding academic papers, discovering competitors, reading webpage content, or getting cited answers grounded in real web sources.
When should I use Exa Research?
Exa Research fits situations like: researching products; finding academic papers; discovering competitors; reading webpage content.
How do I install Exa Research in Claude Code?
Run `npx skills add BlockRunAI/blockrun-mcp --skill exa-research -a claude-code`. Or copy the skill folder (skills/exa-research in BlockRunAI/blockrun-mcp) into .claude/skills/exa-research in your project. Claude Code loads it when a task matches its description.
How do I install Exa Research in Codex?
Run `npx skills add BlockRunAI/blockrun-mcp --skill exa-research -a codex`. Or copy the skill folder (skills/exa-research in BlockRunAI/blockrun-mcp) into .agents/skills/exa-research in your project. Codex loads it when a task matches its description.
Can I use Exa Research in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add BlockRunAI/blockrun-mcp --skill exa-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/exa-research, .gemini/skills/exa-research, .github/skills/exa-research and .opencode/skills/exa-research in your project.
What does Exa Research need to run?
Going by SKILL.md and its folder, Exa Research needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does Exa Research access the network?
SKILL.md names 3 domains. In commands or code: arxiv.org, polymarket.com and target-company.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.
Is Exa Research safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Exa Research use?
Exa Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Exa Research use?
About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Exa Research?
Skills that share tags, products or a category with Exa Research: Brave Search (badlogic/pi-skills, 2.6k stars), Enterprise AI Scenario Map (MetaInFLow/Enterprise-ai-scenario-map-skill, 632 stars), Web Search (jjyaoao/HelloAgents, 3.2k stars) and Ddg Search (TheSyart/claude-agent-examples, 405 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Exa Research?
BlockRunAI (a GitHub organization) maintains it in BlockRunAI/blockrun-mcp, which has 392 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 8, 2026.
Source: BlockRunAI/blockrun-mcp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.