Twitter Data Export

Twitter Data Export

Overview

Search and export Twitter/X data to CSV files for analysis. Supports keyword search, author-based search, date filtering, and bulk exports up to 500K rows — no Twitter API keys required.

When to Use

Activate when the user asks:

• "Export tweets about [TOPIC] to CSV"
• "Download all tweets from @[USER]"
• "Get Twitter data for [KEYWORD] from last month"
• "I need a dataset of tweets about [TOPIC]"
• "Bulk download tweets matching [QUERY]"
• "Twitter data export"

Setup & Authentication

Before fetching data, ensure Xpoz access is configured. Follow these checks in order.

Check 1: Already authenticated?

If you have MCP tools, try calling any Xpoz tool (e.g., checkAccessKeyStatus). If it works → skip to Step 1.

If you have the SDK, try:

from xpoz import XpozClient
client = XpozClient()  # reads XPOZ_API_KEY env var

If this succeeds without error → skip to Step 1.

If neither works, you need to authenticate. Choose the path that fits your environment:

---

Path A: MCP via mcporter (OpenClaw agents)

If mcporter is available:

mcporter call xpoz.checkAccessKeyStatus

If hasAccessKey: true → ready. If not:

mcporter config add xpoz https://mcp.xpoz.ai/mcp --auth oauth

Then authenticate — generate the OAuth URL and send it to the user:

Step 1: Generate authorization URL

import secrets, hashlib, base64, urllib.parse, json, urllib.request, os

verifier = secrets.token_urlsafe(64)
challenge = base64.urlsafe_b64encode(hashlib.sha256(verifier.encode()).digest()).rstrip(b'=').decode()
state = secrets.token_urlsafe(32)

# Dynamic client registration
reg_req = urllib.request.Request(
    'https://mcp.xpoz.ai/oauth/register',
    data=json.dumps({
        'client_name': 'Agent Skills',
        'redirect_uris': ['https://www.xpoz.ai/oauth/openclaw'],
        'grant_types': ['authorization_code'],
        'response_types': ['code'],
        'token_endpoint_auth_method': 'none',
    }).encode(),
    headers={'Content-Type': 'application/json'},
)
reg_resp = json.loads(urllib.request.urlopen(reg_req).read())

params = urllib.parse.urlencode({
    'response_type': 'code',
    'client_id': reg_resp['client_id'],
    'code_challenge': challenge,
    'code_challenge_method': 'S256',
    'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw',
    'state': state,
    'scope': 'mcp:tools',
    'resource': 'https://mcp.xpoz.ai/',
})

auth_url = 'https://mcp.xpoz.ai/oauth/authorize?' + params

# Save state for token exchange
os.makedirs(os.path.expanduser('~/.cache/xpoz-oauth'), exist_ok=True)
with open(os.path.expanduser('~/.cache/xpoz-oauth/state.json'), 'w') as f:
    json.dump({'verifier': verifier, 'state': state, 'client_id': reg_resp['client_id'],
               'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw'}, f)

print(auth_url)

Step 2: Send the URL to the user

Tell them: > "I need to connect to Xpoz for social media data. Please open this link and sign in: > > [auth_url] > > After authorizing, you'll see a code. Paste it back to me here."

Step 3: WAIT for the user to reply with the code. Do not proceed until they respond.

Step 4: Exchange the code for a token

Once the user provides the code (either a raw code or a URL containing ?code=...), extract the code and exchange it:

import json, urllib.request, urllib.parse, subprocess, os

with open(os.path.expanduser('~/.cache/xpoz-oauth/state.json')) as f:
    oauth = json.load(f)

code = "THE_CODE_FROM_USER"  # Extract from user's reply

data = urllib.parse.urlencode({
    'grant_type': 'authorization_code',
    'code': code,
    'redirect_uri': oauth['redirect_uri'],
    'client_id': oauth['client_id'],
    'code_verifier': oauth['verifier'],
}).encode()

req = urllib.request.Request(
    'https://mcp.xpoz.ai/oauth/token',
    data=data,
    headers={'Content-Type': 'application/x-www-form-urlencoded'},
)
resp = json.loads(urllib.request.urlopen(req).read())
token = resp['access_token']

# Configure mcporter with the token (token is never printed)
subprocess.run(['mcporter', 'config', 'remove', 'xpoz'], capture_output=True)
subprocess.run(['mcporter', 'config', 'add', 'xpoz', 'https://mcp.xpoz.ai/mcp',
                '--header', f'Authorization=Bearer {token}'], check=True)

# Clean up
os.remove(os.path.expanduser('~/.cache/xpoz-oauth/state.json'))
print("Xpoz configured successfully")

Step 5: Verify with mcporter call xpoz.checkAccessKeyStatus → should return hasAccessKey: true.

---

Path B: MCP via Claude Code config

For Claude Code users without mcporter, add to ~/.claude.json:

{
  "mcpServers": {
    "xpoz": {
      "url": "https://mcp.xpoz.ai/mcp",
      "transport": "http-stream"
    }
  }
}

Claude Code handles OAuth automatically on first tool call — the user just needs to authorize in their browser when prompted.

---

Path C: SDK (Python or TypeScript)

Ask the user: > "I need a Xpoz API key to access social media data. Please go to https://xpoz.ai/get-token (it's free, no credit card needed) and paste the key back to me."

WAIT for the user to reply with the key. Then:

Python:

pip install xpoz

from xpoz import XpozClient
client = XpozClient("THE_KEY_FROM_USER")

TypeScript:

npm install @xpoz/xpoz

import { XpozClient } from "@xpoz/xpoz";
const client = new XpozClient({ apiKey: "THE_KEY_FROM_USER" });
await client.connect();

Or set the environment variable and use the default constructor:

export XPOZ_API_KEY=THE_KEY_FROM_USER

---

Auth Errors

| Problem | Solution | |---------|----------| | MCP: "Unauthorized" | Re-run the OAuth flow above | | SDK: AuthenticationError | Verify key at xpoz.ai/settings | | Token exchange fails | Ask user to re-authorize — codes are single-use |

Step-by-Step Instructions

Step 1: Parse the Request

Extract:

• Query: keywords, hashtags, or phrases to search
• Author (optional): specific Twitter username
• Date range (default: last 30 days)
• Fields the user cares about (default: full export)

Build the query using boolean operators:

• Exact phrase: "machine learning"
• OR: "AI" OR "artificial intelligence"
• AND: "Tesla" AND "earnings"
• NOT: "crypto" NOT "scam"
• Combined: ("deep learning" OR "neural network") AND python

Step 2: Search and Export

Via MCP

Search by keywords:

Call getTwitterPostsByKeywords:
  query: ""
  fields: ["id", "text", "authorUsername", "authorId", "createdAtDate", "likeCount", "retweetCount", "quoteCount", "impressionCount", "language"]
  startDate: ""
  endDate: ""
  language: "en" (optional)

Search by author:

Call getTwitterPostsByAuthor:
  identifier: ""
  identifierType: "username"
  fields: ["id", "text", "createdAtDate", "likeCount", "retweetCount", "quoteCount", "impressionCount"]
  startDate: ""
  endDate: ""

CRITICAL: Async Pattern — calls return an operationId. Call checkOperationStatus with that ID and poll until "completed" (up to 8 retries, ~5 seconds apart).

CSV Export (two options):

1. Pass responseType="csv" in the original call to get a CSV download directly
2. Or use the dataDumpExportOperationId from the response — call checkOperationStatus with it to get an S3 download URL for the complete dataset

Via Python SDK

from xpoz import XpozClient

client = XpozClient()  # Uses XPOZ_API_KEY env var

# Search by keywords
results = client.twitter.search_posts(
    '"artificial intelligence" AND ethics',
    start_date="2026-01-01",
    end_date="2026-02-23",
    language="en",
    fields=["id", "text", "author_username", "created_at_date", "like_count", "retweet_count", "impression_count"]
)

print(f"Found {results.pagination.total_rows:,} tweets")

# Export entire result set to CSV (up to 500K rows)
csv_url = results.export_csv()
print(f"Download CSV: {csv_url}")

# Or search by author
author_results = client.twitter.get_posts_by_author(
    "elonmusk",
    start_date="2026-01-01",
    fields=["id", "text", "created_at_date", "like_count", "retweet_count"]
)
author_csv = author_results.export_csv()

client.close()

Download and analyze locally:

import pandas as pd
import subprocess

# Download the CSV
subprocess.run(["curl", "-L", "-o", "tweets.csv", csv_url])

# Load and analyze
df = pd.read_csv("tweets.csv")
print(f"Total tweets: {len(df)}")
print(f"Date range: {df['created_at_date'].min()} to {df['created_at_date'].max()}")
print(f"Average likes: {df['like_count'].mean():.1f}")
print(f"Top authors:\n{df['author_username'].value_counts().head(10)}")

Via TypeScript SDK

import { XpozClient } from "@xpoz/xpoz";

const client = new XpozClient();
await client.connect();

const results = await client.twitter.searchPosts('"artificial intelligence" AND ethics', {
  startDate: "2026-01-01",
  endDate: "2026-02-23",
  language: "en",
  fields: ["id", "text", "authorUsername", "createdAtDate", "likeCount", "retweetCount"],
});

console.log(`Found ${results.pagination.totalRows.toLocaleString()} tweets`);

// Export to CSV
const csvUrl = await results.exportCsv();
console.log(`Download: ${csvUrl}`);

await client.close();

Step 3: Present Results

After export, provide the user with:

1. Summary stats: total rows, date range, top authors, avg engagement
2. CSV download link (from export_csv() / exportCsv())
3. Sample data: show first 5-10 rows as a table
4. Suggested analysis: what they might want to do with the data

## Export Complete: [QUERY]

**Rows exported:** 12,456
**Period:** Jan 1 – Feb 23, 2026
**Download:** [CSV link]

### Sample Data
| Date | Author | Text (truncated) | Likes | RTs |
|------|--------|-------------------|-------|-----|
| ... | ... | ... | ... | ... |

### Quick Stats
- Avg likes per tweet: 45.2
- Most active author: @user (234 tweets)
- Peak day: Feb 14, 2026 (1,203 tweets)

### Suggested Next Steps
- Load into pandas/Excel for deeper analysis
- Filter by engagement (like_count > 100) for high-impact posts
- Group by date for trend analysis

Available Fields

| Field | Description | |-------|-------------| | id | Tweet ID | | text | Full tweet text | | authorUsername | Author's username | | authorId | Author's numeric ID | | createdAtDate | Post date (YYYY-MM-DD) | | likeCount | Number of likes | | retweetCount | Number of retweets | | quoteCount | Number of quote tweets | | impressionCount | Number of impressions | | replyCount | Number of replies | | language | Detected language | | isRetweet | Whether it's a retweet | | isReply | Whether it's a reply |

Example Prompts

• "Export all tweets mentioning 'Claude Code' from the last 2 weeks to CSV"
• "Download @OpenAI's tweets from January 2026"
• "Get a dataset of tweets about 'MCP server' OR 'model context protocol'"
• "Export tweets about the Super Bowl with more than 100 likes"

Notes

• Maximum export size: ~500K rows per CSV
• Date range: up to 60-day rolling windows
• Free tier: 100K results/month at xpoz.ai
• Pro: $20/month for 1M results
• No Twitter API keys needed — Xpoz handles all data access

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Aditya923-c
• Source: Aditya923-c/xpoz-agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.