Influencer Discovery

Influencer Discovery

Overview

Find, evaluate, and rank influencers for any niche across Twitter/X and Instagram. Identifies who is actively creating content about a topic, ranks them by engagement and relevance, and provides authenticity scoring.

When to Use

Activate when the user asks:

• "Find influencers in [NICHE] on Twitter"
• "Who are the top voices talking about [TOPIC]?"
• "Discover thought leaders in [INDUSTRY]"
• "Find micro-influencers for [PRODUCT CATEGORY]"
• "KOL research for [TOPIC]"
• "Who should we partner with for [CAMPAIGN]?"

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:

• Niche/topic to search
• Platform (default: Twitter; add Instagram if relevant)
• Influencer tier preference (if specified):
• Mega: 1M+ followers
• Macro: 100K–1M
• Micro: 10K–100K
• Nano: 1K–10K
• Time period (default: last 30 days)

Build search queries targeting content creators, not just mentions:

• Topic keywords: "AI agents" OR "autonomous AI" OR "agentic AI"
• Include specific subtopics for better targeting

Step 2: Find Active Users by Topic

Via MCP

Call getTwitterUsersByKeywords:
  query: ""
  fields: ["id", "username", "name", "description", "followersCount", "followingCount", "tweetCount", "relevantTweetsCount", "relevantTweetsLikesSum", "relevantTweetsImpressionsSum", "isInauthentic", "isInauthenticProbScore", "verified"]
  startDate: ""
  endDate: ""

CRITICAL: Call checkOperationStatus with the returned operationId and poll until "completed".

The response includes powerful aggregation fields:

• relevantTweetsCount — how many times they posted about the topic
• relevantTweetsLikesSum — total likes on their topic-relevant posts
• relevantTweetsImpressionsSum — total impressions on relevant posts

For deeper analysis on top candidates:

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

Via Python SDK

from xpoz import XpozClient

client = XpozClient()

# Find users who posted about the topic
users = client.twitter.get_users_by_keywords(
    '"AI agents" OR "autonomous AI" OR "agentic AI"',
    start_date="2026-01-24",
    end_date="2026-02-23",
    fields=[
        "id", "username", "name", "description",
        "followers_count", "following_count", "tweet_count",
        "relevant_tweets_count", "relevant_tweets_likes_sum",
        "relevant_tweets_impressions_sum",
        "is_inauthentic", "is_inauthentic_prob_score", "verified"
    ]
)

# Collect all pages
all_users = users.data
while users.has_next_page():
    users = users.next_page()
    all_users.extend(users.data)

# Deep-dive on top candidates
for user in top_candidates[:10]:
    posts = client.twitter.get_posts_by_author(
        user.username,
        start_date="2026-01-24",
        fields=["id", "text", "like_count", "retweet_count", "impression_count", "created_at_date"]
    )
    # Analyze their content quality, consistency, tone

client.close()

Via TypeScript SDK

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

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

const users = await client.twitter.getUsersByKeywords(
  '"AI agents" OR "autonomous AI" OR "agentic AI"',
  {
    startDate: "2026-01-24",
    endDate: "2026-02-23",
    fields: [
      "id", "username", "name", "description",
      "followersCount", "followingCount", "tweetCount",
      "relevantTweetsCount", "relevantTweetsLikesSum",
      "relevantTweetsImpressionsSum",
      "isInauthentic", "isInauthenticProbScore", "verified",
    ],
  }
);

await client.close();

Step 3: Score and Rank

For each user, calculate an Influencer Score (0–100):

| Factor | Weight | Calculation | |--------|--------|-------------| | Relevance | 30% | min(relevantTweetsCount × 6, 30) — more topic posts = more relevant | | Engagement | 30% | min((relevantTweetsLikesSum / relevantTweetsCount) / 50, 30) — avg engagement per post | | Reach | 20% | min(log10(followersCount) × 5, 20) — logarithmic follower scale | | Authenticity | 10% | (1 - isInauthenticProbScore) × 10 — Xpoz bot detection | | Consistency | 10% | min(relevantTweetsCount / days × 10, 10) — posting frequency |

Step 4: Classify Influencers

By Tier: | Tier | Followers | Typical Value | |------|-----------|---------------| | Mega | 1M+ | Broad awareness, expensive | | Macro | 100K–1M | Strong reach, established | | Micro | 10K–100K | High engagement, niche authority | | Nano | 1K–10K | Very targeted, authentic, affordable |

By Voice Type (analyze their bio + recent posts): | Type | Description | |------|-------------| | Analyst | Data-driven, market commentary | | Builder | Creates products/tools in the space | | Educator | Tutorials, explainers, threads | | News | Breaks/shares news and updates | | Commentator | Opinions, hot takes, discussions | | Community | Moderates/leads community spaces |

Step 5: Generate Report

## Influencer Discovery: [TOPIC]
**Period:** [date range] | **Users analyzed:** [count] | **Platform:** Twitter

### Top Influencers

| Rank | User | Followers | Posts | Avg Likes | Score | Tier | Type |
|------|------|-----------|-------|-----------|-------|------|------|
| 1 | @user | 45K | 12 | 890 | 87 | Micro | Builder |
| 2 | ... | ... | ... | ... | ... | ... | ... |

### Tier Distribution
- Mega (1M+): X users
- Macro (100K–1M): X users
- Micro (10K–100K): X users
- Nano (1K–10K): X users

### Detailed Profiles (Top 10)

#### 1. @username — "Display Name"
- **Bio:** [description]
- **Followers:** X | **Topic Posts:** X | **Avg Engagement:** X
- **Voice Type:** Builder
- **Authenticity:** ✅ Verified authentic (score: 0.95)
- **Sample Posts:**
  - "[tweet text]" (❤️ X, 🔁 X)
  - "[tweet text]" (❤️ X, 🔁 X)
- **Why They Matter:** [1-2 sentences on their influence in this niche]

### Recommendations
[Which influencers are best for different goals: awareness vs credibility vs engagement]

Example Prompts

• "Find the top 20 AI agent influencers on Twitter"
• "Who are the micro-influencers talking about sustainable fashion on Instagram?"
• "Discover crypto KOLs with high engagement rates"
• "Find developer advocates who post about MCP servers"

Notes

• Xpoz's relevantTweetsCount and relevantTweetsLikesSum fields let you find influencers by what they create, not just follower count
• Authenticity scoring (isInauthenticProbScore) helps filter out bots and fake accounts
• Free tier: 100K results/month at xpoz.ai

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.