influencer-discovery v2026-02-23A

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---
name: influencer-discovery
version: 2026-02-23
description: Find and rank influencers by niche, engagement, and authenticity using Xpoz. Searches Twitter, Instagram, and Reddit for active voices in any topic. Use when asked to "find influencers", "discover thought leaders", "who's talking about X", "influencer research", or "find KOLs".
---

# 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, verify Xpoz access is configured. Try these in order:

### Check 1: MCP Server (preferred for AI agents)

If you have MCP tool access, test if Xpoz is already authenticated:

```bash
mcporter call xpoz.checkAccessKeyStatus
```

- If `hasAccessKey: true` → **Xpoz is ready.** Skip to Step 1.
- If it fails or the server isn't configured → set it up:

**Add the server:**
```bash
mcporter config add xpoz https://mcp.xpoz.ai/mcp --auth oauth
```

**Authenticate (local machine with browser):**
```bash
mcporter config login xpoz
```
This opens the browser for Google sign-in. Tell the user:
> "A browser window should open — just sign in with your Google account and click Authorize."

**Authenticate (remote/headless server):**
If there's no browser, generate an OAuth URL and send it to the user:
```bash
mcporter config login xpoz
```
Copy the authorization URL from the output, send it to the user, and wait for them to paste back the authorization code.

**Verify:**
```bash
mcporter call xpoz.checkAccessKeyStatus
```

For **Claude Code** users, Xpoz can also be added to `~/.claude.json`:
```json
{
  "mcpServers": {
    "xpoz": {
      "url": "https://mcp.xpoz.ai/mcp",
      "transport": "http-stream"
    }
  }
}
```
Authentication is handled via OAuth on first use — no API keys needed in the config.

### Check 2: SDK (for coding tasks)

Install the SDK and set your API key:

**Python:**
```bash
pip install xpoz
export XPOZ_API_KEY=your-token-here  # Get at https://xpoz.ai/get-token
```
```python
from xpoz import XpozClient
client = XpozClient()  # auto-reads XPOZ_API_KEY
# Or pass directly: XpozClient("your-token-here")
```

**TypeScript:**
```bash
npm install xpoz
export XPOZ_API_KEY=your-token-here
```
```typescript
import { XpozClient } from "xpoz";
const client = new XpozClient(); // auto-reads XPOZ_API_KEY
await client.connect();
// Or pass directly: new XpozClient({ apiKey: "your-token-here" })
```

Get a free API key at [xpoz.ai/get-token](https://xpoz.ai/get-token?utm_source=github&utm_medium=agent-skills&utm_campaign=influencer-discovery) — 100K results/month free, no credit card required.

### Auth Errors
| Problem | Solution |
|---------|----------|
| MCP: "Unauthorized" | Run `mcporter config login xpoz --reset` |
| SDK: `AuthenticationError` | Verify API key at [xpoz.ai/settings](https://xpoz.ai/settings) |
| mcporter not found | Included with OpenClaw — ensure OpenClaw is installed |


## 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: "<expanded query>"
  fields: ["id", "username", "name", "description", "followersCount", "followingCount", "tweetCount", "relevantTweetsCount", "relevantTweetsLikesSum", "relevantTweetsImpressionsSum", "isInauthentic", "isInauthenticProbScore", "verified"]
  startDate: "<30 days ago, YYYY-MM-DD>"
  endDate: "<today, YYYY-MM-DD>"
```

**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: "<username>"
  identifierType: "username"
  fields: ["id", "text", "likeCount", "retweetCount", "impressionCount", "createdAtDate"]
  startDate: "<30 days ago>"
```

#### Via Python SDK

```python
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

```typescript
import { XpozClient } from "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](https://xpoz.ai?utm_source=github&utm_medium=agent-skills&utm_campaign=influencer-discovery)