influencer-discovery · v2026-02-23 · 2026-02-23 · sha256 def22d8d5b34cbf1
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 them in order:
### Check 1: MCP Server (preferred)
If you have MCP tool access, check if `xpoz` tools are available (e.g., `getTwitterPostsByKeywords`). If yes, authentication is already handled — skip to Step 1.
### Check 2: SDK via Environment Variable
```bash
echo $XPOZ_API_KEY
```
If set, the SDK will authenticate automatically:
```python
from xpoz import XpozClient
client = XpozClient() # auto-reads XPOZ_API_KEY
```
### Check 3: Nothing Configured
If neither MCP nor env var is available, ask the user to:
1. 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)
2. Then EITHER:
- **Set env var:** `export XPOZ_API_KEY=<their-key>` and use the SDK
- **Configure MCP:** Add to `~/.claude.json` (or agent's MCP config):
```json
{
"mcpServers": {
"xpoz": {
"url": "https://mcp.xpoz.ai/mcp",
"transport": "http-stream",
"headers": { "Authorization": "Bearer <their-key>" }
}
}
}
```
- **Pass directly:** `XpozClient("<their-key>")` in Python or `new XpozClient({ apiKey: "<their-key>" })` in TypeScript
### Auth Errors
If you get `AuthenticationError` or 401 responses:
- Verify the API key is valid at [xpoz.ai/settings](https://xpoz.ai/settings)
- Check the key hasn't expired
- Ensure no extra whitespace in the key
## 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)