{"id":"6f489178-8a03-4ca6-9465-3250662b823f","shortId":"QhWAuR","kind":"skill","title":"influencer-discovery","tagline":"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\".","description":"# Influencer Discovery\n\n## Overview\n\nFind, 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.\n\n## When to Use\n\nActivate when the user asks:\n- \"Find influencers in [NICHE] on Twitter\"\n- \"Who are the top voices talking about [TOPIC]?\"\n- \"Discover thought leaders in [INDUSTRY]\"\n- \"Find micro-influencers for [PRODUCT CATEGORY]\"\n- \"KOL research for [TOPIC]\"\n- \"Who should we partner with for [CAMPAIGN]?\"\n\n## Setup & Authentication\n\nBefore fetching data, ensure Xpoz access is configured. Follow these checks in order.\n\n### Check 1: Already authenticated?\n\n**If you have MCP tools**, try calling any Xpoz tool (e.g., `checkAccessKeyStatus`). If it works → skip to Step 1.\n\n**If you have the SDK**, try:\n```python\nfrom xpoz import XpozClient\nclient = XpozClient()  # reads XPOZ_API_KEY env var\n```\nIf this succeeds without error → skip to Step 1.\n\nIf neither works, you need to authenticate. Choose the path that fits your environment:\n\n---\n\n### Path A: MCP via mcporter (OpenClaw agents)\n\nIf `mcporter` is available:\n\n```bash\nmcporter call xpoz.checkAccessKeyStatus\n```\n\nIf `hasAccessKey: true` → ready. If not:\n\n```bash\nmcporter config add xpoz https://mcp.xpoz.ai/mcp --auth oauth\n```\n\nThen authenticate — generate the OAuth URL and send it to the user:\n\n**Step 1: Generate authorization URL**\n```python\nimport secrets, hashlib, base64, urllib.parse, json, urllib.request, os\n\nverifier = secrets.token_urlsafe(64)\nchallenge = base64.urlsafe_b64encode(hashlib.sha256(verifier.encode()).digest()).rstrip(b'=').decode()\nstate = secrets.token_urlsafe(32)\n\n# Dynamic client registration\nreg_req = urllib.request.Request(\n    'https://mcp.xpoz.ai/oauth/register',\n    data=json.dumps({\n        'client_name': 'Agent Skills',\n        'redirect_uris': ['https://www.xpoz.ai/oauth/openclaw'],\n        'grant_types': ['authorization_code'],\n        'response_types': ['code'],\n        'token_endpoint_auth_method': 'none',\n    }).encode(),\n    headers={'Content-Type': 'application/json'},\n)\nreg_resp = json.loads(urllib.request.urlopen(reg_req).read())\n\nparams = urllib.parse.urlencode({\n    'response_type': 'code',\n    'client_id': reg_resp['client_id'],\n    'code_challenge': challenge,\n    'code_challenge_method': 'S256',\n    'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw',\n    'state': state,\n    'scope': 'mcp:tools',\n    'resource': 'https://mcp.xpoz.ai/',\n})\n\nauth_url = 'https://mcp.xpoz.ai/oauth/authorize?' + params\n\n# Save state for token exchange\nos.makedirs(os.path.expanduser('~/.cache/xpoz-oauth'), exist_ok=True)\nwith open(os.path.expanduser('~/.cache/xpoz-oauth/state.json'), 'w') as f:\n    json.dump({'verifier': verifier, 'state': state, 'client_id': reg_resp['client_id'],\n               'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw'}, f)\n\nprint(auth_url)\n```\n\n**Step 2: Send the URL to the user**\n\nTell them:\n> \"I need to connect to Xpoz for social media data. Please open this link and sign in:\n>\n> [auth_url]\n>\n> After authorizing, you'll see a code. Paste it back to me here.\"\n\n**Step 3: WAIT for the user to reply with the code.** Do not proceed until they respond.\n\n**Step 4: Exchange the code for a token**\n\nOnce the user provides the code (either a raw code or a URL containing `?code=...`), extract the code and exchange it:\n\n```python\nimport json, urllib.request, urllib.parse, subprocess, os\n\nwith open(os.path.expanduser('~/.cache/xpoz-oauth/state.json')) as f:\n    oauth = json.load(f)\n\ncode = \"THE_CODE_FROM_USER\"  # Extract from user's reply\n\ndata = urllib.parse.urlencode({\n    'grant_type': 'authorization_code',\n    'code': code,\n    'redirect_uri': oauth['redirect_uri'],\n    'client_id': oauth['client_id'],\n    'code_verifier': oauth['verifier'],\n}).encode()\n\nreq = urllib.request.Request(\n    'https://mcp.xpoz.ai/oauth/token',\n    data=data,\n    headers={'Content-Type': 'application/x-www-form-urlencoded'},\n)\nresp = json.loads(urllib.request.urlopen(req).read())\ntoken = resp['access_token']\n\n# Configure mcporter with the token (token is never printed)\nsubprocess.run(['mcporter', 'config', 'remove', 'xpoz'], capture_output=True)\nsubprocess.run(['mcporter', 'config', 'add', 'xpoz', 'https://mcp.xpoz.ai/mcp',\n                '--header', f'Authorization=Bearer {token}'], check=True)\n\n# Clean up\nos.remove(os.path.expanduser('~/.cache/xpoz-oauth/state.json'))\nprint(\"Xpoz configured successfully\")\n```\n\n**Step 5: Verify** with `mcporter call xpoz.checkAccessKeyStatus` → should return `hasAccessKey: true`.\n\n---\n\n### Path B: MCP via Claude Code config\n\nFor Claude Code users without mcporter, add to `~/.claude.json`:\n```json\n{\n  \"mcpServers\": {\n    \"xpoz\": {\n      \"url\": \"https://mcp.xpoz.ai/mcp\",\n      \"transport\": \"http-stream\"\n    }\n  }\n}\n```\nClaude Code handles OAuth automatically on first tool call — the user just needs to authorize in their browser when prompted.\n\n---\n\n### Path C: SDK (Python or TypeScript)\n\nAsk the user:\n> \"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.\"\n\n**WAIT for the user to reply with the key.** Then:\n\n**Python:**\n```bash\npip install xpoz\n```\n```python\nfrom xpoz import XpozClient\nclient = XpozClient(\"THE_KEY_FROM_USER\")\n```\n\n**TypeScript:**\n```bash\nnpm install @xpoz/xpoz\n```\n```typescript\nimport { XpozClient } from \"@xpoz/xpoz\";\nconst client = new XpozClient({ apiKey: \"THE_KEY_FROM_USER\" });\nawait client.connect();\n```\n\nOr set the environment variable and use the default constructor:\n```bash\nexport XPOZ_API_KEY=THE_KEY_FROM_USER\n```\n\n---\n\n### Auth Errors\n| Problem | Solution |\n|---------|----------|\n| MCP: \"Unauthorized\" | Re-run the OAuth flow above |\n| SDK: `AuthenticationError` | Verify key at [xpoz.ai/settings](https://xpoz.ai/settings) |\n| Token exchange fails | Ask user to re-authorize — codes are single-use |\n\n\n## Step-by-Step Instructions\n\n### Step 1: Parse the Request\n\nExtract:\n- **Niche/topic** to search\n- **Platform** (default: Twitter; add Instagram if relevant)\n- **Influencer tier** preference (if specified):\n  - Mega: 1M+ followers\n  - Macro: 100K–1M\n  - Micro: 10K–100K\n  - Nano: 1K–10K\n- **Time period** (default: last 30 days)\n\nBuild search queries targeting content creators, not just mentions:\n- Topic keywords: `\"AI agents\" OR \"autonomous AI\" OR \"agentic AI\"`\n- Include specific subtopics for better targeting\n\n### Step 2: Find Active Users by Topic\n\n#### Via MCP\n\n```\nCall getTwitterUsersByKeywords:\n  query: \"<expanded query>\"\n  fields: [\"id\", \"username\", \"name\", \"description\", \"followersCount\", \"followingCount\", \"tweetCount\", \"relevantTweetsCount\", \"relevantTweetsLikesSum\", \"relevantTweetsImpressionsSum\", \"isInauthentic\", \"isInauthenticProbScore\", \"verified\"]\n  startDate: \"<30 days ago, YYYY-MM-DD>\"\n  endDate: \"<today, YYYY-MM-DD>\"\n```\n\n**CRITICAL:** Call `checkOperationStatus` with the returned `operationId` and poll until \"completed\".\n\nThe response includes powerful aggregation fields:\n- `relevantTweetsCount` — how many times they posted about the topic\n- `relevantTweetsLikesSum` — total likes on their topic-relevant posts\n- `relevantTweetsImpressionsSum` — total impressions on relevant posts\n\n**For deeper analysis on top candidates:**\n```\nCall getTwitterPostsByAuthor:\n  identifier: \"<username>\"\n  identifierType: \"username\"\n  fields: [\"id\", \"text\", \"likeCount\", \"retweetCount\", \"impressionCount\", \"createdAtDate\"]\n  startDate: \"<30 days ago>\"\n```\n\n#### Via Python SDK\n\n```python\nfrom xpoz import XpozClient\n\nclient = XpozClient()\n\n# Find users who posted about the topic\nusers = client.twitter.get_users_by_keywords(\n    '\"AI agents\" OR \"autonomous AI\" OR \"agentic AI\"',\n    start_date=\"2026-01-24\",\n    end_date=\"2026-02-23\",\n    fields=[\n        \"id\", \"username\", \"name\", \"description\",\n        \"followers_count\", \"following_count\", \"tweet_count\",\n        \"relevant_tweets_count\", \"relevant_tweets_likes_sum\",\n        \"relevant_tweets_impressions_sum\",\n        \"is_inauthentic\", \"is_inauthentic_prob_score\", \"verified\"\n    ]\n)\n\n# Collect all pages\nall_users = users.data\nwhile users.has_next_page():\n    users = users.next_page()\n    all_users.extend(users.data)\n\n# Deep-dive on top candidates\nfor user in top_candidates[:10]:\n    posts = client.twitter.get_posts_by_author(\n        user.username,\n        start_date=\"2026-01-24\",\n        fields=[\"id\", \"text\", \"like_count\", \"retweet_count\", \"impression_count\", \"created_at_date\"]\n    )\n    # Analyze their content quality, consistency, tone\n\nclient.close()\n```\n\n#### Via TypeScript SDK\n\n```typescript\nimport { XpozClient } from \"@xpoz/xpoz\";\n\nconst client = new XpozClient();\nawait client.connect();\n\nconst users = await client.twitter.getUsersByKeywords(\n  '\"AI agents\" OR \"autonomous AI\" OR \"agentic AI\"',\n  {\n    startDate: \"2026-01-24\",\n    endDate: \"2026-02-23\",\n    fields: [\n      \"id\", \"username\", \"name\", \"description\",\n      \"followersCount\", \"followingCount\", \"tweetCount\",\n      \"relevantTweetsCount\", \"relevantTweetsLikesSum\",\n      \"relevantTweetsImpressionsSum\",\n      \"isInauthentic\", \"isInauthenticProbScore\", \"verified\",\n    ],\n  }\n);\n\nawait client.close();\n```\n\n### Step 3: Score and Rank\n\nFor each user, calculate an **Influencer Score (0–100)**:\n\n| Factor | Weight | Calculation |\n|--------|--------|-------------|\n| Relevance | 30% | `min(relevantTweetsCount × 6, 30)` — more topic posts = more relevant |\n| Engagement | 30% | `min((relevantTweetsLikesSum / relevantTweetsCount) / 50, 30)` — avg engagement per post |\n| Reach | 20% | `min(log10(followersCount) × 5, 20)` — logarithmic follower scale |\n| Authenticity | 10% | `(1 - isInauthenticProbScore) × 10` — Xpoz bot detection |\n| Consistency | 10% | `min(relevantTweetsCount / days × 10, 10)` — posting frequency |\n\n### Step 4: Classify Influencers\n\n**By Tier:**\n| Tier | Followers | Typical Value |\n|------|-----------|---------------|\n| Mega | 1M+ | Broad awareness, expensive |\n| Macro | 100K–1M | Strong reach, established |\n| Micro | 10K–100K | High engagement, niche authority |\n| Nano | 1K–10K | Very targeted, authentic, affordable |\n\n**By Voice Type** (analyze their bio + recent posts):\n| Type | Description |\n|------|-------------|\n| Analyst | Data-driven, market commentary |\n| Builder | Creates products/tools in the space |\n| Educator | Tutorials, explainers, threads |\n| News | Breaks/shares news and updates |\n| Commentator | Opinions, hot takes, discussions |\n| Community | Moderates/leads community spaces |\n\n### Step 5: Generate Report\n\n```\n## Influencer Discovery: [TOPIC]\n**Period:** [date range] | **Users analyzed:** [count] | **Platform:** Twitter\n\n### Top Influencers\n\n| Rank | User | Followers | Posts | Avg Likes | Score | Tier | Type |\n|------|------|-----------|-------|-----------|-------|------|------|\n| 1 | @user | 45K | 12 | 890 | 87 | Micro | Builder |\n| 2 | ... | ... | ... | ... | ... | ... | ... |\n\n### Tier Distribution\n- Mega (1M+): X users\n- Macro (100K–1M): X users\n- Micro (10K–100K): X users\n- Nano (1K–10K): X users\n\n### Detailed Profiles (Top 10)\n\n#### 1. @username — \"Display Name\"\n- **Bio:** [description]\n- **Followers:** X | **Topic Posts:** X | **Avg Engagement:** X\n- **Voice Type:** Builder\n- **Authenticity:** ✅ Verified authentic (score: 0.95)\n- **Sample Posts:**\n  - \"[tweet text]\" (❤️ X, 🔁 X)\n  - \"[tweet text]\" (❤️ X, 🔁 X)\n- **Why They Matter:** [1-2 sentences on their influence in this niche]\n\n### Recommendations\n[Which influencers are best for different goals: awareness vs credibility vs engagement]\n```\n\n## Example Prompts\n\n- \"Find the top 20 AI agent influencers on Twitter\"\n- \"Who are the micro-influencers talking about sustainable fashion on Instagram?\"\n- \"Discover crypto KOLs with high engagement rates\"\n- \"Find developer advocates who post about MCP servers\"\n\n## Notes\n\n- Xpoz's `relevantTweetsCount` and `relevantTweetsLikesSum` fields let you find influencers by **what they create**, not just follower count\n- Authenticity scoring (`isInauthenticProbScore`) helps filter out bots and fake accounts\n- Free tier: 100K results/month at 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