Influencer Research Skill

Automated workflow for comprehensive KOL/influencer research across social platforms.

Inputs

Workflow

Step 1: Parallel Web Research

Run 3-5 WebSearch queries simultaneously per platform:

Platform: Instagram
├── "{niche} influencers Instagram 2024 2025 followers"
├── "{niche} content creators Instagram {region}"
└── "top {niche} Instagram accounts followers"

Platform: TikTok
├── "TikTok {niche} creators followers 2024"
├── "{niche} TikTok influencers {region}"
└── "best {niche} TikTok accounts"

Platform: YouTube
├── "YouTube {niche} channels subscribers 2024"
├── "{niche} YouTubers {region}"
└── "{niche} YouTube creators"

Platform: X/Twitter
├── "Twitter {niche} advocates influencers followers"
├── "{niche} experts X Twitter"
└── "{niche} thought leaders Twitter"

Platform: Facebook
├── "Facebook {niche} pages groups followers"
├── "{niche} community Facebook {region}"
└── "{niche} organizations Facebook"

Key: Run ALL searches in parallel (single message with multiple WebSearch calls).

Step 2: Deep Fetch Aggregator Sites

Fetch structured data from curated lists:

Step 3: Data Normalization

Cross-reference sources to:

• Verify follower counts (average if sources differ >20%)
• Correct handles (many sources have typos)
• Fill gaps (combine name from one source, handle from another)
• Deduplicate (same person across multiple sources)

Step 4: Excel Generation

Generate formatted Excel with Python:

import pandas as pd
from openpyxl.styles import Font, Alignment, PatternFill

# Standard columns for all platforms
columns = [
    'handle', 'name', 'followers', 'country', 'platform',
    'niche', 'description', 'profile_url', 'engagement', 'content_type'
]

# Create separate DataFrames per platform
# Combine into single Excel with:
# - One sheet per platform
# - "All_Platforms" combined sheet
# - Formatted headers (blue fill, white text)
# - Auto-sized columns
# - Wrapped text for description

output_path = f'{baseDir}/output/{Region}_{Niche}_KOLs.xlsx'

Step 5: Output Summary

Provide chat summary with:

• File path
• Count per platform
• Top 3 influencers per platform (by followers)
• Data completeness %

Data Schema

Efficiency Principles

Example Invocations

Example 1: User says "find me travel influencers in Southeast Asia"

Niche: travel
Region: Southeast Asia
Platforms: All
Target: 50/platform
Output: SEA_Travel_KOLs.xlsx

Example 2: User says "I need ADHD creators for a partnership campaign"

Niche: ADHD
Region: North America (inferred from English request)
Platforms: All
Target: 30-50/platform
Output: NorthAmerica_ADHD_KOLs.xlsx

Example 3: User says "TikTok beauty influencers in US"

Niche: beauty
Region: USA
Platforms: TikTok only
Target: 50
Output: USA_Beauty_TikTok_KOLs.xlsx

Integration with Other Skills

Chain with:

• /founder-content — Create outreach message templates
• /linkedin-gtm — Plan engagement strategy for LinkedIn KOLs
• /twitter-x-gtm — Plan engagement strategy for Twitter KOLs
• /event-gtm — Identify KOLs attending same conferences

Autonomous Mode

When user says variations of "don't ask me" or "work autonomously":

• Make reasonable assumptions for missing parameters
• Default region based on language (English → North America, Chinese → China/SEA)
• Default to all 5 platforms
• Default to 30-50 per platform target
• Proceed without confirmation prompts

Author

Canlah AI — Run performance marketing without breaking your brand.

• GitHub: github.com/PHY041
• All Skills: clawhub.ai/PHY041