dvcrn Influencer Research Skill
Automated workflow for comprehensive KOL/influencer research across social platforms.
Inputs
| Parameter | Required | Default | Examples |
|---|---|---|---|
| Niche | Yes | - | "ADHD", "travel", "DTC beauty", "AI/tech", "fitness" |
| Region | Yes | - | "North America", "Southeast Asia", "Europe", "Global" |
| Platforms | No | All 5 | Instagram, TikTok, YouTube, X/Twitter, Facebook |
| Target Count | No | 30-50/platform | Any number |
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:
| Site | URL Pattern | Data Quality |
|---|---|---|
| Feedspot | influencers.feedspot.com/{niche}_instagram_influencers/ |
High - top 100 lists |
| The Influence Agency | theinfluenceagency.com/blog/{niche}-influencers-to-follow |
High - curated with handles |
| Upfluence | upfluence.com/find-influencers/top-{niche}-influencers |
Medium - regional focus |
| IZEA | izea.com/resources/content-creators-with-{niche}/ |
Medium - brand partnership focus |
| Heepsy | heepsy.com/top-{platform}/{niche} |
High - platform-specific |
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
| Field | Description | Example |
|---|---|---|
| handle | Platform username with @ | @thepsychdoctormd |
| name | Display name | Dr. Sasha Hamdani |
| followers | Count with K/M suffix | 936K |
| country | ISO country or region | USA, Canada, Singapore |
| platform | Source platform | |
| niche | Content focus | ADHD Psychiatrist |
| description | 1-2 sentence bio | Board-certified psychiatrist, author... |
| profile_url | Direct link | https://instagram.com/thepsychdoctormd |
| engagement | High/Medium/Low | High |
| content_type | Content themes | Medical education, Q&A |
Efficiency Principles
| Principle | Implementation |
|---|---|
| Parallel execution | Multiple WebSearch in single message |
| Leverage aggregators | Use curated lists, not individual profile searches |
| Single script output | One Python block creates entire formatted Excel |
| No manual steps | Fully automated end-to-end |
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