LinkedIn Influencer Discovery
Discover top LinkedIn influencers and voices by topic, industry, follower count, and country. Use when you need to find the top 100 voices in a space, build influencer lists for outreach, or identify thought leaders on LinkedIn.
by Athina AInpx gooseworks install --all # then, in Claude Code, Cursor, or Codex: /gooseworks use the linkedin-influencer-discovery skill
LinkedIn Influencer Discovery
Discover top LinkedIn influencers by topic, country, and follower count using the Apify powerai/influencer-filter-api-scraper actor. Queries a database of 3.6M+ influencer profiles filtered to those with LinkedIn presence.
Quick Start
Requires APIFY_API_TOKEN env var (or --token flag). Install dependency: pip install requests.
# Find top AI influencers with LinkedIn profiles
python3 skills/linkedin-influencer-discovery/scripts/discover_influencers.py \
--topic "artificial intelligence" --max-results 50 --output summary
# Find SaaS influencers in the US
python3 skills/linkedin-influencer-discovery/scripts/discover_influencers.py \
--topic "saas" --country "United States of America" --output summary
# Find marketing influencers with email available
python3 skills/linkedin-influencer-discovery/scripts/discover_influencers.py \
--topic "marketing" --has-email --max-results 100
# Filter to a specific follower range
python3 skills/linkedin-influencer-discovery/scripts/discover_influencers.py \
--topic "fintech" --min-followers 10000 --max-followers 500000 --output summaryCLI Reference
| Flag | Default | Description |
|---|---|---|
--topic | required | Topic to search (e.g. "artificial intelligence", "saas", "marketing") |
--category | none | Category filter (e.g. "technology", "business", "lifestyle") |
--country | none | Country (e.g. "United States of America", "United Kingdom") |
--language | English | Language filter |
--min-followers | 0 | Minimum follower count (client-side filter) |
--max-followers | 0 (unlimited) | Maximum follower count (client-side filter) |
--has-email | false | Only return influencers with an email address |
--max-results | 100 | Max influencers to discover (up to 1000) |
--output | json | Output format: json or summary |
--token | env var | Apify token (prefer APIFY_API_TOKEN env var) |
--timeout | 600 | Max seconds to wait for Apify run |
Cost
~$0.01 per result. 100 influencers ~ $1.00. The script prints a cost estimate before running.
Output Fields
Each influencer result includes (when available):
full_name- Display nameusername- Social media handlebiography- Bio textfollower_count- Total followers (across platforms)following_count- Following countmain_topic- Primary topic/nichetopics- List of associated topicscategory_name- Category classificationlinkedin_url- LinkedIn profile URLhas_email- Whether email is availableexternal_url- Website URLscountry,city- Locationis_verified- Verification status
Notes
- Results are sorted by follower count (descending) by default
- The actor queries a pre-indexed database, not live LinkedIn search
- Follower counts are across all platforms, not LinkedIn-specific
- The
--min-followersand--max-followersflags filter client-side after results return - For detailed profile enrichment, use the Apify
harvestapi/linkedin-profile-scraperactor on the discovered LinkedIn URLs - For post analysis, use the Apify
harvestapi/linkedin-profile-postsactor on the discovered LinkedIn URLs
What's included
full_name - Display nameusername - Social media handlebiography - Bio textfollower_count - Total followers (across platforms)following_count - Following countRender Model Comparison Grid
Render a 'model comparison grid' video from a config — a fal-style "same prompt, N contenders" showcase — a dark real-DOM stage where per beat a monospace prompt fades in centered, docks to a small top strip, then a labeled 2-4 panel grid (static images OR muted video clips, mixable per cell) staggers in and holds for comparison, plus a minimal end card — frame-stepped via Playwright (video cells are frame-seeked deterministically) and encoded with FFmpeg. Deterministic assembly, FREE (cell media comes from create-image-fal / create-video-fal, music from create-music-elevenlabs), text stays pixel-crisp. Use for the model-comparison-grid format.
Render Offer Ad
Render a punchy ~12s vertical (9:16) music-only direct-response OFFER ad as a 4-beat kinetic-typography film — HEADLINE slam → real PRODUCT drop → CLAIM/proof → CTA pill — from one config of copy slots, a real product photo, a brand palette, fonts, bpm, and beat split. DETERMINISTIC + FREE (a bundled Remotion project; springs + interpolate, no AI-gen for visuals). Backgrounds are engine gradient divs off the palette, props are inline SVG, the ONLY composited bitmap is the REAL product photo (objectFit:contain, never stretched), and ALL headline/claim/CTA/URL/wordmark text is typeset in the engine — never AI-rendered (the format's credibility guard). A driver binds the config to Remotion input props, renders the 9:16 master, and derives a 1:1 center-crop with ffmpeg. Two gating checks run before render (claim verbs must match the product's physical format; the claim beat needs an edge-entry mechanism prop). Use for the motion-graphics-offer-ad format.
Render Myth Vs Fact
Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word onsets, concat at a uniform fps, karaoke `.ass` captions burned last (suppressed on the proof + end-card beats), and a VO + optional music mix (music −20 dB, `amix normalize=0`, tail fade). FREE (Python + Playwright + ffmpeg); the recipe supplies the copy / palette / end-card / VO and gates the paid VO / music / Whisper calls to their own capabilities. Use for the myth-vs-fact format.