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.
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安装前可以查看交给 AI 代理的指令内容。
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.
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 summary
| 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 |
~$0.01 per result. 100 influencers ~ $1.00. The script prints a cost estimate before running.
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--min-followers and --max-followers flags filter client-side after results returnharvestapi/linkedin-profile-scraper actor on the discovered LinkedIn URLsharvestapi/linkedin-profile-posts actor on the discovered LinkedIn URLs还没有评价。欢迎分享你的使用感受。
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
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