Extract commenters from LinkedIn posts via Apify. Returns commenter names, titles, LinkedIn profile URLs, and comment text. Use to find warm leads engaging with relevant discussions. No LinkedIn cookies required.
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Extract names, titles, companies, LinkedIn URLs, and comment text from people who commented on specific LinkedIn posts. Uses Apify — no LinkedIn cookies required.
Requires requests and APIFY_API_TOKEN environment variable.
# Extract commenters from a single post
python3 skills/linkedin-commenter-extractor/scripts/extract_commenters.py \
--post-url "https://www.linkedin.com/posts/someone_topic-activity-123456789"
# Multiple posts
python3 skills/linkedin-commenter-extractor/scripts/extract_commenters.py \
--post-url URL1 --post-url URL2
# Limit comments per post
python3 skills/linkedin-commenter-extractor/scripts/extract_commenters.py \
--post-url URL --max-comments 50
# Output formats
python3 skills/linkedin-commenter-extractor/scripts/extract_commenters.py --post-url URL --output json
python3 skills/linkedin-commenter-extractor/scripts/extract_commenters.py --post-url URL --output csv
python3 skills/linkedin-commenter-extractor/scripts/extract_commenters.py --post-url URL --output summary
# Deduplicate across multiple posts
python3 skills/linkedin-commenter-extractor/scripts/extract_commenters.py \
--post-url URL1 --post-url URL2 --dedup
harvestapi~linkedin-post-comments Apify actor (no cookies needed)| Flag | Default | Description |
|---|---|---|
--post-url | required | LinkedIn post URL (can be repeated for multiple posts) |
--max-comments | 100 | Max comments to extract per post |
--output | json | Output format: json, csv, summary |
--dedup | false | Deduplicate commenters across multiple posts |
--token | env var | Apify API token (overrides APIFY_API_TOKEN env var) |
--timeout | 120 | Max seconds to wait for Apify run |
{
"name": "Jane Smith",
"headline": "VP of Finance at Acme Corp",
"title": "VP of Finance",
"company": "Acme Corp",
"linkedin_url": "https://www.linkedin.com/in/janesmith",
"comment_text": "Great insights on AI in accounting...",
"post_url": "https://www.linkedin.com/posts/...",
"profile_image_url": "https://..."
}
Uses harvestapi~linkedin-post-comments Apify actor — ~$2 per 1,000 comments. No LinkedIn cookies or login required.
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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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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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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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