Search LinkedIn posts by keywords, sorted by engagement or date. Use when researching what people are saying about a topic on LinkedIn, finding high-engagement content, identifying thought leaders, or discovering warm leads through post engagement. Returns author, post text, reactions, comments, shares, post URL, and date. No LinkedIn cookies or login required.
npx gooseworks install --claude # Then in your agent: /gooseworks <prompt> --skill linkedin-post-research
Search LinkedIn for posts matching keywords via Apify. Returns posts sorted by engagement or date, with author info, full text, reaction counts, and direct URLs.
No LinkedIn cookies. No login. Just keywords in, posts out.
Load this skill when:
Required for searching LinkedIn posts. Set in .env:
APIFY_API_TOKEN=your_token_hereNo LinkedIn cookies, login, or session tokens needed. That's the only setup.
apimaestro/linkedin-posts-search-scraper-no-cookies Apify actoractivity_id# Single keyword search
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
--keyword "AI sourcing" --max-items 20
# Multiple keywords
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
--keyword "AI sourcing" --keyword "recruiting automation" --max-items 30
# Sort by date (most recent first)
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
--keyword "AI agents" --sort-by date_posted --max-items 20
# Output as CSV
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
--keyword "AI agents" --output csv --output-file results.csv
# Summary table
python3 skills/capabilities/linkedin-post-research/scripts/search_posts.py \
--keyword "AI agents" --output summary| Flag | Default | Description |
|---|---|---|
--keyword, -k | required | Keyword to search (can be repeated for multiple keywords) |
--max-items | 50 | Max posts to return per keyword |
--sort-by | relevance | Sort order: relevance or date_posted |
--output, -o | json | Output format: json, csv, summary |
--output-file | stdout | Write output to file |
--token | env var | Apify API token (overrides APIFY_API_TOKEN env var) |
--timeout | 120 | Max seconds to wait for Apify run |
Actor: apimaestro/linkedin-posts-search-scraper-no-cookies
API call:
curl -X POST "https://api.apify.com/v2/acts/apimaestro~linkedin-posts-search-scraper-no-cookies/runs?token=$APIFY_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"keyword": "AI agents",
"maxItems": 50,
"sortBy": "date_posted"
}'Polling for results:
# Check run status
curl "https://api.apify.com/v2/acts/apimaestro~linkedin-posts-search-scraper-no-cookies/runs/{RUN_ID}?token=$APIFY_API_TOKEN"
# When status is SUCCEEDED, fetch results
curl "https://api.apify.com/v2/datasets/{DATASET_ID}/items?token=$APIFY_API_TOKEN"{
"author": "Jane Smith",
"author_headline": "VP of Sales at Acme Corp",
"author_profile_url": "https://www.linkedin.com/in/janesmith",
"keyword": "AI sourcing",
"reactions": 142,
"comments": 28,
"shares": 12,
"reactions_by_type": {"LIKE": 100, "EMPATHY": 30, "PRAISE": 12},
"date": "2026-04-01",
"post_preview": "First 200 chars of the post text...",
"full_text": "Complete post text...",
"url": "https://www.linkedin.com/posts/...",
"activity_id": "7447040447966826496",
"hashtags": ["#AI", "#sales"],
"is_repost": false,
"content_type": "text"
}| Column | Description |
|---|---|
| author | Post author name |
| author_headline | Author's LinkedIn headline |
| author_profile_url | Author's LinkedIn profile URL |
| keyword | Which search keyword matched |
| reactions | Total reaction count |
| comments | Comment count |
| shares | Share count |
| date | Post date (YYYY-MM-DD) |
| post_preview | First ~200 characters |
| url | Direct link to the LinkedIn post |
| activity_id | Unique post identifier |
| hashtags | Comma-separated hashtags |
Apify charges per compute usage. ~50 posts costs approximately $0.01-0.05 depending on the actor's pricing tier. The script prints cost info after each run.
| Error | Fix |
|---|---|
APIFY_API_TOKEN not set | Ask user to add it to .env |
| Apify run fails or times out | Retry once. If still fails, try a broader keyword. |
| 0 results | Keyword may be too specific. Try broader terms. |
Diagnose Meta Ads campaign performance using Meta's actual system mechanics — Breakdown Effect, Learning Phase, Auction Overlap, Pacing, and Creative Fatigue — and produce structured, testable recommendations that avoid judging segments by average CPA instead of marginal efficiency.
Pre-flight policy check for Meta ads. Takes ad copy plus advertiser context, resolves and fetches the relevant Meta transparency-center policy pages at runtime, and returns a Pass / Fix Required / Block verdict with cited findings and rewrites.
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.