Linkedin Post Research
Search LinkedIn posts by keywords using Crustdata API directly, deduplicate and sort by engagement. Outputs to CSV or JSON. Use when researching LinkedIn content around specific topics.
by Athina AInpx gooseworks install --claude # Then in your agent: /gooseworks <prompt> --skill linkedin-post-research
LinkedIn Post Research
Search LinkedIn for posts matching keywords via the Crustdata API, deduplicate results, and output sorted by engagement.
Quick Start
Requires requests and CRUSTDATA_API_TOKEN environment variable.
# Single keyword search
python3 skills/linkedin-post-research/scripts/search_posts.py \
--keyword "AI sourcing" \
--time-frame past-week
# Multiple keywords, output CSV
python3 skills/linkedin-post-research/scripts/search_posts.py \
--keyword "talent sourcing tools" \
--keyword "recruiting automation" \
--keyword "AI sourcing" \
--time-frame past-week \
--output csv \
--output-file results.csv
# Keywords from file, multiple pages
python3 skills/linkedin-post-research/scripts/search_posts.py \
--keywords-file keywords.txt \
--time-frame past-week \
--pages 3 \
--output json \
--output-file results.json
# Summary only (prints to stderr)
python3 skills/linkedin-post-research/scripts/search_posts.py \
--keyword "recruiting stack" \
--output summaryInputs
- Keywords: Search terms — pass via
--keywordflags or--keywords-file(one per line) - Time frame:
past-day,past-week,past-month,past-quarter,past-year,all-time(default:past-month) - Pages: Number of pages per keyword (default: 1, ~5 posts/page). Max 20.
- Sort by:
relevanceordate(default:relevance)
CLI Reference
| Flag | Default | Description |
|---|---|---|
--keyword, -k | required | Keyword to search (repeatable) |
--keywords-file, -f | — | File with one keyword per line (lines starting with # are ignored) |
--time-frame, -t | past-month | Time filter |
--sort-by, -s | relevance | Sort order |
--pages, -p | 1 | Pages per keyword (~5 posts per page) |
--limit, -l | — | Exact number of posts per API call (1-100) |
--output, -o | json | Output format: json, csv, summary |
--output-file | stdout | Write output to file |
--max-workers | 6 | Max parallel API calls |
How It Works
- Takes keywords via CLI args or file
- Calls Crustdata's
/screener/linkedin_posts/keyword_searchAPI in parallel for all (keyword × page) combinations - Deduplicates posts across keywords by
backend_urn - Sorts by
total_reactionsdescending - Outputs JSON, CSV, or summary
Output Schema (JSON)
{
"author": "Jane Smith",
"keyword": "AI sourcing",
"reactions": 142,
"comments": 28,
"date": "2026-02-20",
"post_preview": "First 200 chars of the post text...",
"url": "https://www.linkedin.com/posts/...",
"backend_urn": "urn:li:activity:123456789",
"num_shares": 12,
"reactions_by_type": "{\"LIKE\": 100, \"EMPATHY\": 30, \"PRAISE\": 12}",
"is_repost": false
}Output Columns (CSV)
| Column | Description |
|---|---|
| author | LinkedIn post author name |
| keyword | Which search keyword matched this post |
| reactions | Total reaction count |
| comments | Total comment count |
| date | Post date (YYYY-MM-DD) |
| post_preview | First ~200 characters of the post |
| url | Direct link to the LinkedIn post |
| backend_urn | Unique post identifier |
| num_shares | Number of shares |
Crustdata API Details
Endpoint: GET https://api.crustdata.com/screener/linkedin_posts/keyword_search
Parameters:
keyword— Search termpage— Page number (1-based, ~5 posts per page)sort_by—relevanceordatedate_posted— Time filter (past-day, past-week, etc.)limit— Exact number of posts to return (1-100)
Auth: Authorization: Token <CRUSTDATA_API_TOKEN>
Credit usage: 1 credit per post returned.
Rate limits: Searches run in parallel (default 6 workers). The script handles 429 responses with automatic retry.
Environment Variables
| Variable | Required | Description |
|---|---|---|
CRUSTDATA_API_TOKEN | Yes | Crustdata API token |
Cost
~1 credit per post returned. Searching 10 keywords × 1 page = ~50 posts = ~50 credits.
Chaining with Other Skills
After getting the post list, common next steps:
- Extract commenters →
linkedin-commenter-extractor— pass post URLs to find warm leads - Research authors → web search on high-engagement post authors
- Qualify leads →
lead-qualification— score extracted people against ICP
Example pipeline:
# Step 1: Search posts
python3 skills/linkedin-post-research/scripts/search_posts.py \
--keyword "AI sourcing" --keyword "recruiting automation" \
--time-frame past-week --output json --output-file posts.json
# Step 2: Extract post URLs for commenter extraction
cat posts.json | python3 -c "
import json, sys
posts = json.load(sys.stdin)
# Filter: keep posts with 5+ comments
for p in posts:
if p['comments'] >= 5:
print(p['url'])
" > post_urls.txt
# Step 3: Extract commenters from those posts
while read url; do
python3 skills/linkedin-commenter-extractor/scripts/extract_commenters.py --post-url "\$url" --output json
done < post_urls.txtWhat's included
--keyword flags or --keywords-file (one per line)past-day, past-week, past-month, past-quarter, past-year, all-time (default: past-month)relevance or date (default: relevance)keyword — Search termLinks
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