Agent skill

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.

View SKILL.md on GitHub Repository
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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/linkedin-post-research

SKILL.md

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.

bash
# 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 summary

Inputs

  • Keywords: Search terms — pass via --keyword flags 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: relevance or date (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

  1. Takes keywords via CLI args or file
  2. Calls Crustdata's /screener/linkedin_posts/keyword_search API in parallel for all (keyword × page) combinations
  3. Deduplicates posts across keywords by backend_urn
  4. Sorts by total_reactions descending
  5. Outputs JSON, CSV, or summary

Output Schema (JSON)

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 term
  • page — Page number (1-based, ~5 posts per page)
  • sort_by — relevance or date
  • date_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:

  1. Extract commenters → linkedin-commenter-extractor — pass post URLs to find warm leads
  2. Research authors → web search on high-engagement post authors
  3. Qualify leads → lead-qualification — score extracted people against ICP

Example pipeline:

bash
# 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.txt

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