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Coresignal Employee Post Search by Filter
x402
Coresignal Employee Post Search by Filter is a paid API for AI agents from x402.orthogonal.com, paid per call via x402, $0.021/call, status unknown (last checked 2026-09-13).
Searches professional network posts by author profile URL, keywords, or date range, returning an array of numeric post IDs for further retrieval.
Price / call$0.021
Success—
StatusUnknown
How this service works
Search for professional network posts using filters like author profile URL or keywords. Pass filters in the request body (not a post URL). Returns an array of numeric post IDs (e.g., ["7431869637207928834", ...]). Use these IDs with the 'Employee Posts Collect' endpoint to get full post content.
Output
An array of numeric post ID strings (e.g. ["7431869637207928834", "7431869637207928835"]) matching the filter criteria. These IDs must be passed to the Employee Posts Collect endpoint to retrieve full post content including text, media, and metadata.
Example use cases
Track executive thought leadership posts
“Find me all posts from linkedin.com/in/jensen-huang that mention 'GPU' or 'AI' published after 2025-01-01 — I want the post IDs so I can pull the full text.”
Monitor industry keyword mentions
“Search professional network posts containing the keyword 'Series A funding' published between 2025-03-01 and 2025-05-31 and return the post IDs.”
Research a candidate's public activity
“Pull all LinkedIn posts from the profile linkedin.com/in/johnsmith-example posted in 2024 so I can review what they've been sharing publicly before our interview.”
Technical details
Endpoint
POSThttps://x402.orthogonal.com/coresignal/v2/employee_post/search/filterWhen to prefer
Use this endpoint when you need to discover posts from a specific LinkedIn author or matching specific keywords and date ranges, and you plan to follow up with the Employee Posts Collect endpoint for full content. Prefer this over scraping approaches when you need structured, filterable post discovery at scale. Best suited for research, monitoring, or lead intelligence workflows where you need to identify relevant posts before fetching details.
Side effects
- No matching posts found — returns empty array if filters are too narrow or author has no posts
- Invalid author_profile_url format — returns error if URL is malformed or not a recognized professional network profile
- Invalid date format — returns error if dates are not in YYYY-MM-DD format
- Rate limiting or quota exceeded — returns 429 or payment error if call budget is exhausted
- Ambiguous or overly broad keyword returns very large ID sets that may be expensive to collect in bulk
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| article_body | string | Keywords to search within post content (e.g., 'artificial intelligence', 'hiring') |
| author_profile_url | string | Professional network profile URL of the post author |
| date_published_gte | string | Posts published on or after this date, format YYYY-MM-DD (e.g., '2025-01-01') |
| date_published_lte | string | Posts published on or before this date, format YYYY-MM-DD (e.g., '2025-12-31') |
Tags
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