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Get comprehensive LinkedIn post data from any post URL, including author details, content, hashtags, media, engagement metrics, and metadata for analytics and intelligence workflows.
Get comprehensive LinkedIn post data from any post URL, including author details, content, hashtags, media, engagement metrics, and metadata for analytics and intelligence workflows.
Turn a LinkedIn post URL into a complete structured record in seconds — enrichment hop before comment or reaction jobs.
On Scrapy.io, Thunderbolt LinkedIn Post Details Extractor by Thunderbolt is built for linkedin post scraper workflows — run from the Store, schedule recurring jobs, or POST to thunderbolt.p.scrapy.io from your stack.
post_url, input, or post_urlsLinkedIn is the system of record for B2B identity — profiles, company updates, and post feeds that power ABM and competitive intelligence. A linkedin post scraper pipeline on Scrapy.io converts public URLs into structured JSON without maintaining cookie-based browser farms.
Thunderbolt LinkedIn Post Parser is built for API-first batch jobs with caps you control. Schedule runs, call thunderbolt.p.scrapy.io, or chain Thunderbolt LinkedIn tools on the same Scrapy.io billing account.
This linkedin post scraper on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover Thunderbolt LinkedIn Post Parser while researching linkedin post scraper and related terms such as . This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of Thunderbolt LinkedIn Post Parser:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetthunderbolt.p.scrapy.io/linkedin-post-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Thunderbolt LinkedIn Post Parser so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Paste one or more public LinkedIn post URLs in post_urls.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"post_urls": [
"https://www.linkedin.com/posts/linkedin_add-job-preferences-activity-7208418413906464769-P2hn"
]
}| Field | Type | Required | Description |
|---|---|---|---|
post_urls | string[] | Yes | List of LinkedIn post URLs to scrape. Enter one URL per line. Also accepted: post_url (Single post URL alias); or input (Single post URL alias). |
Results appear in the Scrapy.io run Output tab. Posts and Errors tables in the run output.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"post_url": "https://www.linkedin.com/posts/example_activity-123",
"text": "Excited to share our latest update...",
"author": { "name": "Alex Kim", "profile_url": "https://www.linkedin.com/in/alex-kim/" },
"reactions": 142,
"comments": 28,
"reposts": 12
}Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
fetch post meta before deeper scrapers
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
snapshot a post at multiple points in time
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
store public statements with metadata
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
join engagement metrics to campaign IDs
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
Single-call post parsing at Thunderbolt speed — prefer us when post URLs must become warehouse rows without a multi-step browser workflow.
Thunderbolt LinkedIn Post Parser is rarely the only step in a data workflow. Teams typically:
See Related Thunderbolt tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersThunderbolt LinkedIn Post Parser separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Thunderbolt tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Facebook, LinkedIn, Instagram, or X extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. Thunderbolt LinkedIn Post Parser on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Thunderbolt listings add API-first, warehouse-ready documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
Thunderbolt LinkedIn Post Parser runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run Thunderbolt LinkedIn Post Parser once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
thunderbolt.p.scrapy.io on a queueThunderbolt tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://thunderbolt.p.scrapy.io/linkedin-post-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"post_urls": [
"https://www.linkedin.com/posts/example_activity-1234567890-abcd"
]
}'Bulk runs via the scraper endpoint:
curl -X POST "https://thunderbolt.p.scrapy.io/linkedin-post-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"post_urls": [
"https://www.linkedin.com/posts/example_activity-1234567890-abcd"
]
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Thunderbolt LinkedIn Post Parser is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on Thunderbolt LinkedIn Post Parser for MCP connection snippets.
| Tool | Best for |
|---|---|
| LinkedIn Profile Lead Scraper | Thunderbolt companion tool — overview |
| Instagram Profile Bulk Extractor | Thunderbolt companion tool — overview |
| X Profile Insights Extractor | Thunderbolt companion tool — overview |
Batch post URLs in API jobs with inline error reporting
Dedupe on post_url when merging nightly snapshots
Validate URL shape before large production runs
Inspect per-row errors in the Errors output view instead of failing an entire batch
Facebook-heavy runs failing in a pattern? Use Issues on this Thunderbolt listing and attach your run ID plus one post, page, or search query
Thunderbolt LinkedIn Post Parser collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$1.20 per 1,000 posts — billed per successful row on Scrapy.io. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://thunderbolt.p.scrapy.io/linkedin-post-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query Thunderbolt LinkedIn Post Parser from AI assistants and agent workflows.
Thunderbolt focuses on Meta and social discovery workflows — leave a Reviews note if this actor saved you from browser scraping and which Facebook surface you use daily.
Turn a LinkedIn post URL into a complete structured record in seconds — enrichment hop before comment or reaction jobs.
On Scrapy.io, Thunderbolt LinkedIn Post Details Extractor by Thunderbolt is built for linkedin post scraper workflows — run from the Store, schedule recurring jobs, or POST to thunderbolt.p.scrapy.io from your stack.
post_url, input, or post_urlsLinkedIn is the system of record for B2B identity — profiles, company updates, and post feeds that power ABM and competitive intelligence. A linkedin post scraper pipeline on Scrapy.io converts public URLs into structured JSON without maintaining cookie-based browser farms.
Thunderbolt LinkedIn Post Parser is built for API-first batch jobs with caps you control. Schedule runs, call thunderbolt.p.scrapy.io, or chain Thunderbolt LinkedIn tools on the same Scrapy.io billing account.
This linkedin post scraper on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover Thunderbolt LinkedIn Post Parser while researching linkedin post scraper and related terms such as . This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of Thunderbolt LinkedIn Post Parser:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetthunderbolt.p.scrapy.io/linkedin-post-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Thunderbolt LinkedIn Post Parser so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Paste one or more public LinkedIn post URLs in post_urls.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"post_urls": [
"https://www.linkedin.com/posts/linkedin_add-job-preferences-activity-7208418413906464769-P2hn"
]
}| Field | Type | Required | Description |
|---|---|---|---|
post_urls | string[] | Yes | List of LinkedIn post URLs to scrape. Enter one URL per line. Also accepted: post_url (Single post URL alias); or input (Single post URL alias). |
Results appear in the Scrapy.io run Output tab. Posts and Errors tables in the run output.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"post_url": "https://www.linkedin.com/posts/example_activity-123",
"text": "Excited to share our latest update...",
"author": { "name": "Alex Kim", "profile_url": "https://www.linkedin.com/in/alex-kim/" },
"reactions": 142,
"comments": 28,
"reposts": 12
}Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
fetch post meta before deeper scrapers
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
snapshot a post at multiple points in time
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
store public statements with metadata
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
join engagement metrics to campaign IDs
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
Single-call post parsing at Thunderbolt speed — prefer us when post URLs must become warehouse rows without a multi-step browser workflow.
Thunderbolt LinkedIn Post Parser is rarely the only step in a data workflow. Teams typically:
See Related Thunderbolt tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersThunderbolt LinkedIn Post Parser separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Thunderbolt tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Facebook, LinkedIn, Instagram, or X extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. Thunderbolt LinkedIn Post Parser on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Thunderbolt listings add API-first, warehouse-ready documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
Thunderbolt LinkedIn Post Parser runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run Thunderbolt LinkedIn Post Parser once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
thunderbolt.p.scrapy.io on a queueThunderbolt tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://thunderbolt.p.scrapy.io/linkedin-post-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"post_urls": [
"https://www.linkedin.com/posts/example_activity-1234567890-abcd"
]
}'Bulk runs via the scraper endpoint:
curl -X POST "https://thunderbolt.p.scrapy.io/linkedin-post-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"post_urls": [
"https://www.linkedin.com/posts/example_activity-1234567890-abcd"
]
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Thunderbolt LinkedIn Post Parser is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on Thunderbolt LinkedIn Post Parser for MCP connection snippets.
| Tool | Best for |
|---|---|
| LinkedIn Profile Lead Scraper | Thunderbolt companion tool — overview |
| Instagram Profile Bulk Extractor | Thunderbolt companion tool — overview |
| X Profile Insights Extractor | Thunderbolt companion tool — overview |
Batch post URLs in API jobs with inline error reporting
Dedupe on post_url when merging nightly snapshots
Validate URL shape before large production runs
Inspect per-row errors in the Errors output view instead of failing an entire batch
Facebook-heavy runs failing in a pattern? Use Issues on this Thunderbolt listing and attach your run ID plus one post, page, or search query
Thunderbolt LinkedIn Post Parser collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$1.20 per 1,000 posts — billed per successful row on Scrapy.io. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://thunderbolt.p.scrapy.io/linkedin-post-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query Thunderbolt LinkedIn Post Parser from AI assistants and agent workflows.
Thunderbolt focuses on Meta and social discovery workflows — leave a Reviews note if this actor saved you from browser scraping and which Facebook surface you use daily.