We extract class catalogues, instructor profiles, student projects, and engagement metrics from Skillshare. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Classes objects from skillshare.com. All fields typed and schema-versioned.
"class_id": "84729103", "title": "Graphic Design Basics: Core Principles for Visual Design", "instructor_name": "Ellen Lupton", "duration_minutes": 35, "student_count": 142851, "is_original": true, "is_staff_pick": true, "skill_tags": "['Graphic Design', 'Typography', 'Creative']"
| # | class_id | title | url | instructor_name | instructor_id | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Instructors objects from skillshare.com. All fields typed and schema-versioned.
"instructor_id": "1948271", "name": "Aaron Draplin", "headline": "Graphic Designer, Draplin Design Co.", "follower_count": 89412, "total_students": 215491, "classes_taught": 8, "social_links": "['instagram.com/draplin', 'draplin.com']"
| # | instructor_id | name | profile_url | headline | bio | follower_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lessons objects from skillshare.com. All fields typed and schema-versioned.
"lesson_id": "491029", "class_id": "84729103", "title": "Introduction to Typography", "sequence_number": 2, "duration_seconds": 412, "discussion_count": 48, "resources_included": true
| # | lesson_id | class_id | title | sequence_number | duration_seconds | video_preview_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Student Projects objects from skillshare.com. All fields typed and schema-versioned.
"project_id": "948172", "class_id": "84729103", "student_name": "Sarah Jenkins", "project_title": "My First Brand Identity", "like_count": 124, "comment_count": 14, "publish_date": "2024-03-12T14:22:00Z"
| # | project_id | class_id | student_name | student_id | project_title | project_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from skillshare.com. All fields typed and schema-versioned.
"review_id": "104827", "class_id": "84729103", "rating_expectations": "Exceeded", "rating_clarity": "High", "rating_actionability": "High", "helpful_votes": 42, "post_date": "2024-02-18T09:11:00Z"
| # | review_id | class_id | student_id | rating_expectations | rating_clarity | rating_actionability |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Skillshare scraper navigates the entire platform taxonomy: class metadata, instructor metrics, student projects, and engagement data - with automated pagination and dynamic content hydration built in.
Extract class titles, duration, student counts, project counts, and required skill tags across all main categories and subcategories.
Capture instructor bios, follower counts, total students taught, and external social links to identify top creators.
Scrape project galleries including project titles, like counts, comment metrics, and image URLs to gauge course engagement.
Map the internal structure of classes, tracking sequence numbers, lesson durations, and resource availability.
Track platform endorsements by capturing 'Skillshare Original' and 'Staff Pick' badges across the catalogue.
Extract granular review data including clarity, actionability, and expectation ratings from verified students.
Map the entire tag ecosystem to understand how skills are categorised and discover emerging topics.
Extract questions, answers, and general engagement metrics from class discussion tabs.
Run pipelines at daily or weekly cadences to track follower growth and new class publications via diffing.
Brief in. Clean data out.
Provide categories, instructor IDs, or skill tags. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for skillshare.com.
Schema validation, null-rate checks, metric outlier detection, and sample payloads before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Skillshare relies on dynamic React components and strict rate limits. Here is how we maintain data flow.
Skillshare employs edge protection that blocks aggressive datacenter IPs. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing.
Class modules, student projects, and dynamic metric counters require JavaScript execution. We run full Playwright browser sessions to trigger lazy-loads and hydrate the DOM before extraction.
Skillshare updates its UI components frequently. Our strategy uses multiple fallback chains per field - CSS selectors, XPath, and internal JSON state extraction - ensuring high data completeness.
Popular classes have thousands of student projects spread across infinite-scroll interfaces. We automate the scrolling and API interception to extract the complete gallery without memory bloat.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing tags, and coverage drops - responding before your downstream systems are affected.
EdTech platforms track Skillshare's catalogue size, category growth, and instructor acquisition rates to benchmark their own offerings.
Agencies and competing platforms identify top-performing instructors by tracking student counts, follower growth, and project engagement.
Market researchers track rising skill tags and new class volume to identify trending topics in design, business, and technology.
Content strategists analyse highly searched topics with low course counts to identify underserved niches for new course creation.
Investors evaluate the health of the creator economy by monitoring aggregate student enrollment and active instructor metrics.
ML teams use structured course taxonomy and module sequencing data to train educational content generation models.
"Skillshare represents a massive taxonomy of the modern creator economy, mapping exactly what creative professionals are teaching and learning right now."
Extracting this data requires navigating dynamic React applications, strict rate limits, and complex pagination across thousands of user-generated projects. DataFlirt manages the proxy rotation, JavaScript execution, and schema maintenance so your team receives structured, query-ready datasets without the infrastructure overhead.
Everything supported by our skillshare.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, infinite scroll, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About skillshare.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Skillshare is generally permissible under applicable law. DataFlirt targets only public, non-authenticated course metadata, instructor profiles, and project galleries. We do not extract personal data behind logins or circumvent DRM video protections. Clients should review Skillshare's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. This prevents IP blocks and mitigates automated security challenges.
No. We extract metadata, lesson titles, duration metrics, and public project galleries. We do not bypass DRM or download premium video content.
Pipelines can be configured to run daily or weekly. The student count, follower metrics, and project counts reflect the exact numbers displayed on the platform at the time of extraction.
Yes. We maintain a time-series table per instructor, capturing changes in follower counts, total students, and new class publications from the date your pipeline starts.
Our smallest packages start at a defined category or list of instructors with weekly delivery. For full catalogue extraction, we price based on volume and delivery frequency.
Yes. We extract the metadata for student projects, including titles, likes, comments, and the public URLs of the uploaded project images.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full catalogue dump or continuous tracking of top instructors and trending skills - we scope, build, and operate the pipeline. Tell us what you need.
