See what buyers already pay for. Then query it like a dataset.
Track 17,842 cleaned Fiverr demand snapshots across 274 categories. Browse the dashboard, or query the same dataset through JSON, SSE, and reusable skills.
2026-03 live snapshot
D1-backed demand layer
This is not generic trend content. The workflow turns raw marketplace exports into a backed-up, queryable dataset with category, pricing, revenue, and pain-point fields.
274
categories in the live catalog
17,842
cleaned demand snapshots
Monthly
refresh and backup cycle
4
ways to consume the data
Use the same dataset in the UI, call it like an API, stream it over SSE, or let an agent consume it through local skills.
Cluster similar pain points to see what buyers consistently need solved, then map them back to services and pricing.
Live examples
Which categories have the strongest revenue right now?
Rank current categories by average revenue and row volume to find the most commercially dense areas.
Which pain points keep repeating across categories?
Cluster similar pain points to see what buyers consistently need solved, then map them back to services and pricing.
How does one gig or seller look over time?
Inspect a snapshot in depth and then pull gig history to understand how one offer behaves across monthly captures.
Built for people who need proof, not opinions
The homepage should make the value obvious for three groups immediately: builders validating demand, operators pricing work, and agents consuming structured data.
Founders validating what to build
Use real paid orders, categories, and pain points to decide whether a market exists before you commit roadmap and capital.
Agencies and freelancers pricing real work
See how buyers describe the job, what ranges sellers charge, and which categories generate durable revenue.
Agents and analysts querying the dataset
Skip manual browsing when you need structured access. The same monthly snapshot is available through JSON, SSE, and local skills.
From monthly source exports to LLM-ready demand signals
This is not generic trend content. The workflow turns raw marketplace exports into a backed-up, queryable dataset with category, pricing, revenue, and pain-point fields.
Capture monthly source exports
Pull every Fiverr category from Exploding Insights and preserve the raw CSV files as the backup layer for each month.
Normalize into analysis-ready records
Clean services, pain points, price ranges, orders, and revenue into a consistent schema designed for filtering and model analysis.
Query through dashboard, JSON, SSE, or skill
Use the same dataset in the UI, call it like an API, stream it over SSE, or let an agent consume it through local skills.
Three ways to use the same dataset
The product should feel useful whether someone wants a visual dashboard, a programmatic endpoint, or an agent-native workflow.
Dashboard for fast browsing
Search categories, compare price bands, inspect pain points, and spot high-revenue services without writing a query.
Best for human exploration and quick validation.
Open dashboardJSON and SSE for direct access
Pull month summaries, category rollups, snapshot search results, pain-point clusters, and gig history from live endpoints.
Best for internal tools, scripts, and external consumers.
Open API pageSkills for agent workflows
Use local skills to refresh the dataset, inspect the latest month, and ask agents to analyze the same structured source.
Best for Codex, Claude Code, and other agent-based workflows.
Open skills pageExample questions the homepage should answer immediately
These are the kinds of questions the product can answer today without hand-wavy market research.
Which categories have the strongest revenue right now?
Rank current categories by average revenue and row volume to find the most commercially dense areas.
Which pain points keep repeating across categories?
Cluster similar pain points to see what buyers consistently need solved, then map them back to services and pricing.
How does one gig or seller look over time?
Inspect a snapshot in depth and then pull gig history to understand how one offer behaves across monthly captures.
Stop browsing generic trend data. Start with paid demand.
If the homepage is doing its job, a visitor should understand in seconds that this is a live market dataset they can browse, query, and hand to an agent.