Automate GitHub Trending Data Collection with FireCrawl, GPT and Supabase

GitHub Trending to Supabase (Daily, Weekly, Monthly)

Who is this for?

This workflow is for developers, researchers, founders, and data analysts who want a historical dataset of GitHub Trending repositories without manual scraping. It’s ideal for building dashboards, newsletters, or trend analytics on top of a clean Supabase table.

What problem is this workflow solving?

Checking GitHub Trending by hand (daily/weekly/monthly) is repetitive and error-prone. This workflow automates collection, parsing, and storage so you can reliably track changes over time and query them from Supabase.

What this workflow does

Scrapes GitHub Trending across Daily, Weekly, and Monthly timeframes using FireCrawl. Extracts per-project fields: name, url, description, language, stars. Adds a type dimension (daily / weekly / monthly) to each row. Inserts structured results into a Supabase table for long-term storage.

Setup

Ensure you have an n8n instance (Cloud or self-hosted). Create credentials:

FireCrawl API credential (no hardcoded keys in nodes). Supabase credential (URL + Service Role / Insert-capable key). Prepare a Supabase table (example):

CREATE TABLE public.githubtrending (
id bigint GENERATED ALWAYS AS IDENTITY NOT NULL,
created_at timestamp with time zone NOT NULL DEFAULT now(),
data_date date DEFAULT now(),
url text,
project_id text,
project_desc text,
code_language text,
stars bigint DEFAULT '0'::bigint,
type text,
CONSTRAINT githubtrending_pkey PRIMARY KEY (id)
);

Import this workflow JSON into n8n. Run once to validate, then schedule (e.g., daily at 08:00).