MARKET RESEARCH
Daily GitHub-trending velocity snapshot to BigQuery
Every morning, pulls the day's trending GitHub repositories, computes star/fork velocity versus the prior snapshot.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily morning schedule
- ActionFetch trending repos from GitHubGitHub
- ActionRead prior snapshot rows from BigQueryBigQuery
- LogicCompute star/fork velocity vs last snapshot
- ActionAppend timestamped rows to BigQueryBigQuery
- OutputConfirm rows written for the run
What it does
Captures a daily point-in-time snapshot of GitHub-trending repositories and lands it in BigQuery so you can chart momentum over weeks instead of guessing from a single day's leaderboard. Each run records stars, forks, language, and the delta since the last snapshot.
When to use it
Run this when you want a durable, queryable history of OSS momentum — feeding dashboards, investor memos, or competitive-landscape SQL — rather than eyeballing the GitHub trending page each morning.
How it works
- 1A scheduled trigger fires each morning at a fixed hour.
- 2A GitHub action fetches the current trending repositories with stars, forks, and primary language.
- 3A logic step reads each repo's most recent prior row from BigQuery and computes star and fork velocity (delta over hours elapsed).
- 4A logic filter drops repos with no measurable change to keep the table clean.
- 5A BigQuery action appends one timestamped row per repo into the snapshots table.
- 6The output step confirms row counts written for the run.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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