Topical Series
Deal Sourcing Workflow
Practical sourcing playbooks, pre-seed, seed, Series A, that combine GitHub signals with the rest of an investor's stack.
Articles in this series (4)
The series runs 2026-04-05 through 2026-04-12, 4 posts so far, newest first below. The spread of publish dates is the cadence: the pillar is maintained, not a one-off essay collection.
Pre-Seed Deal Sourcing with GitHub Data: A Practical Guide
How to use GitHub engineering signals to find pre-seed startups before they raise. Covers what pre-seed activity looks like on GitHub, signal patterns, and a step-by-step sourcing workflow.
Series A Signals: What GitHub Data Reveals About Growth-Stage Startups
Series A startups show distinctive GitHub patterns: infrastructure buildout, rapid contributor growth, and platform expansion. Learn what these signals mean for investors evaluating growth-stage deals.
How to Source Startup Deals Before They Appear on Crunchbase
Crunchbase tells you what already happened. Learn three approaches to finding startups before they raise - using GitHub signals, community sourcing, and hiring data as leading indicators.
A Weekly Deal Sourcing Workflow Using Engineering Signals
A 30-minute weekly workflow for investors who want to use GitHub engineering signals for deal sourcing. Step-by-step process: check rankings, screen startups, verify signals, and build a pipeline.
How this pillar fits together
"Deal Sourcing Workflow" is one of 10 topical series on the site, and its scope is deliberately narrow: practical sourcing playbooks, pre-seed, seed, series a, that combine github signals with the rest of an investor's stack. The keyword set (deal sourcing, pre-seed sourcing, Series A sourcing, startup discovery) marks the edges of that scope.
Each post in the grid above covers one slice of that scope, and together the 4 posts sequence from measurement (what the signals are) to workflow (what an investor does with them). Read them in publish order if you are new to the series; jump to the newest if you already run a sourcing stack.
The pillar interlocks with the dataset itself: every claim in the series cites the same weekly-recomputed methodology and the same machine-readable feeds, so the essays age as data updates rather than as opinions.
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