Social Intelligence

GitHub Activity Signals

11 signal subtypes from portfolio-level GitHub analysis. Track AI/ML investment, developer tooling adoption, platform ecosystem growth, and engineering velocity across 15M+ companies.

Social Intelligence illustration
15M+

Companies Covered

11

Signal Subtypes

Weekly

Refresh Cadence

Stars, Forks, Languages

Metrics Tracked

Social Intelligence11 subtypes · Weekly refresh

What Are GitHub Activity Signals?

GitHub is where software is built. A company's GitHub presence reveals its engineering priorities, technology bets, and development velocity in ways that no corporate announcement can match. When a company starts publishing AI/ML repositories, contributes to Kubernetes tooling, or launches a developer platform, those activities signal strategic technology investments.

Autobound's GitHub signals operate at the portfolio level, analyzing a company's entire collection of repositories rather than individual repos. We track 11 distinct signal subtypes: AI/ML investment, developer tooling, platform ecosystem, infrastructure modernization, open-source contribution velocity, star growth trends, fork activity, compliance programs, security tooling, API development, and developer community engagement.

Our signals go beyond simple repository counting. We analyze star growth rates over 90 and 180 days, fork velocity trends, programming language distribution, and the relationship between a company's open-source activity and its commercial products. A company whose developer tools are gaining rapid adoption is likely investing in platform growth.

GitHub signals are particularly valuable for developer tool vendors, cloud platforms, and infrastructure companies. When a target account's GitHub activity shows increased Terraform usage, growing Docker adoption, or new CI/CD pipeline configurations, those are concrete signals that the engineering team is actively building and may need supporting tools and services.

Example Signal Subtypes

aiMlInvestmentdeveloperToolingplatformEcosysteminfrastructureModernizationopenSourceVelocitystarGrowthSurgecomplianceProgramsecurityToolingapiDevelopmentdeveloperCommunityforkActivity

See It in Action

Real-World Example

1

Signal Detected

Vercel's GitHub organization shows a surge in AI-related repositories — 3 new ML inference repos in 60 days, plus their Next.js star count jumping 8% to 132K. The 'aiMlInvestment' signal fires.

2

Sales Action

An AI infrastructure vendor reaches out to Vercel's CTO: 'Your GitHub activity shows a clear bet on AI-native web apps — 3 new ML repos this quarter. We power the inference layer for companies making exactly this transition. Worth 15 minutes?'

3

Result

Technical deep-dive scheduled because the outreach was grounded in observable engineering activity, not marketing press releases.

Data Schema

GitHub Signal Data Schema

GitHub signals include portfolio-level metrics, repository trends, technology categorization, and growth velocity measurements.

{
  "signal_id": "c9d34e56-7b2a-4f19-8c83-1e5d9f0a2b67",
  "signal_type": "github",
  "signal_subtype": "developerTooling",
  "detected_at": "2026-01-12T13: 20: 44Z",
  "association": "company",
  "company": {
    "name": "Prometheus",
    "domain": "prometheus.io",
    "linkedin_url": "linkedin.com/company/prometheus-monitoring",
    "industries": ["Open Source Monitoring"]
  },
  "data": {
    "summary": "Launches compliance program to standardize its ecosystem with PromQL conformance.",
    "detail": "Prometheus released a new compliance testing framework to ensure PromQL compatibility...",
    "relevance": 0.68,
    "confidence": "high",
    "sentiment": "positive",
    "repo_count": 42,
    "total_stars": 58400,
    "star_growth_90d": 0.03,
    "star_growth_180d": 0.05,
    "primary_languages": ["Go", "TypeScript"],
    "signal_category": "platform_ecosystem",
    "technologies_mentioned": ["PromQL", "Kubernetes", "Grafana"],
    "sales_relevance": "Expanding platform ecosystem creates integration opportunities"
  }
}
GCS Bucket: gs://autobound-github-v1/Formats: JSONL + ParquetRefresh: Weekly

Use Cases

How Sales Teams Use GitHub Activity Signals

1

Developer Tool Sales

Identify companies whose engineering teams are actively building with technologies your product supports. GitHub activity reveals technology adoption before any official procurement process begins.

2

Cloud Platform Partnerships

Track companies with growing open-source portfolios that could benefit from cloud infrastructure partnerships. Repository growth and star velocity indicate expanding engineering operations.

3

AI/ML Investment Detection

Detect companies investing in AI and machine learning by tracking ML framework usage, model repository creation, and data pipeline tooling in their GitHub portfolios.

4

DevOps and Infrastructure Sales

Monitor for companies adopting containerization, orchestration, and CI/CD tooling in their repositories. These adoption patterns indicate infrastructure modernization initiatives.

FAQ

Frequently Asked Questions

What makes GitHub activity signals different from other intent data?
GitHub signals reveal engineering investment at the portfolio level — which technologies a company is building with, how fast their developer ecosystem is growing, and where they're investing R&D resources. This is observable behavior, not self-reported data.
How many companies have GitHub activity signals?
Our GitHub signal coverage spans 15M+ companies, making it one of our broadest datasets. We track organizational GitHub accounts, repository activity, star growth, fork velocity, and new project launches.
What are the 11 GitHub signal subtypes?
The subtypes include AI/ML investment, developer tooling, platform ecosystem, infrastructure modernization, open-source velocity, star growth surge, compliance programs, security tooling, API development, developer community, and fork activity.
Can I detect when a company starts investing in AI or ML?
Yes. The aiMlInvestment subtype specifically detects when companies create new AI/ML-related repositories, adopt ML frameworks, or show increasing activity in AI-adjacent projects. This is a strong signal for AI infrastructure and tooling vendors.

How It Works

From Raw Data to Actionable Signals

Autobound transforms unstructured data into structured, scored signals your team can act on immediately.

1

Autobound Ingests

Raw data from LinkedIn API, Glassdoor, GitHub, Reddit, G2 is continuously collected and normalized.

2

AI Extracts & Scores

ML models extract signal subtypes with relevance scoring, confidence levels, and sentiment analysis.

3

You Receive

Structured JSONL signals delivered via REST API, GCS Push, Generate Insights API, or Flat File export.

Flexible Delivery

Access GitHub Activity Signals Your Way

GitHub Activity Signals are available through all Autobound delivery methods. Choose the approach that fits your infrastructure.

REST API

REST API

Real-time access with subtype filtering

300 req/min
GCS Push

GCS Push

Automated delivery to your bucket

JSONL + Parquet
Generate Insights API

Generate Insights API

On-demand LLM-ranked insights

AI relevance scoring
Flat File

Flat File

Bulk exports for data warehouses

CSV, JSON, Parquet

Related Signals

Combine for Deeper Intelligence

GitHub Activity Signals become more powerful when combined with related signal types. Cross-referencing multiple signals reveals patterns that no single source can surface alone.

3 vendors consolidated
By consolidating three data vendors into Autobound's Generate Insights API, we added 100+ new signal types and saved 4 months of engineering time.

AiSDR Team

Engineering, AiSDR

API Documentation

Explore the API

Full schema reference, sample requests, and integration guides. Everything you need to start consuming GitHub Activity Signals in your application.

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GitHub Activity Signals?

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