How GitiGit Scores GitHub Repositories
GitiGit calculates objective repository health and architecture scores by inspecting verifiable maintainer telemetry, security policies, dependency graphs, and code organization.
1. Evaluation Philosophy: Telemetry vs. Popularity
Traditional open-source discovery relies heavily on GitHub stars. However, stars are a lagging metric of historical mindshare rather than current maintainer health or security posture. GitiGit strictly decouples Ecosystem Adoption (Popularity) from Engineering Quality & Maintenance Health.
Commit frequency, release cadence, issue response latency, lockfile reproducibility, and security scanning configs are computed deterministically from raw API data.
AI models synthesize architectural trade-offs and use-case recommendations strictly constrained by verifiable evidence nodes, preventing hallucinated conclusions.
2. Empirical Data Sources
Every score is backed by observable data collected from official developer infrastructure:
- 🐙GitHub REST & GraphQL APIs:Commit history over 52 weeks, git version tags, pull request resolution velocity, issue closure cadence, active maintainer count, license files, and README contents.
- 📦Package Registries (npm, PyPI, Crates.io):Manifest dependency declarations, semver publishing cadences, and install command verification.
- 🛡️Security & Vulnerability Databases:GitHub Advisory Database, CVE registries, Dependabot bot presence, CodeQL / SAST workflow detection, and SECURITY.md disclosure policies.
3. The 6 Scoring Pillars & Mathematical Weights
The composite GitiGit Quality Score (0–100) and Letter Grade (A+ through F) are evaluated across six core dimensions:
| Pillar | Weight | Key Signals Evaluated |
|---|---|---|
| Maintenance Health | 25% | 52-week commit consistency, recent releases, PR merge cadence, active maintainer weeks. |
| Engineering Quality | 20% | CI automated workflows, static analysis configuration, test suites, modular structure. |
| Security Posture | 20% | SECURITY.md policy, automated dependency scanners, CodeQL/SAST checks, live CVE count. |
| Documentation & Onboarding | 15% | README completeness, installation commands, code usage snippets, CONTRIBUTING guides. |
| Community Health | 10% | Contributor diversity (bus factor mitigation), governance documents, code of conduct. |
| Ecosystem Adoption | 10% | Log-scaled stars, forks network reach, open-source OSI license compliance. |
4. Rating Grades & Production Verdicts
Active maintainer commits within the last 30 days, verified security disclosures, OSI license, and CI automated testing.
Healthy architecture, but with slower release cadences (3–6 months), single-maintainer risks, or missing SAST workflows.
Inactivity exceeding 6 months, high ratio of stale unanswered issues, or unverified license terms.
Abandoned codebase (> 1 year without commits), unresolved critical CVEs, or missing essential documentation.
5. Limitations & Transparency
No automated analysis is a complete replacement for human code review. GitiGit scores reflect observable software engineering signals, but teams should always evaluate specific architectural requirements, compliance, and internal performance benchmarks before adopting a third-party dependency into production systems.
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