gitvoyant/CITATION.cff at main · Cre4T3Tiv3/gitvoyant · GitHub

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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
type: software
title: "GitVoyant: Temporal Git Workflow Analysis with Multi-Scale Pattern Detection"
abstract: "Advanced Git repository analysis framework employing temporal decomposition and multi-scale pattern recognition to extract meaningful development insights from commit histories. Implements windowed analysis with exponential weighting, trend detection via linear regression, and anomaly identification using statistical methods (z-scores, IQR) across configurable time scales. Integrates Claude AI for contextual interpretation of temporal patterns, providing actionable recommendations for workflow optimization, development velocity improvement, and team collaboration enhancement based on scientifically-grounded temporal metrics."
authors:
  - family-names: "Moses"
    given-names: "Jesse"
    orcid: "https://orcid.org/0009-0006-0322-7974"
    affiliation: "ByteStack Labs"
repository-code: "https://github.com/Cre4T3Tiv3/gitvoyant"
url: "https://github.com/Cre4T3Tiv3/gitvoyant"
keywords:
  - temporal analysis
  - Git analytics
  - pattern recognition
  - artificial intelligence
  - Claude AI
  - time series analysis
  - workflow optimization
  - repository metrics
  - development velocity
  - software engineering
  - anomaly detection
  - trend analysis
license: Apache-2.0
version: 0.1.0
date-released: 2025-07-15
preferred-citation:
  type: software
  title: "GitVoyant: Temporal Git Workflow Analysis with Multi-Scale Pattern Detection"
  authors:
    - family-names: "Moses"
      given-names: "Jesse"
      orcid: "https://orcid.org/0009-0006-0322-7974"
      affiliation: "ByteStack Labs"
  year: 2025
  url: "https://github.com/Cre4T3Tiv3/gitvoyant"
  abstract: "A framework for Git repository analysis utilizing temporal decomposition across multiple time scales (daily, weekly, monthly) with exponentially-weighted moving averages, statistical trend detection, and outlier identification. Combines quantitative temporal metrics with Claude AI contextual reasoning to deliver scientifically-validated insights into commit patterns, development rhythms, and collaboration dynamics, enabling data-driven workflow optimization with measurable improvements in team productivity and code quality."