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docs: add CITATION.cff for proper academic citation
Added a CFF file for GitVoyant software citation including metadata, authors, keywords, and preferred citation format. Signed-off-by: Jesse Moses <90336421+Cre4T3Tiv3@users.noreply.github.com>
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CITATION.cff

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cff-version: 1.2.0
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message: "If you use this software, please cite it as below."
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type: software
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title: "GitVoyant: Temporal Git Workflow Analysis with Multi-Scale Pattern Detection"
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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."
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authors:
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- family-names: "Moses"
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given-names: "Jesse"
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orcid: "https://orcid.org/0009-0006-0322-7974"
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affiliation: "ByteStack Labs"
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repository-code: "https://github.com/Cre4T3Tiv3/gitvoyant"
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url: "https://github.com/Cre4T3Tiv3/gitvoyant"
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keywords:
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- temporal analysis
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- Git analytics
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- pattern recognition
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- artificial intelligence
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- Claude AI
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- time series analysis
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- workflow optimization
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- repository metrics
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- development velocity
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- software engineering
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- anomaly detection
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- trend analysis
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license: Apache-2.0
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version: 0.1.0
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date-released: 2025-07-15
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preferred-citation:
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type: software
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title: "GitVoyant: Temporal Git Workflow Analysis with Multi-Scale Pattern Detection"
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authors:
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- family-names: "Moses"
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given-names: "Jesse"
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orcid: "https://orcid.org/0009-0006-0322-7974"
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affiliation: "ByteStack Labs"
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year: 2025
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url: "https://github.com/Cre4T3Tiv3/gitvoyant"
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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."

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