hawk-ai-aml/github-actions
Aug 2025 – Sep 2025
2 Months active
Languages Used
JavaScriptPythonShellTypeScriptYAMLBash
Technical Skills
AI/ML IntegrationAPI IntegrationAutomationCI/CDCode AnalysisCode Metrics
Jonathan Kaleve
PROFILE
Jonathan Kaleve
Developed and enhanced automated risk assessment workflows for the hawk-ai-aml/github-actions repository, focusing on AI-driven pull request triage and security governance. Leveraged GitHub Actions, TypeScript, and Python to build a system that scores PR risk using AI inference and code metrics, with weighted scoring and multi-file support. Integrated Prometheus for metrics collection and observability, enabling dashboards and trend analysis. Improved reliability by adding fallback logic for AI models and consolidated LLM calls to streamline processing. Extended security risk assessment across repositories by enhancing the enforce-labels action, reducing manual review effort and centralizing risk evaluation for improved governance coverage.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
3
Lines of code
62,741
Activity Months2
Your Network
29 peopleWork History
September 2025
3 Commits • 2 Features
Sep 1, 2025Month: 2025-09 — Key outcomes in hawk-ai-aml/github-actions: two major features delivered to strengthen risk assessment and governance. The first feature enhances AI risk assessment reliability by adding a fallback to a weaker but robust model when the primary model fails, and consolidates all LLM calls into the main risk assessment workflow to streamline processing. The second feature enables Security Risk Assessment (SRA) across all repositories by integrating SRA controls into the enforce-labels GitHub Action, with configurable inputs to enable/disable, enforce results, specify risk reference, and load risk assessment configuration. Together, these efforts improve risk visibility, reliability, and governance coverage, while reducing processing overhead and maintenance burden.
3 Commits • 2 Features
Sep 1, 2025Month: 2025-09 — Key outcomes in hawk-ai-aml/github-actions: two major features delivered to strengthen risk assessment and governance. The first feature enhances AI risk assessment reliability by adding a fallback to a weaker but robust model when the primary model fails, and consolidates all LLM calls into the main risk assessment workflow to streamline processing. The second feature enables Security Risk Assessment (SRA) across all repositories by integrating SRA controls into the enforce-labels GitHub Action, with configurable inputs to enable/disable, enforce results, specify risk reference, and load risk assessment configuration. Together, these efforts improve risk visibility, reliability, and governance coverage, while reducing processing overhead and maintenance burden.
September 2025
August 2025
9 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary — hawk-ai-aml/github-actions Key features delivered: - Automated Risk Assessment Workflow for PRs with AI inference and metrics: GitHub Actions-based workflow that scores PR risk using AI inferences and metrics (code churn, complexity), supports multi-file configurations, and uses weighted scoring to improve accuracy. Includes refined prompts and simplified config to emphasize core business value. - Observability and metrics: Instrumented the workflow with metrics collection via Prometheus Pushgateway, enabling dashboards and trend analysis. - Workflow scope and prompt refinements: Consolidated changes to highlight core value; removed lower-impact metrics and interactions to streamline PR risk assessment. Bugs fixed and improvements: - Fixed weight calculation logic to ensure accurate scoring (DC-2185). - Do not execute risk assessment on draft PRs (DC-432) to avoid noise. - Remove logChurn and increase code churn weight to improve signal fidelity (DC-433). - Remove Halstead Complexity metric (DC-431) to simplify metric set. - Remove rollback plan question (DC-434) and reformulate version upgrade question (DC-435) for clearer prompts. - Consider only added and removed lines for metrics and prompts (DC-436). Impact and accomplishments: - Faster, more reliable PR triage with AI-assisted risk scores and tiering based on actionable metrics. - Improved developer velocity by reducing manual review effort for low-risk PRs. - Enhanced observability and governance through metrics dashboards and streamlined prompts. Technologies/skills demonstrated: - GitHub Actions, AI inference, Prometheus Pushgateway, metrics instrumentation, multi-file workflow configuration, weighted scoring algorithms, prompt/config design, PR lifecycle awareness.
August 2025
9 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary — hawk-ai-aml/github-actions Key features delivered: - Automated Risk Assessment Workflow for PRs with AI inference and metrics: GitHub Actions-based workflow that scores PR risk using AI inferences and metrics (code churn, complexity), supports multi-file configurations, and uses weighted scoring to improve accuracy. Includes refined prompts and simplified config to emphasize core business value. - Observability and metrics: Instrumented the workflow with metrics collection via Prometheus Pushgateway, enabling dashboards and trend analysis. - Workflow scope and prompt refinements: Consolidated changes to highlight core value; removed lower-impact metrics and interactions to streamline PR risk assessment. Bugs fixed and improvements: - Fixed weight calculation logic to ensure accurate scoring (DC-2185). - Do not execute risk assessment on draft PRs (DC-432) to avoid noise. - Remove logChurn and increase code churn weight to improve signal fidelity (DC-433). - Remove Halstead Complexity metric (DC-431) to simplify metric set. - Remove rollback plan question (DC-434) and reformulate version upgrade question (DC-435) for clearer prompts. - Consider only added and removed lines for metrics and prompts (DC-436). Impact and accomplishments: - Faster, more reliable PR triage with AI-assisted risk scores and tiering based on actionable metrics. - Improved developer velocity by reducing manual review effort for low-risk PRs. - Enhanced observability and governance through metrics dashboards and streamlined prompts. Technologies/skills demonstrated: - GitHub Actions, AI inference, Prometheus Pushgateway, metrics instrumentation, multi-file workflow configuration, weighted scoring algorithms, prompt/config design, PR lifecycle awareness.
Activity
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Quality Metrics
Correctness85.8%
Maintainability85.0%
Architecture84.2%
Performance80.0%
AI Usage41.8%
Skills & Technologies
Programming Languages
BashJavaScriptPythonShellTypeScriptYAML
Technical Skills
AI IntegrationAI/ML IntegrationAPI IntegrationAutomationCI/CDCode AnalysisCode MetricsCode RefactoringDevOpsGitGitHub ActionsJSONJavaScriptJavaScript DevelopmentLLM Integration
Repositories Contributed To
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