lowtouch-ai/agent_dags
Sep 2025 – Feb 2026
6 Months active
Languages Used
Python
Technical Skills
ScriptingAPI IntegrationBackend DevelopmentData ValidationEmail ValidationAI Integration
yaqubmoosa1
PROFILE
Yaqubmoosa1
Yusuf Mohamed developed and maintained automation and observability pipelines for the lowtouch-ai/agent_dags repository, focusing on SRE reporting, workflow automation, and reliability improvements over six months. He engineered Airflow DAGs for daily and weekly SRE reports, integrating Prometheus metrics and Kubernetes data to enhance operational visibility and incident response. His work included modernizing alerting with Slack integration, refactoring code for Airflow 2.0 compatibility, and automating email notifications for system health. Using Python, Airflow, and Kubernetes, Yusuf improved data validation, streamlined reporting, and reorganized DAGs for maintainability. The solutions delivered measurable gains in reliability, security, and onboarding efficiency for stakeholders.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
60Total
Bugs
6
Commits
60
Features
24
Lines of code
17,471
Activity Months6
Your Network
28 peopleSame Organization
@ecloudcontrol.com12
Work History
February 2026
7 Commits • 2 Features
Feb 1, 2026February 2026: Delivered automated SRE reporting and DAG modernization for MBK platform, yielding enhanced observability, reliability, and maintainability. Implemented a Python-based daily SRE report generator that consumes Prometheus metrics, disseminates results via email, and includes improved IP-to-name mapping, a corrected 24-hour pod restart query, and clarified SMTP configuration. Reorganized Airflow DAGs into a dedicated SRE structure with client-specific subfolders and updated scheduling syntax to improve clarity and onboarding. Included stability improvements and code cleanups to reduce noise and ensure consistent deployments. Overall impact: faster, data-driven incident visibility and scalable SRE tooling that strengthens MBK client reliability and operational efficiency.
7 Commits • 2 Features
Feb 1, 2026February 2026: Delivered automated SRE reporting and DAG modernization for MBK platform, yielding enhanced observability, reliability, and maintainability. Implemented a Python-based daily SRE report generator that consumes Prometheus metrics, disseminates results via email, and includes improved IP-to-name mapping, a corrected 24-hour pod restart query, and clarified SMTP configuration. Reorganized Airflow DAGs into a dedicated SRE structure with client-specific subfolders and updated scheduling syntax to improve clarity and onboarding. Included stability improvements and code cleanups to reduce noise and ensure consistent deployments. Overall impact: faster, data-driven incident visibility and scalable SRE tooling that strengthens MBK client reliability and operational efficiency.
February 2026
January 2026
14 Commits • 3 Features
Jan 1, 2026January 2026 monthly summary for lowtouch-ai/agent_dags. Delivered modernization, security tightening, and automation enhancements across Airflow DAGs, with clear business value in reliability, security posture, and operational efficiency. Key features delivered include Airflow EOL Monitoring and Alerting Modernization, Notification and Security Cleanup for Alerts, and SRE TradeIdeas Daily/Weekly Report Automation and Simplification. The work encompasses migration to Airflow 2.0, Slack-based alerting, removal of Gmail/SMTP dependencies, and automation scripts with observability considerations. Additional code quality improvements included standardized variable naming, removal of deprecated provide_context usage, and correct handling of schedule_interval. Impact: improved incident response speed, reduced security risks, and streamlined reporting for leadership and on-call teams. Technologies demonstrated: Airflow 2.0, Python scripting, Slack integration, Prometheus observability, and robust configuration management.
January 2026
14 Commits • 3 Features
Jan 1, 2026January 2026 monthly summary for lowtouch-ai/agent_dags. Delivered modernization, security tightening, and automation enhancements across Airflow DAGs, with clear business value in reliability, security posture, and operational efficiency. Key features delivered include Airflow EOL Monitoring and Alerting Modernization, Notification and Security Cleanup for Alerts, and SRE TradeIdeas Daily/Weekly Report Automation and Simplification. The work encompasses migration to Airflow 2.0, Slack-based alerting, removal of Gmail/SMTP dependencies, and automation scripts with observability considerations. Additional code quality improvements included standardized variable naming, removal of deprecated provide_context usage, and correct handling of schedule_interval. Impact: improved incident response speed, reduced security risks, and streamlined reporting for leadership and on-call teams. Technologies demonstrated: Airflow 2.0, Python scripting, Slack integration, Prometheus observability, and robust configuration management.
December 2025
15 Commits • 2 Features
Dec 1, 2025December 2025 monthly performance for lowtouch-ai/agent_dags: delivered critical reliability fixes and expanded weekly metrics capabilities in a Kubernetes-focused Airflow DAGs pipeline. Key bug fixes corrected the DAG readiness check and MicroK8s expiry check Python callables, reducing risk of mis-executed tasks and DAG failures. Feature work introduced and stabilized a weekly metrics reporting pipeline (Kubernetes-centric content, enhanced scheduling, and robust data access) and a parallel weekly data retrieval pathway (AI-assisted to Python data fetch) to improve accuracy and flexibility of metrics collection. The combined work improved task success rates, timeliness of weekly insights, and overall observability. Technologies demonstrated include Python, Airflow DAG design and scheduling, Kubernetes integration, Prometheus naming conventions, and data retrieval patterns (AI-assisted vs Python). Business value: higher reliability, faster metrics delivery, and clearer operational visibility with aligned naming and modularization.
15 Commits • 2 Features
Dec 1, 2025December 2025 monthly performance for lowtouch-ai/agent_dags: delivered critical reliability fixes and expanded weekly metrics capabilities in a Kubernetes-focused Airflow DAGs pipeline. Key bug fixes corrected the DAG readiness check and MicroK8s expiry check Python callables, reducing risk of mis-executed tasks and DAG failures. Feature work introduced and stabilized a weekly metrics reporting pipeline (Kubernetes-centric content, enhanced scheduling, and robust data access) and a parallel weekly data retrieval pathway (AI-assisted to Python data fetch) to improve accuracy and flexibility of metrics collection. The combined work improved task success rates, timeliness of weekly insights, and overall observability. Technologies demonstrated include Python, Airflow DAG design and scheduling, Kubernetes integration, Prometheus naming conventions, and data retrieval patterns (AI-assisted vs Python). Business value: higher reliability, faster metrics delivery, and clearer operational visibility with aligned naming and modularization.
December 2025
November 2025
22 Commits • 15 Features
Nov 1, 2025Month: 2025-11 — Focused on delivering automated SRE reporting, expanding observability, and stabilizing data collection across Kubernetes for proactive reliability and business insight.
November 2025
22 Commits • 15 Features
Nov 1, 2025Month: 2025-11 — Focused on delivering automated SRE reporting, expanding observability, and stabilizing data collection across Kubernetes for proactive reliability and business insight.
October 2025
1 Commits • 1 Features
Oct 1, 2025Oct 2025 monthly work summary for lowtouch-ai/agent_dags: Delivered a robust Email Validation feature for the Autofinix Email Respond Script, strengthening security and automation reliability. Implemented case-insensitive sender/loan email matching, ensured the sender's email is registered, and enforced output formatting to only VALIDATION_PASSED or VALIDATION_FAILED. The change is captured in commit 73d46491486ba37c6adf7c3acc3583ed6448e982. This work reduces misrouting risk, improves downstream automation, and enhances maintainability.
1 Commits • 1 Features
Oct 1, 2025Oct 2025 monthly work summary for lowtouch-ai/agent_dags: Delivered a robust Email Validation feature for the Autofinix Email Respond Script, strengthening security and automation reliability. Implemented case-insensitive sender/loan email matching, ensured the sender's email is registered, and enforced output formatting to only VALIDATION_PASSED or VALIDATION_FAILED. The change is captured in commit 73d46491486ba37c6adf7c3acc3583ed6448e982. This work reduces misrouting risk, improves downstream automation, and enhances maintainability.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for lowtouch-ai/agent_dags focused on feature delivery and automation improvements around EMI-related customer inquiries. No major bugs fixed this period according to available data.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for lowtouch-ai/agent_dags focused on feature delivery and automation improvements around EMI-related customer inquiries. No major bugs fixed this period according to available data.
Activity
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Quality Metrics
Correctness94.2%
Maintainability89.4%
Architecture90.0%
Performance89.0%
AI Usage40.4%
Skills & Technologies
Programming Languages
Python
Technical Skills
AI IntegrationAI integrationAPI IntegrationAPI integrationAirflowApache AirflowBackend DevelopmentCode RefactoringData AnalysisData EngineeringData PipelinesData ProcessingData ValidationData VisualizationData pipeline management
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