pymc-labs/pymc-marketing
May 2025 – Apr 2026
3 Months active
Languages Used
Markdown
Technical Skills
Comparison AnalysisDocumentationTechnical WritingContent MarketingDevOpsSystem Administration
Luca Fiaschi
PROFILE
Luca Fiaschi
Worked on the pymc-labs/pymc-marketing repository to deliver feature-rich documentation and infrastructure improvements supporting marketing analytics workflows. Developed a comprehensive MMM package comparison guide using Markdown and Python-based documentation tooling, enabling data-driven package selection and clearer evaluation criteria. Enhanced the landing page and documentation to highlight performance benchmarks and professional services, leveraging content marketing and technical writing skills. Reorganized the mmm-modeling skill directory to improve discoverability and enable automatic registry synchronization via DevOps practices, using filesystem refactoring and symlinks. Collaborated across teams, maintained code hygiene with pre-commit automation, and ensured seamless integration with Decision Hub and downstream analytics pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
125
Activity Months3
Your Network
18 peopleWork History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 — Key feature delivered for pymc-labs/pymc-marketing: MMM Modeling Skill Discoverability and Automatic Registry Synchronization. Reorganized the mmm-modeling skill from a hidden location to skills/mmm-modeling to enable discovery by the Decision Hub crawler and automatic synchronization to the registry. A symlink was kept to preserve Cursor access, ensuring no disruption to existing workflows. This work reduces manual maintenance, accelerates skill availability, and improves end-user usability across marketing analytics pipelines. No major bugs fixed in this repo this month. Technologies demonstrated include filesystem layout refactor, directory reorganization, symlinks, crawler-based discovery, and registry synchronization. Collaboration across teams is evidenced by the co-authored commit.
1 Commits • 1 Features
Apr 1, 2026April 2026 — Key feature delivered for pymc-labs/pymc-marketing: MMM Modeling Skill Discoverability and Automatic Registry Synchronization. Reorganized the mmm-modeling skill from a hidden location to skills/mmm-modeling to enable discovery by the Decision Hub crawler and automatic synchronization to the registry. A symlink was kept to preserve Cursor access, ensuring no disruption to existing workflows. This work reduces manual maintenance, accelerates skill availability, and improves end-user usability across marketing analytics pipelines. No major bugs fixed in this repo this month. Technologies demonstrated include filesystem layout refactor, directory reorganization, symlinks, crawler-based discovery, and registry synchronization. Collaboration across teams is evidenced by the co-authored commit.
April 2026
October 2025
1 Commits • 1 Features
Oct 1, 2025Oct 2025 monthly summary for pymc-labs/pymc-marketing: Delivered key marketing-focused enhancements to support better evaluation and engagement with PyMC Labs offerings. Core deliverables include refining the 'How We Compare' documentation to clearly contrast PyMC-Marketing with other MMM packages, introducing a new performance benchmark against Google Meridian, and upgrading the landing page with expanded contact options and explicit professional services details. These changes improve decision-making for potential users, speed up assessments, and strengthen the value proposition. The work was backed by code/documentation commits (e.g., 33b16a0289b1fb8ad6483a9b5a8c9cbe843b6ea5) and included pre-commit automation for code hygiene.
October 2025
1 Commits • 1 Features
Oct 1, 2025Oct 2025 monthly summary for pymc-labs/pymc-marketing: Delivered key marketing-focused enhancements to support better evaluation and engagement with PyMC Labs offerings. Core deliverables include refining the 'How We Compare' documentation to clearly contrast PyMC-Marketing with other MMM packages, introducing a new performance benchmark against Google Meridian, and upgrading the landing page with expanded contact options and explicit professional services details. These changes improve decision-making for potential users, speed up assessments, and strengthen the value proposition. The work was backed by code/documentation commits (e.g., 33b16a0289b1fb8ad6483a9b5a8c9cbe843b6ea5) and included pre-commit automation for code hygiene.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for pymc-marketing: Delivered a comprehensive MMM package comparison guide documentation that enables data-driven package selection across PyMC-Marketing, Lightweight-MMM, Robyn, Orbit KTR, and Google Meridian. The guide includes a feature comparison table, guidance on choosing the right package, and a refreshed download-popularity plot, with a restyled visualization and a corrected typo. No major bugs fixed this month. Overall impact includes improved decision-making for package selection, faster onboarding for new users, and stronger documentation quality. Technologies/skills demonstrated include Python-based documentation tooling, data visualization, and version-controlled collaboration with clear commit references. Key outcomes and trends: reusable documentation asset, clearer evaluation criteria, and aligned cross-team understanding of MMM tooling.
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for pymc-marketing: Delivered a comprehensive MMM package comparison guide documentation that enables data-driven package selection across PyMC-Marketing, Lightweight-MMM, Robyn, Orbit KTR, and Google Meridian. The guide includes a feature comparison table, guidance on choosing the right package, and a refreshed download-popularity plot, with a restyled visualization and a corrected typo. No major bugs fixed this month. Overall impact includes improved decision-making for package selection, faster onboarding for new users, and stronger documentation quality. Technologies/skills demonstrated include Python-based documentation tooling, data visualization, and version-controlled collaboration with clear commit references. Key outcomes and trends: reusable documentation asset, clearer evaluation criteria, and aligned cross-team understanding of MMM tooling.
May 2025
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance85.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Markdown
Technical Skills
Comparison AnalysisContent MarketingDevOpsDocumentationSystem AdministrationTechnical Writing
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