punkpeye/awesome-mcp-servers
Apr 2026 – Apr 2026
1 Month active
Languages Used
Markdown
Technical Skills
AI integrationdata analysisdata sciencedocumentationrepository management
jessicarumbelow
PROFILE
Jessicarumbelow
Jessica Rumbelow developed Disco, an automated scientific discovery tool for tabular data, in the punkpeye/awesome-mcp-servers repository. Disco accelerates exploratory data analysis by automatically detecting feature interactions and subgroup effects, addressing challenges in uncovering complex data relationships. Jessica focused on AI integration and data science techniques, leveraging Markdown for documentation and repository management. She also improved repository hygiene by updating documentation, correcting entry name formats, and adding performance tracking with a Glama badge. Her work emphasized maintainability and discoverability, with collaborative efforts to align metadata with release standards. The engineering demonstrated depth in both analytics feature delivery and project organization.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
3
Activity Months1
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Work History
April 2026
2 Commits • 2 Features
Apr 1, 2026April 2026 monthly report for punkpeye/awesome-mcp-servers: Highlights include the Disco automated scientific discovery tool for tabular data and targeted documentation/metadata improvements that enhance discoverability and maintainability. No critical bugs reported this month; engineering effort focused on delivering a high-value analytics feature and reinforcing repo hygiene.
2 Commits • 2 Features
Apr 1, 2026April 2026 monthly report for punkpeye/awesome-mcp-servers: Highlights include the Disco automated scientific discovery tool for tabular data and targeted documentation/metadata improvements that enhance discoverability and maintainability. No critical bugs reported this month; engineering effort focused on delivering a high-value analytics feature and reinforcing repo hygiene.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Markdown
Technical Skills
AI integrationdata analysisdata sciencedocumentationrepository management
Repositories Contributed To
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