10 boosters for "data-science" — open source, verified from GitHub, ready to install
<!-- Generated from e2eplaywright/AGENTS.md. Edit that file instead, then run: uv run python scripts/generateagent_rules.py --> We use playwright with pytest to e2e test Streamlit library. E2E tests verify the complete Streamlit system (frontend, backend, communication, state, visual appearance) fro
Cursor rules that provide AI coding agents with guidance for developing the Streamlit library itself (backend, frontend, protobufs), rather than building Streamlit applications.
FLAML (Fast Library for Automated Machine Learning & Tuning) is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, etc. and optimizes their performance. The repository uses pre-co
<!-- AUTO-GENERATED FILE. DO NOT EDIT MANUALLY. --> <!-- Source: tools/ai/llms-base.txt + tools/ai/aicontextheader.md --> <!-- Regenerate with: python tools/ai/generateaicontext_files.py -->
"version": "0.4.33", "description": "Scientific research agent extension - turns research goals into reproducible Jupyter notebooks with Python REPL, data analysis, and ML workflows", "name": "Yeachan Heo",
Treat every competition as a validation problem first and a modeling problem second. The default target platform is Kaggle, so prefer Kaggle-native notebooks/scripts, datasets, model artifacts, competition submissions, and score receipts. For code competitions, assume the final notebook/kernel will
"description": "Independent, unofficial Kaggle.com workflow plugin: competition reports, dataset/model downloads, notebook execution, forums, writeups, submissions, and badge collection. Not affiliated with, endorsed by, or sponsored by Kaggle or Google.", "homepage": "https://github.com/shepsci/kag
FHIRy is a Python package that converts FHIR healthcare data into pandas DataFrames, enabling data scientists and healthcare developers to perform analytics, machine learning, and AI on standardized health records. It's ideal for researchers and engineers working with health data who need quick, programmatic access to structured clinical datasets.
Automates cleanup of JupyterHub Docker environments by stopping containers and removing orphaned resources. Ideal for data scientists and ML engineers managing local Jupyter development platforms who need to free up disk space and reset their environments.
MCP Analytics provides searchable analytics tools and interactive HTML reports integrated with Claude via the Model Context Protocol, enabling developers to embed data analysis and visualization capabilities into AI-powered applications.
