A versatile command-line tool for interacting with Google Workspace APIs, designed for both human users and AI agents.

Reviews animation and motion code against a high craft bar derived from Emil Kowalski's design engineering philosophy. Default to flagging; approval is earned.

A Claude Code skill that directs website creation like a film, with art direction locking, local asset generation, and an executable anti-slop linter gating every deployment.

AI agent skill that reads academic papers and generates structured Obsidian notes.

A platform-neutral analytical skill that profiles messy data, selects adaptive methods, and produces source-backed visual reports for high-stakes decisions.

A curated set of agent skills for Qdrant vector search, providing structured knowledge on scaling, optimization, monitoring, deployment, and SDK usage.

An evidence-bound Codex skill for LLM internship resume polishing, JD tailoring, interview grilling, and project scouting.

机器学习方法习题解答，在线阅读地址：https://datawhalechina.github.io/statistical-learning-method-solutions-manual

An IMAP/POP/SMTP proxy that transparently adds OAuth 2.0 authentication for email clients that don't support this method. Keep legacy email clients working with Exchange Online, Gmail and other providers.

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.