Agent skills for Cursor

AI-assisted safety layer for git push operations with quality gates and collaboration guardrails.

Push to remote and monitor CI. Validates branch safety, executes git push WITH explicit user approval, then monitors CI run status via gh…

Commit and push agent-skill changes to the user's private skills GitHub repo (private-skills-repo, rooted at ~/.agents).

DDOS production handoff layer. Converts a validated single-file HTML preview (typically from /dashboard-preview) into a deployable PWA…

Build production-ready AI agents with PydanticAI — type-safe tool use, structured outputs, dependency injection, and multi-model support.

Fast high-dimensional fixed effects: OLS, Poisson, IV with multi-way FE; DiD (TWFE, did2s, Sun-Abraham); clustered SEs…

Configure PyInstaller for Python binary builds with spec files and bundling options.

Pairwise test generation

Complete guide for Pyth Network - decentralized oracle providing real-time price feeds for DeFi.

Creates FastAPI endpoints with layered architecture (Router → Service → Repository).

Use when planning, implementing, or reviewing code for any Python project — designing architecture, writing classes or functions…

Guidance for writing correct, non-blocking async Python — using async/await for I/O-bound work, never calling blocking functions from…

Python background job patterns including task queues, workers, and event-driven architecture.

When to activate: Typer, Click, argparse, CLI applications, rich output, progress bars, configuration files

Python code quality standards. Use when working with types, linting, type checking, exceptions, logging, or configuring…

Analyze Python code quality using MCP tools - complexity, clones, dead code, coupling.

Enforces strict Python code quality standards using pytest, mypy, ruff, black, and isort.

Python configuration management via environment variables and typed settings.

When to activate: ETL, data pipeline, Prefect, Airflow, DLT, Polars, batch processing, data quality

Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance.

Python error handling patterns including input validation, exception hierarchies, and partial failure handling.