আপনার এজেন্টের জন্য উপযুক্ত সক্ষমতা খুঁজে নিন।
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.
Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)
Run Python scripts or inspect installed packages using the HoloMotion training conda env. Use when running project Python scripts, executing training-related code, or inspecting packages in the holomotion_train environment. Always source train.env first, then use Train_CONDA_PREFIX.
Apply Python tooling standards including uv package management, pytest testing, ruff/basedpyright code quality, one-line docstrings, and self-documenting code practices. Use this skill when working with Python backend code, managing dependencies, running tests, or ensuring code quality. Apply when installing packages, writing tests, formatting code, type checking, adding docstrings, organizing imports, or deciding whether to create new files vs. extending existing ones. Use for any Python development task requiring adherence to tooling standards and best practices.
Create or update a Kaggle game environment (Python backend with specification, interpreter, renderer, agents, and tests)
Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, and brand monitoring. Covers `ahrefs-python` usage including AhrefsClient / AsyncAhrefsClient, typed request/response models, error handling, and all API sections.
Check Python logging levels and patterns for correctness. Focus on identifying wrong severity levels and missing exception handling. Use when reviewing code quality.
构建健壮、高效且易于维护的 Python 应用程序的 Python 惯用法（Pythonic idioms）、PEP 8 标准、类型提示（Type hints）以及最佳实践。
Pythonic 惯用法、PEP 8 标准、类型提示，以及构建稳健、高效且可维护 Python 应用的最佳实践。
Configures global exception handling middleware. Use when adding centralized error handling to .NET or Python backends.
Upgrades Python pip/poetry/pipenv dependencies with breaking change handling. Use when updating Python dependencies.
Configures structured JSON logging with Serilog (.NET) or structlog (Python). Use when adding logging to backend projects.
Use when the user provides a natural language description of a 3D object or mechanical part and wants to generate a CAD model. Converts the description into CadQuery Python code, automatically detects or sets up the CadQuery environment, executes the script, and produces STL and STEP output files.
Ultra-fast data validation library for Python (520x faster than Pydantic). Use when building validated data models, API request/response schemas, or configuration objects. Provides Pydantic v2-compatible BaseModel API with Zig-powered native validation.
This skill should be used when the user requests to create professional business documents (proposals, business plans, or budgets) from templates. It provides PDF templates and a Python script for generating filled documents from user data.
Use when you need concrete UI/UX inputs (palette, typography, landing patterns, UX/a11y constraints) to drive design or review. Searchable UI/UX design intelligence (styles, palettes, typography, landing patterns, charts, UX/a11y guidelines + stack best practices) backed by CSV + a Python search script. Triggers: UIUX/uiux, UI/UX, UX design, UI design, design system, design spec, color palette, typography, layout, animation, accessibility/a11y, component styling. Actions: search, recommend, review, improve UI.
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
