अपने एजेंट के लिए सही क्षमता खोजें।
Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.
Python API and tools for working with UCIS (Unified Coverage Interoperability Standard) coverage databases. Use when working with hardware verification coverage data, converting coverage formats, merging coverage databases, generating coverage reports, or analyzing functional and code coverage metrics.
Choosing and implementing dbt materializations (ephemeral, view, table, incremental, snapshots, Python models). Use this skill when deciding on materialization strategy, implementing incremental models, setting up snapshots for SCD Type 2 tracking, or creating Python models for machine learning workloads.
This skill provides guidance for translating COBOL programs to modern languages (Python, Java, etc.) while preserving exact behavior. It should be used when tasks involve COBOL-to-modern-language migration, legacy code translation, fixed-width file format handling, or ensuring byte-level compatibility between source and target implementations.
Guidance for implementing CLI tools that perform inference using PyTorch models in native languages (C/C++/Rust). This skill should be used when tasks involve extracting weights from PyTorch .pth files, implementing neural network forward passes in C/C++, or creating standalone inference tools without Python dependencies.
Guidance for creating standalone CLI tools that perform neural network inference by extracting PyTorch model weights and reimplementing inference in C/C++. This skill applies when tasks involve converting PyTorch models to standalone executables, extracting model weights to portable formats (JSON), implementing neural network forward passes in C/C++, or creating CLI tools that load images and run inference without Python dependencies.
This skill provides guidance for translating RStan (R-based Stan interface) code to PyStan (Python-based Stan interface). It should be used when converting Stan models from R to Python, migrating Bayesian inference workflows between languages, or adapting R data preparation logic to Python equivalents.
Guidance for converting R-Stan (RStan) code to Python-Stan (PyStan). This skill applies when translating Stan models and inference code from R to Python, including API mapping between RStan and PyStan 3.x, hyperparameter translation, and handling differences in output formats. Use this skill for statistical model migration, Bayesian inference code conversion, or when working with Stan models across R and Python ecosystems.
Guidance for building gRPC-based key-value store services in Python. This skill should be used when tasks involve creating gRPC servers, defining protocol buffer schemas, or implementing key-value storage APIs with gRPC. Covers proto file creation, code generation, server implementation, and verification strategies.
Guide for implementing gRPC-based key-value store services in Python. This skill should be used when building gRPC servers with protobuf definitions, implementing KV store operations (Get, Set, Delete), or troubleshooting gRPC service connectivity. Applicable to tasks involving grpcio, protobuf code generation, and background server processes.
Modern Python API development with FastAPI covering async patterns, Pydantic validation, dependency injection, and production deployment
Comprehensive FastAPI skill for building modern Python web APIs with focus on customer support systems, ticket management, real-time chat, and backend operations
Guidance for creating polyglot files that are valid in both Python and C. This skill applies when tasked with writing code that must be parseable and executable by both the Python interpreter and C compiler. Covers polyglot syntax techniques, testing strategies, and critical cleanup requirements.
Guidance for implementing high-performance portfolio optimization using Python C extensions. This skill applies when tasks require optimizing financial computations (matrix operations, covariance calculations, portfolio risk metrics) by implementing C extensions for Python. Use when performance speedup requirements exist (e.g., 1.2x or greater) and the task involves numerical computations on large datasets (thousands of assets).
Guide for optimizing Python numerical computations with C extensions. This skill should be used when tasks involve creating C extensions for Python, implementing mathematical algorithms (matrix operations, linear algebra) in C, or optimizing computational bottlenecks to achieve significant speedup. Particularly relevant for portfolio risk/return calculations, scientific computing, and performance-critical code requiring validation against baseline implementations.
Guidance for creating Python packages and serving them via a local PyPI server. This skill applies when tasks involve building Python packages (with pyproject.toml or setup.py), setting up local package repositories, or serving packages via HTTP for pip installation. Use when the goal is to create installable Python packages and make them available through a local index URL.
Guidance for building and installing Cython extension packages, particularly when resolving compatibility issues with modern Python and NumPy versions. This skill applies when installing legacy Cython packages, fixing NumPy 2.0 deprecation errors, resolving Python 3.x compatibility issues in extension modules, or troubleshooting Cython compilation failures. Use this skill for tasks involving setup.py with Cython extensions, deprecated NumPy type errors, or installing packages to system Python environments.
Guide for setting up local PyPI servers to host and serve Python packages. This skill should be used when tasks involve creating a local PyPI repository, serving Python packages over HTTP, building distributable Python packages, or testing pip installations from a custom index URL.
Guidance for modernizing legacy Python 2 scientific computing code to Python 3. This skill should be used when tasks involve converting outdated scientific Python scripts (using deprecated libraries like ConfigParser, cPickle, urllib2, or Python 2 syntax) to modern Python 3 equivalents with contemporary scientific stack (NumPy, pandas, scipy, matplotlib). Applies to data processing, analysis pipelines, and scientific computation modernization tasks.
Guidance for converting COBOL programs to modern languages (Python, Java, etc.) while preserving exact behavior and data format compatibility. This skill should be used when modernizing legacy COBOL applications, converting COBOL business logic to modern languages, or ensuring byte-for-byte output compatibility between COBOL and its replacement.
