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Found 1,918 Skills
Advanced Python unit testing framework for customer support tech enablement, covering FastAPI, SQLAlchemy, PostgreSQL, async operations, mocking, fixtures, parametrization, coverage, and comprehensive testing strategies for backend support systems
Analyze code repository logging coverage to ensure all function branches have LOGE/LOGI logs and identify high-frequency log risks. Supports multiple programming languages (C++, Java, Python, JavaScript, etc.)
Analyze CSV files, generate summary statistics, and create visualizations using Python and pandas. Use when the user uploads, attaches, or references a CSV file, asks to summarize or analyze tabular data, requests insights from CSV data, or wants to understand data structure and quality.
Implement payment integrations with SePay (Vietnamese payment gateway with VietQR, bank transfers, cards) and Polar (global SaaS monetization platform with subscriptions, usage-based billing, automated benefits). Use when integrating payment processing, implementing checkout flows, managing subscriptions, handling webhooks, processing bank transfers, generating QR codes, automating benefit delivery, or building billing systems. Supports authentication (API keys, OAuth2), product management, customer portals, tax compliance (Polar as MoR), and comprehensive SDK integrations (Node.js, PHP, Python, Go, Laravel, Next.js).
Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction. Originally from OpenAI's curated skills catalog.
Generates dead code detection configurations for loom plan verification. Provides language-specific commands, fail patterns, and ignore patterns for Rust, TypeScript, Python, Go, and JavaScript. Use when adding code quality checks to acceptance criteria or truths fields in loom plans. Dead code detection catches incomplete wiring by identifying code that exists but is never called.
Setup Sentry Tracing (Performance Monitoring) in any project. Use this when asked to add performance monitoring, enable tracing, track transactions/spans, or instrument application performance. Supports JavaScript, TypeScript, Python, Ruby, React, Next.js, and Node.js.
Central authority for Claude Code status line configuration. Covers custom status line creation, /statusline command, status line settings (statusLine in settings.json), JSON input structure (model, workspace, cost, session info), status line scripts (Bash, Python, Node.js), terminal color codes, git-aware status lines, helper functions, and status line troubleshooting. Supports creating custom status lines, configuring status line behavior, and displaying contextual session information. Delegates 100% to docs-management skill for official documentation.
Bootstrap new projects with strong typing, linting, formatting, and testing. Supports Python, TypeScript, and other languages with research fallback.
Mise development environment manager (asdf + direnv + make replacement). Capabilities: tool version management (node, python, go, ruby, rust), environment variables, task runners, project-local configs. Actions: install, manage, configure, run tools/tasks with mise. Keywords: mise, mise.toml, tool version, runtime version, node, python, go, ruby, rust, asdf, direnv, task runner, environment variables, version manager, .tool-versions, mise install, mise use, mise run, mise tasks, project config, global config. Use when: installing runtime versions, managing tool versions, setting up dev environments, creating task runners, replacing asdf/direnv/make, configuring project-local tools.
Test, commit, and push in one atomic workflow. Runs Go and Python tests, commits with conventional message, pushes to current branch.
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).