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Found 1,323 Skills
Instrument Python LLM apps, build golden datasets, write eval-based tests, run them, and root-cause failures — covering the full eval-driven development cycle. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM, even if they don't say "evals" explicitly. Use for making sure an AI app works correctly, catching regressions after prompt changes, debugging why an agent started behaving differently, or validating output quality before shipping.
Expert guidance for developing cross-platform desktop applications with Avalonia UI framework. Use when building, debugging, or optimizing Avalonia apps including MVVM architecture, XAML design, data binding, styling, theming, custom controls, and cross-platform deployment for Windows, macOS, Linux, iOS, Android, and WebAssembly.
Use this skill when profiling application performance, debugging memory leaks, optimizing latency, benchmarking code, or reducing resource consumption. Triggers on CPU profiling, memory profiling, flame graphs, garbage collection tuning, load testing, P99 latency, throughput optimization, bundle size reduction, and any task requiring performance analysis or optimization.
Build, tune, and operate Ruff for Python linting, formatting, and editor/CI integration. Use when adding or updating Ruff configuration, migrating from Black/Flake8/isort, selecting rule families, enforcing fix safety, or debugging lint/format behavior in local development, pre-commit, and CI.
Go testing patterns and methodology: table-driven tests, t.Run subtests, t.Helper helpers, mocking interfaces, benchmarks, race detection, and synctest. Use when writing new Go tests, modifying existing tests, adding coverage, fixing failing tests, writing benchmarks, or creating mocks. Triggered by "go test", "_test.go", "table-driven", "t.Run", "benchmark", "mock", "race detection", "test coverage". Do NOT use for non-Go testing (use test-driven-development instead), debugging test failures (use systematic-debugging), or general Go development without test focus (use golang-general-engineer directly).
Intelligent interaction performance analysis with automated workflows for INP debugging, scroll jank investigation, and main thread blocking. Includes decision trees that automatically run script attribution when long frames detected, break down input latency phases, and correlate layout shifts with interactions. Features workflows for complete interaction audit, third-party script impact analysis, and animation performance debugging. Cross-skill integration with Core Web Vitals (INP/CLS correlation) and Loading (script execution analysis). Use when the user asks about slow interactions, janky scrolling, unresponsive pages, or INP optimization. Compatible with Chrome DevTools MCP.
Best practices for using Radon IDE's MCP tools when developing, debugging, and inspecting React Native and Expo apps. Use when interacting with a running app through Radon IDE - viewing screenshots, reading logs, inspecting the component tree, debugging network requests, reloading the app, or querying React Native documentation and library info. Trigger on: 'debug React Native', 'fix UI', 'network issues', 'build issues', 'Radon IDE', 'view screenshot', 'app logs', 'component tree', 'network inspector', 'reload app', 'React Native docs', 'library description', 'emulator', 'development viewport', 'view_screenshot', 'view_application_logs', 'view_component_tree', 'reload_application', 'view_network_logs', 'view_network_request_details', 'query_documentation', 'get_library_description', and every request involving live app inspection, debugging or development in a Radon IDE session.
Production-first enterprise skill for The Composable Architecture (TCA) with SwiftUI (iOS 16+, TCA 1.7+). This skill should be used when building new TCA features with @Reducer macro, decomposing god reducers, implementing StackState/StackAction navigation or tree-based @Presents navigation, writing TestStore tests, migrating legacy TCA code to modern @ObservableState patterns, debugging TCA performance issues, managing side effects and dependencies with @DependencyClient, or reviewing TCA code for anti-patterns. Use this skill any time someone works with TCA reducers, stores, effects, or dependencies — AI tools consistently generate outdated pre-1.7 TCA patterns, so this skill is essential for correct code.
AI-powered JavaScript reverse engineering tool. Senior JavaScript reverse engineering expert assistant. Actions: collect, search, deobfuscate, understand, summarize, detect-crypto, browser, debugger, breakpoint, debug-step, debug-eval, debug-vars, script, hook, stealth, dom, page. Capabilities: obfuscated code analysis, VM cracking, Webpack unpacking, AST transformation, Puppeteer/CDP automation, anti-detection, fingerprint spoofing, encryption identification, parameter extraction, algorithm restoration, Canvas/WebGL fingerprinting, WebDriver hiding, CDP debugging, breakpoint analysis, dynamic tracing, Hook injection, DOM inspection, page control.
Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
Reference skill for Zoom authentication. Use after routing to an auth workflow when choosing app credentials, grant types, scopes, token refresh behavior, or debugging Zoom OAuth failures.
Systematic debugging with persistent state across context resets