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Found 5,393 Skills
Updates model references across all skill files when new Claude models are released. Use when Anthropic releases new Claude models to keep skills current.
Run a full-scale implementation review with parallel subagents for plan alignment, UI verification, technical and strategic analysis, and test coverage gap closure across app and database layers.
Chat with web AI agents (ChatGPT, Gemini, Claude, Grok, NotebookLM) via browser automation. Use when stuck, need cross-validation, or want a second-model review.
Use when implementing client-server communication in Roblox, firing events between LocalScripts and Scripts, passing data across the network boundary, syncing game state, or defending against exploits that abuse RemoteEvents or RemoteFunctions.
Set up uv (Rust-based Python package manager) in CI/CD pipelines. Use when configuring GitHub Actions workflows, GitLab CI/CD, Docker builds, or matrix testing across Python versions. Includes patterns for cache optimization, frozen lockfiles, multi-stage builds, and PyPI publishing with trusted publishing. Covers GitHub Actions setup-uv action, Docker multi-stage production/development builds, and deployment patterns.
Verify a specific claim by searching for evidence across web and academic sources. Use when the user asks to verify, fact-check, or confirm a statement.
Critically review terminal user interfaces for UX quality, responsiveness, visual design, and interactivity. Use when asked to "review my TUI", "test my TUI UX", "audit my terminal UI", "check TUI responsiveness", "review TUI keybindings", "check interactivity", or any request to evaluate the user experience quality of a ratatui/crossterm/ncurses-based terminal application. Launches the TUI in tmux, systematically tests 10 dimensions (responsiveness, input conflicts, visual clarity, navigation, feedback loops, error states, layout, keyboard design, permission flows, visual design & color), and produces a graded report with screenshots and specific findings. Benchmarks against Claude Code, OpenCode, and Codex — the three best-in-class AI terminal UIs.
Intelligent README.md generation prompt that analyzes project documentation structure and creates comprehensive repository documentation. Scans .github/copilot directory files and copilot-instructions.md to extract project information, technology stack, architecture, development workflow, coding standards, and testing approaches while generating well-structured markdown documentation with proper formatting, cross-references, and developer-focused content.
Technology-agnostic prompt generator that creates customizable AI prompts for scanning codebases and identifying high-quality code exemplars. Supports multiple programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable analysis depth, categorization methods, and documentation formats to establish coding standards and maintain consistency across development teams.
Capture and persist lessons learned from a session to compound knowledge over time. Triggers on "/lessons-learned", "what did we learn", "save lessons", "update skills with what we learned", or at the end of a complex multi-session task. PROACTIVE USE: This skill should also be suggested or invoked (1) when resuming from context compaction (the previous context likely contained unrecorded lessons), (2) after resolving a non-trivial bug or debugging session, (3) after significant friction or failed approaches that yielded insight, (4) after a council-of-bots review that surfaced fixes. Identifies reusable patterns, bug fixes, workflow insights, and tool quirks, then persists them to the right places: auto-memory (project-specific), skill files (reusable across projects), or both.
A micro-prompt that reminds the agent that it is an interactive programmer. Works great in Clojure when Copilot has access to the REPL (probably via Backseat Driver). Will work with any system that has a live REPL that the agent can use. Adapt the prompt with any specific reminders in your workflow and/or workspace.
Find and replace code patterns structurally using ast-grep. Use when you need to match code by its AST structure (not just text), such as finding all functions with specific signatures, replacing API patterns across files, or detecting code anti-patterns that regex cannot reliably match.