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Found 4,154 Skills
Analyze a software codebase for algorithmic complexity and performance hotspots, then propose or implement safe optimizations without breaking behavior. Use when Codex is asked to scan many files, find inefficient loops, nested iteration, repeated scans, costly rendering/recomputation, N+1 queries, avoidable O(n^2) or O(n) operations, or reduce complexity such as O(n^2) to O(n log n) / O(n), while preserving tests, APIs, outputs, and maintainability.
Run technical SEO audit. Use when: checking Core Web Vitals, crawlability, indexation, speed, or structured data.
Schedule social media posts. Use when: publishing to Twitter/X, Instagram, LinkedIn, TikTok, YouTube, or Pinterest.
Authors MSW `.behaviourtree` files end-to-end and maintains the project-specific authoring spec (`.behaviourDocs/bt-spec.md`). Scans every `.codeblock` whose paired `.mlua` extends `ActionNode`/`DecoratorNode` to build a compact catalog of custom action/decorator UUIDs, propertyKey names, and version-stamped MODNativeType strings. Then generates the full tree: RootNode → Nodes graph, Blackboard variables, nodeProperties wiring, and self-validates parent/child consistency. Triggers: 'create behaviourtree', 'new BT', 'add a behaviour tree', 'BT node graph', '비헤이비어 트리 만들어', '.behaviourtree 생성', 'SequenceNode SelectorNode', 'Blackboard variable', 'definitionId codeblock', 'startNodeId', 'build BT spec', 'refresh bt-spec', 'generate behaviourtree catalog', 'BT 스펙 생성', 'bt-spec.md 만들어', 'rescan BT nodes'.
Perform a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.
Record browser interactions as video using Playwright. Use for capturing demo videos, app walkthroughs, and UI flows for Remotion videos. Triggers include recording a demo, capturing browser video, screen recording a website, or creating walkthrough footage.
Implement feature tasks using AI agents in logical batches, track completion status, identify blockers, and manage task handoffs. Use when you have an execution sequence and need AI agents to build tasks while maintaining progress tracking.
Use when working on the spec branch of sdlc-dev, when requirements are ambiguous, scope is unstable, constraints are unclear, and issues such as context drift, unfounded assumptions, multiple questions asked at once, or requests to skip the FEATURE_DIR/raw gate occur.
Use when you need to produce a D2 decision document (RFC/Decision Doc) for a Spec Pack, and need to independently decide whether to execute D0 (bypass design via diversion) and D1 (optional research) even under pressure, avoiding guessing FEATURE_DIR, making up input details, writing design as implementation specifics, or leaving behind TODO/To-Confirm lists.
Use when you need to perform D1 research during the Spec-level design phase (output `{FEATURE_DIR}/design/research.md`), or when you face critical uncertainties/high-risk points that require verification first instead of directly proceeding to D2; common symptoms include lack of evidence to support trade-offs, unknown items written as TODOs/to-be-confirmed issues, attempting to guess the FEATURE_DIR under pressure, or writing research as implementation details.
Create time-boxed technical spike documents for researching and resolving critical development decisions before implementation.
Packaging and distributing Qt Python applications — PyInstaller, Briefcase, and platform-specific build configurations. Use when distributing a PySide6 or PyQt6 app as a standalone executable, creating installers, configuring macOS bundles, Windows executables, or Linux AppImages. Trigger phrases: "package app", "PyInstaller", "distribute", "deploy", "standalone executable", "installer", "bundle app", "briefcase", "Windows build", "macOS build", "AppImage", "one-file"