React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

Provides comprehensive GitHub operations using gh CLI and GitHub API. Activates when working with pull requests, issues, repositories, workflows, or GitHub API operations including creating/viewing/merging PRs, managing issues, querying API endpoints, and handling GitHub workflows in enterprise or public GitHub environments.

Bump tantivy to the latest commit on main branch, fix compilation issues, and open a PR

Use when planning or reviewing Go SDK work for Deepgram conversational STT / Flux v2. This repo does not currently ship a first-class v2 listen client, so route supported v1 transcription to deepgram-go-speech-to-text and document raw WebSocket fallback honestly when v2 is requested.

Implement, deploy, and index ERC20 and ERC721 tokens in Dojo. Use when adding token contracts, deploying them, or configuring Torii to index balances and transfers.

Incrementally implement new features in Java repositories from natural language descriptions. Use when adding functionality to existing Java codebases (Maven or Gradle projects). Takes a feature description as input and outputs modified repository with implementation code, corresponding JUnit tests, and verification that all tests pass. Supports method additions, new class creation, and method modifications with proper Java conventions.

Translate C or C++ programs into equivalent Lean4 code, preserving program semantics and ensuring the generated code is well-typed, executable, and can run successfully. Use when the user asks to convert C/C++ code to Lean4, port C/C++ programs to Lean4, translate imperative code to functional Lean4, or create Lean4 versions of C/C++ algorithms.

Detects and analyzes ambiguous language in software requirements and user stories. Use when reviewing requirements documents, user stories, specifications, or any software requirement text to identify vague quantifiers, unclear scope, undefined terms, missing edge cases, subjective language, and incomplete specifications. Provides detailed analysis with clarifying questions and suggested improvements.

Mental-model reset for Rust. Use when writing or reviewing Rust code to shift from "it compiles" to "thinks in Rust." Triggers on Rust code review, "is this idiomatic", borrow-checker errors, API design, domain modeling, ownership, lifetimes, errors, traits, async/Tokio, unsafe, serde, FFI, tests, performance, Cargo structure, .rs files, Cargo.toml, rustc diagnostics, clippy findings, Result/Option, thiserror vs anyhow, newtype, typestate, enum vs trait, dyn Trait, Send/Sync, Pin, Miri, PyO3, napi-rs, cxx, UniFFI, wasm-bindgen, serde attributes, or feature unification.

Implement, deploy, and index ERC20 and ERC721 tokens in Dojo. Use when adding token contracts, deploying them, or configuring Torii to index balances and transfers.

Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Download YouTube videos and HLS streams (m3u8) from platforms like Mux, Vimeo, etc. using yt-dlp and ffmpeg. Use this skill when users request downloading videos, extracting audio, handling protected streams with authentication headers, or troubleshooting download issues like nsig extraction failures, 403 errors, or cookie extraction problems.

This skill should be used when setting up, auditing, or enforcing internationalization/localization in UI codebases (React/TS, i18next or similar, JSON locales), including installing/configuring the i18n framework, replacing hard-coded strings, ensuring en-US/zh-CN coverage, mapping error codes to localized messages, and validating key parity, pluralization, and formatting.

The Data Analyst specialist for Headout's PM OS. Engage this skill whenever a PM needs to understand existing user behavior before (or while) defining a solution. It operates in two modes: Two modes: MODE A — Question Bank: generates the behavioral questions worth answering for a given problem (what to measure, what cuts matter, what would confirm or refute the hypothesis). MODE B — Query Runner: writes and executes BQ queries, interprets results, produces a behavioral insights brief. Trigger for: "what does the data say about X", "pull some numbers on Y", "understand user behavior before writing the spec", "what should I be measuring", "build a data picture of this funnel stage", or any time a PM needs behavioral evidence before committing to a solution.

Automatically generate clear, actionable issue reports from failing tests and repository analysis. Analyze test failures to understand expected vs. actual behavior, identify affected code components, and produce well-structured Markdown reports suitable for GitHub Issues or similar trackers. Use when a test fails, when debugging issues, or when the user asks to create an issue report, generate a bug report, or document a test failure.

Generate and edit images using OpenAI's GPT Image 1.5 model. Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports text-to-image generation and image editing with optional mask. DO NOT read the image file first - use this skill directly with the --input-image parameter.

Andrej Karpathy的思维框架与表达方式。基于20+篇博文、16段深度访谈、100+条X帖子的系统蒸馏， 提炼6个核心心智模型、8条决策启发式、完整的中文输出适配和经典句式速查。 用途：作为思维顾问，用Karpathy的视角分析AI技术可靠性、学习方法、行业趋势、产品设计。 当用户提到「用Karpathy的视角」「Karpathy会怎么看」「卡帕西」「karpathy模式」时使用。 也适用于：Software 2.0/3.0讨论、vibe coding话题、神经网络训练、AI炒作判断、LLM能力边界。 即使用户只是说「从工程现实主义角度」「march of nines」「构建即理解」「锯齿状智能」也可触发。 不在用户只是普通问AI相关问题时触发——只在明确想要Karpathy式思维框架时激活。