研究与知识

book-study

Reading coach: guides users through books systematically with knowledge compilation, mastery testing, spaced repetition, and knowledge querying. Use when user says 'read this book with me', 'book study', 'start studying X', 'reading plan', 'ingest this chapter', 'review what I read', 'quiz me on the book', 'what did the book say about X', or invokes /book-study. Supports sub-commands: ingest, query, review, compare, status. Triggers: book, study, read, chapter, ingest, review, quiz, reading plan, book notes.

sanyuan0704

swiftui-performance-audit

通过代码审查和架构分析，审计并提升 SwiftUI 运行时性能。适用于诊断 SwiftUI 应用中的慢渲染、滚动卡顿、高 CPU/内存使用、视图过度更新或布局抖动等问题，并在仅靠代码审查不足以定位时，指导用户使用 Instruments 进行性能分析。

dimillian

gemini-interactions-api

在编写调用 Gemini API 进行文本生成、多轮对话、多模态理解、图像生成、视频生成、流式响应、后台研究任务、函数调用、结构化输出或从旧的 generateContent API 迁移时，使用此技能。本技能涵盖 Interactions API，这是在 Python 和 TypeScript 中使用 Gemini 模型和代理的推荐方式。

google-gemini

skill-forge

为 Claude Code 打造高质量、生产级的 Skill。在 Skill 架构设计、工作流规划、提示词工程和打包构建方面提供专家级指导。当用户想要创建新 Skill、构建 Skill、设计 Skill、编写 Skill、更新现有 Skill、优化 Skill、重构 Skill、调试 Skill 或打包 Skill 时使用。触发词：'create skill', 'build skill', 'new skill', 'skill creation', 'write a skill', 'make a skill', 'design a skill', 'improve skill', 'package skill', 'skill development', 'skill template', 'skill best practices', 'write SKILL.md'。

sanyuan0704

gemini-api-dev

3.7Kresearch-knowledge

在构建使用 Gemini API 托管模型（包括 Gemini 和 Gemma 4）的应用程序、处理多模态内容（文本、图像、音频、视频）、实现函数调用、使用结构化输出或需要当前模型规格时，使用此技能。涵盖 SDK 用法（Python 的 google-genai、JavaScript/TypeScript 的 @google/genai、Java 的 com.google.genai:google-genai、Go 的 google.golang.org/genai）、模型选择和 API 功能。

google-gemini

update-swiftui-apis

3.4Kresearch-knowledge

Scan Apple's SwiftUI documentation for deprecated APIs and update the SwiftUI Expert Skill with modern replacements. Use when asked to "update latest APIs", "refresh deprecated SwiftUI APIs", "check for new SwiftUI deprecations", "scan for API changes", or after a new iOS/Xcode release. Requires the Sosumi MCP to be available.

avdlee

jetson-print-bsp-info

Use when you need to print Jetson BSP info (L4T version, board configs, rootfs state) from a Linux_for_Tegra root on the host PC. This is an example skill.

jetson-print-device-info

Use when you need to print Jetson device info (module model, L4T version, kernel, OS version, current power mode) from a running Jetson target. This is an example skill.

jetson-customize-fan

Use when you need to add, remove, edit, list, or change the boot default of an nvfancontrol fan profile on a Jetson/Tegra (Orin, Thor) target. Triggers: edit fan profile, tune fan curve.

jetson-customize-usb

Enable/disable Jetson USB2/USB3 SS ports via kernel-DT overlay. Do NOT use for UPHY lane allocation or ODMDATA edits.

jetson-init-image

Extract Jetson Linux + sample-rootfs tarballs and run apply_binaries.sh for the active target, then record bsp_image in the profile. Use after jetson-init-target; not for source-tree setup.

wiki-research

Autonomously research a topic via multi-round web search, synthesize findings, and file structured results into the Obsidian wiki. Use this skill when the user says "/wiki-research [topic]", "research X", "find everything about Y", "do a deep dive on Z", "autonomous research on X", or wants comprehensive, web-sourced knowledge on a topic filed directly into their wiki.

ar9av

jetson-customize-nvpmodel

3.2Kresearch-knowledge

Use when you need to add, remove, edit, list, or change the boot default of an nvpmodel power mode on a Jetson/Tegra (Orin, Thor) target. Triggers: edit power mode, tune frequency caps.

nemoclaw-user-guide

3.2Kresearch-knowledge

Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.

cuopt-numerical-optimization-api-c

3.2Kresearch-knowledge

LP, MILP, and QP (beta) with cuOpt — C API only. Use when the user is embedding LP, MILP, or QP in C/C++.

avoid-ai-writing

Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detect-only mode, an edit-in-place mode for files, an optional voice profile (casual / professional / technical / warm / blunt), and an iterate-to-convergence pass.

conorbronsdon

infocard

使用 Markdown 中的 HTML/CSS 创建编辑风格的信息卡片。最适合知识摘要、数据亮点、活动公告以及具有杂志级排版的单主题内容卡片。

markdown-viewer

infographic

使用空格分隔的键值对语法（非YAML）创建基于模板的信息图。最适合KPI仪表盘、时间线、路线图、SWOT分析、漏斗图、对比图和组织架构图，快速实现视觉冲击。

markdown-viewer

tao-finetune-clip

CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX

google-agents-cli-workflow

此技能应在用户想要“开发智能体”、“使用 ADK 构建智能体”、“本地运行智能体”、“调试智能体代码”、“测试智能体”、“部署智能体”、“发布智能体”、“监控智能体”，或需要 ADK（智能体开发工具包）开发生命周期和编码指南时使用。构建 ADK 智能体的入口点。始终处于活动状态——提供完整的工作流程（脚手架、构建、评估、部署、发布、观察）、代码保留规则、模型选择指南以及 ADK 或任何智能体开发的故障排除步骤。

google

google-agents-cli-eval

当用户想要“运行评估”、“评估我的ADK智能体”、“编写评估数据集”、“分析评估失败原因”、“比较评估结果”、“优化智能体”，或需要Agent Platform评估方法论和质量飞轮方面的指导时，应使用此技能。涵盖评估指标、数据集模式、LLM作为评判的评分以及常见失败原因。不要用于API代码模式（使用google-agents-cli-adk-code）、部署（使用google-agents-cli-deploy）或项目脚手架（使用google-agents-cli-scaffold）。

google

tao-finetune-huggingface-model

Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Supports image classification, object detection, semantic / instance / panoptic segmentation, depth estimation, image-text-to-text VLM (SFT / LoRA), and LLM SFT / DPO / GRPO. Six-step workflow: inspect and qualify, hardware and NGC image, research, generate and smoke, train + eval + infer, push and emit rerun skill.

tao-finetune-cosmos-embed

Cosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning. Use when the user asks to "fine-tune Cosmos-Embed1", "run cosmos-embed inference", "export Cosmos-Embed1", "embed videos", or "search videos with text".