Навык описывает процесс использования языковой модели для реверс-инжиниринга, где модель работает как denoising автоэнкодер, итеративно улучшая код C++ на основе ассемблера и предоставленных метаданных (типы, имена функций).
Arduino Nano, QTR sensörler ve MZ80 engel algılayıcı kullanarak siyah zemin üzerinde beyaz çizgiyi takip eden robot için C++ kodu yazar, PID parametrelerini ayarlar ve hız kontrolü sağlar.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
基于ByteHouse MCP Server，生成数据资产目录和血缘分析的技能，用于获取数据库表结构、生成数据资产目录、分析表之间的血缘关系。当用户需要获取ByteHouse数据库的表结构、生成数据资产目录、分析表之间的血缘关系时，使用此Skill。
ByteHouse 多模态检索 Skill，支持文本、图片、视频的向量化存储和混合检索。当用户需要在ByteHouse数据库中进行多模态向量化存储和混合检索时，使用此Skill。
Generate an evidence-grounded article-level mind map as strict JSON.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Interactive post-onboarding tour with adaptive pathways based on available data
A skill that uses GLM-V native grounding capabilities for coordinate conversion, bounding-box visualization, and more. GLM-V native grounding can locate any target specified by the prompt in an image and output relative coordinates normalized to 0-1000 based on image size. Coordinate formats include 2D bounding box (default), 2D points, and 3D bounding box. GLM-V also supports spatiotemporal localization and tracking of multiple prompt-specified targets in videos, outputting 2D bounding boxes per second.
Comprehensive data visualization skill covering visual execution and technical implementation. Includes perceptual foundations, chart selection, layout algorithms, and library guidance. Triggers on: charts, graphs, dashboards, 'visualize', 'plot', data presentation, D3, Recharts, Victory.
Analyze spring-data-relational version changes and identify required updates for the current project. Use when upgrading spring-data-relational versions or syncing with upstream changes.
Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.
Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
Builds AI-native products using OpenAI's development philosophy and modern AI UX patterns. Use when integrating AI features, designing for model improvements, implementing evals as product specs, or creating AI-first experiences. Based on Kevin Weil (OpenAI CPO) on building for future models, hybrid approaches, and cost optimization.
Apply Change Data Capture (CDC) with apply_changes API in Spark Declarative Pipelines. Use when user needs to process CDC feeds from databases, handle upserts/deletes, maintain slowly changing dimensions (SCD Type 1 and Type 2), synchronize data from operational databases, or process merge operations.
Activate ATLAS pattern recognition mode. Identify repeated patterns, extract essential characteristics, and create reusable abstractions from concrete examples.
