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Found 1,781 Skills
Detect performance anti-patterns and apply optimization techniques in Go. Covers allocations, string handling, slice/map preallocation, sync.Pool, benchmarking, and profiling with pprof. Use when checking performance, finding slow code, reducing allocations, profiling, or reviewing hot paths. Trigger examples: "check performance", "find slow code", "reduce allocations", "benchmark this", "profile", "optimize Go code". Do NOT use for concurrency correctness (use go-concurrency-review) or general code style (use go-coding-standards).
Build Rust code with proper error handling and optimization for development, testing, and production
Audit and optimize OpenClaw API costs. Applies six proven optimizations — model routing, prompt caching, lean context, local heartbeats, rate limits, and workspace trimming — to cut monthly spend by up to 90%. Use when asked to reduce costs, optimize tokens, audit API spend, or configure cost-saving settings.
Implements Syncfusion TreeViewAdv control for Windows Forms applications to display hierarchical tree structures, file explorers, organization charts, and nested data. Use this when working with hierarchical data, parent-child relationships, expandable nodes, file browser interfaces, or folder structures. The skill covers data binding, drag-and-drop, node customization, editing capabilities, and performance optimization for tree structures in WinForms.
Review, refactor, or build SwiftUI features with correct state management, modern API usage, optimal view composition, navigation patterns, performance optimization, and testing best practices.
Alibaba Cloud RDS Copilot intelligent operations assistant skill. Used for RDS-related intelligent Q&A, SQL optimization, instance operations, and troubleshooting. Calls RdsAi OpenAPI through Alibaba Cloud CLI to get real-time RDS Copilot responses. Triggers: "RDS Copilot", "RDS Assistant", "SQL optimization", "RDS troubleshooting", "RDS operations", "database diagnosis"
Dockerfile optimization guidelines from official Docker documentation. This skill should be used when writing, reviewing, or refactoring Dockerfiles to ensure optimal build time, image size, security, and robustness. Triggers on tasks involving Dockerfile creation, Docker image builds, container optimization, multi-stage builds, build cache, or Docker security hardening.
When the user wants to create, optimize, or improve sales presentations, pitch decks, demos, or proposal presentations. Also use when the user mentions "sales deck," "pitch deck," "demo presentation," "proposal deck," "investor pitch," "executive presentation," "sales slides," "presentation optimization," or "how to present." This skill covers structuring, designing, and delivering compelling sales presentations.
Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation. Use this skill when the user needs to improve search quality, implement query processing features, or diagnose search relevance issues — even if they say 'search results are bad', 'improve product search', or 'search relevance optimization'.
Troubleshoot and optimize the performance of Ascend C operators. This skill is applicable when users develop, review or optimize Ascend C kernel operators, or triggered when users mention keywords such as Ascend C performance optimization, operator optimization, tiling, pipeline, data copy, memory optimization, NPU/Ascend.
昇腾（Ascend）推理生态开源代码仓库智能问答专家旨在为 vLLM、vLLM-Ascend、MindIE-LLM、MindIE-SD、MindIE-Motor、MindIE-Turbo 以及 msModelSlim (MindStudio-ModelSlim) 等仓库提供专家级且易于理解的解释。在处理昇腾（Ascend）推理生态相关项目的用户询问时，务必触发此技能（Skill），可解答使用方法、部署流程、支持模型、支持特性、系统架构、配置管理、调试、测试、故障排查、性能优化、定制开发、源码解析以及其他技术问题。支持中英文双语回复，并可借助 deepwiki MCP 工具检索仓库知识库，生成具备上下文感知且基于证据的回答。Ascend inference ecosystem open-source code repository intelligent question-and-answer (Q&A) expert. Provide expert-level yet comprehensible explanations for repositories such as vLLM, vLLM-Ascend, MindIE-LLM, MindIE-SD, MindIE-Motor, MindIE-Turbo, and msModelSlim (MindStudio-ModelSlim). Use this skill when addressing user inquiries related to these Ascend inference ecosystem projects, including topics such as usage, deployment process, supported models, supported features, system architecture, configuration management, debugging, testing, troubleshooting, performance optimization, custom development, source code analysis, and any other technical issues about these projects. Support responses in both Chinese and English. Use deepwiki MCP tools to query repository knowledge bases and generate context-aware, evidence-based responses.
V8 JIT Compilation, TurboFan, Maglev, Sparkplug. Load this when needing to understand V8's compilation pipeline, JIT optimization, or JITless mode.