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Found 2,380 Skills
Prepare advisors for client review meetings by assembling context packages, performance summaries, drift analysis, talking points, and meeting agendas. Use when the user asks about preparing for a client review, building a pre-meeting checklist, generating talking points for an upcoming meeting, identifying allocation drift before a review, automating review prep workflows, or assembling a meeting package with exhibits. Also trigger when users mention 'client meeting prep', 'review preparation', 'what should I discuss with my client', 'proactive recommendations', 'life event triggered review', 'meeting agenda', or 'compliance pre-check before review'.
Real User Monitoring (RUM), Web Vitals, user sessions, mobile crashes, page performance, user interactions, and frontend errors. Query web and mobile frontend telemetry.
Guide for interpreting ResolveProjectReferences time in MSBuild performance summaries. Only activate in MSBuild/.NET build context. Activate when ResolveProjectReferences appears as the most expensive target and developers are trying to optimize it directly. Explains that the reported time includes wait time for dependent project builds and is misleading. Guides users to focus on task self-time instead. Do not activate for general build performance -- use build-perf-diagnostics instead.
Review, refactor, or build SwiftUI features with correct state management, modern API usage, optimal view composition, navigation patterns, performance optimization, and testing best practices.
React rendering performance patterns. Use when reducing re-renders, optimizing memoization, state design, or reviewing React performance.
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.
Helps engineering managers assess and improve team health across morale, cohesion, delivery culture, and engagement — produces Google's 5 Factors (Project Aristotle), a 4-state team health diagnosis (Falling Behind / Treading Water / Repaying Debt / Innovating), a 5-zone intensity model, the Engagement Stack, the Trust Battery, Teamicide patterns (Peopleware), a blameless postmortem format, and a library of team activities organized by driver. Use when the user says "team morale," "team is struggling," "burnout," "engagement," "attrition risk," "psychological safety," "team dynamics," "something feels off," "team culture," "team is unhappy," "retros aren't working," "team isn't working hard enough," "ideas for team activities," or "how do I run a team offsite." Do NOT use for individual performance concerns (use `managing-high-performers`), team staffing or hiring (use `team-composition`), or individual motivation interventions (use `engineer-motivation`).
Enter this sub-process when conducting code optimization — handle tasks where 'behavior remains unchanged, structure changes' (structure / performance / readability). Shift single-module internal optimization from 'AI random refactoring' to 'first scan to generate a checklist, confirm each item with the user, execute step-by-step according to the method library, and require manual approval for each step'. Trigger scenarios: Users mention phrases like 'optimize it / refactor / rewrite / split it / poor performance / code is too long' without any accompanying behavior changes. Do not handle new requirements (route to feature), bugs (route to issue), or cross-module architecture restructuring (route to architecture + decisions).
Scans code for performance and scalability issues — N+1 queries, missing indexes, unbounded queries, memory inefficiencies, caching gaps, algorithmic complexity, concurrency bugs, and frontend performance problems. Generates severity-scored findings with copy-pasteable fix prompts. Trigger phrases: "performance audit", "performance check", "N+1 detection", "query optimization", "slow code", "performance review".
Use when debugging bugs, test failures, build failures, performance regressions, or unexpected behavior and you need root-cause investigation before proposing fixes. Trigger on requests to debug, investigate why something broke, or find the source of a technical issue.
Use these skills when you need to troubleshoot performance bottlenecks, analyze query execution plans, identify resource-heavy processes, and monitor system-level PromQL metrics.
Helps engineering managers measure and improve team delivery — produces a history of why common metrics fail, the DORA four-key-metrics framework (deployment frequency, lead time, change failure rate, MTTR), DevEx's three dimensions (feedback loops, cognitive load, flow state), a translation layer from engineering metrics to business outcomes, and a list of measurement anti-patterns to avoid. Use when the user says "how do I measure productivity," "DORA metrics," "velocity," "cycle time," "developer experience," "DevEx," "how do I show our team is performing well," "metrics for engineering," "team is slow," "engineering performance," or "connect engineering to business." Do NOT use for managing an underperforming individual — use performance-reviews instead.