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Found 2,257 Skills
Guides engineering managers through the specific challenges of managing top engineers — produces a four-quadrant ability/confidence diagnostic, the Rock Star vs. Superstar distinction, common mistakes to avoid, a stagnation diagnostic (Diminishing XP), and a Pusher vs. Puller framework for managing burnout and team friction. Use when the user says "rockstar engineer," "superstar," "high performer," "brilliant jerk," "wants promotion," "hardest to manage," "overconfident," "my best developer is burning out," "engineer is frustrated," or "my best developer is pushing me." Do NOT use for standard underperformance (use performance-reviews) or general motivation questions (use engineer-motivation).
Compute and compare investment return metrics including TWR, MWR/IRR, CAGR, and annualized returns. Use when the user asks about portfolio performance calculation, comparing manager returns, linking sub-period returns, understanding why different return methods give different numbers, or converting returns across time periods. Also trigger when users mention 'how much did I make', 'annual return', 'compound growth', 'dollar-weighted vs time-weighted', 'what was my rate of return', 'geometric vs arithmetic mean', 'log returns', or ask about the effect of cash flows on reported returns.
Performance analysis coordination workflow. Guides profiling delegation, bottleneck classification (compute/memory/launch/communication/sync), and structured report generation. Use when the user asks to analyze performance, profile a workload, check MFU/SOL, or diagnose bottlenecks.
Prepare for client review meetings with portfolio performance summary, allocation analysis, talking points, and action items. Pulls together account data into a concise meeting-ready format. Use before quarterly reviews, annual checkups, or ad-hoc client meetings. Triggers on "client review", "meeting prep for [client]", "quarterly review", "prep for [client name]", or "client meeting".
Alibaba Cloud EMAS APM (mobile Application Performance Monitoring) issue troubleshooting skill. Covers the 4 read-only OpenAPIs exposed by the `aliyun emas-appmonitor` plugin: `get-issues` / `get-issue` / `get-errors` / `get-error`. Capabilities: Top-N aggregation, sample stack drill-down and dimension breakdowns for 6 issue types (crash / anr / lag / custom / memory_leak / memory_alloc), combined with the user's source code (Java / Kotlin / Objective-C / Swift / ArkTS / Dart / C# / JS) to produce root cause analysis and fix suggestions. Client coverage: native Android / iOS / HarmonyOS, Flutter, Unity (bundled to android / iphoneos / harmony; H5 is out of scope). Triggers: analyze app crash, troubleshoot ANR, APM crash investigation, list top issues, "what is this digestHash", iOS ANR Top 5, Android memory leak analysis, Flutter custom exception stacks, pull lag samples, emas appmonitor usage, sort issues by error rate, map stack to source, appKey problem, EMAS APM issue analysis, analyze APM issues.
Run a focused N-question study session on a subject — MBE, essay, or flashcards. Tracks performance and updates the study plan. Use when the user says "run me 10 questions on [subject]", "do a session on [subject]", "let's do 5 cards on [subject]", or wants to drill a fixed number of questions and have the plan adapt.
Use when reviewing a PR/MR diff and producing a structured finding list — covers security, logic, performance, cross-file impact, test coverage, and spec compliance. Posts a sticky summary comment plus inline review comments to the PR. NOT for writing PR descriptions, design reviews requiring business judgment, or deep CVE/supply-chain audits.
Self-improving agent toolkit — forge runtime tools, adapt personality traits, manage skills dynamically, compose multi-step workflows, and self-evaluate performance with bounded autonomy.
Use when the user asks to "create a metric", "write a metric", "design a metric", "build a metric for", "evaluate agent performance", "measure call quality", "track a KPI", "add a workflow metric", "improve my metric", "fix a metric", "debug metric results", "set up quality scoring", or "what metrics do I need". Also relevant when discussing LLM judge prompts, custom code metrics, evaluation triggers, VALID_SKIP patterns, section extraction, or metric best practices for Cekura voice AI agents. Covers both creating new metrics and reviewing, iterating on, or troubleshooting existing ones.
Expert in React state management including Redux Toolkit, Zustand, Jotai, React Query/TanStack Query, and Context API. Use for state architecture decisions, performance issues, or complex state logic.
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
Build sophisticated React animations with Motion (formerly Framer Motion) - declarative animations, gestures (drag, hover, tap), scroll effects, spring physics, layout animations, and SVG manipulation. Optimize bundle size with LazyMotion (4.6 KB) or useAnimate mini (2.3 KB). Use when: adding drag-and-drop interactions, creating scroll-triggered animations, implementing modal dialogs with transitions, building carousels with momentum, animating page/route transitions, creating parallax hero sections, implementing accordions with smooth expand/collapse, or optimizing animation bundle sizes. For simple list animations, use auto-animate skill instead (3.28 KB vs 34 KB). Troubleshoot: AnimatePresence exit not working, large list performance issues, Tailwind transition conflicts, Next.js "use client" errors, scrollable container layout issues, or Cloudflare Workers build errors (resolved Dec 2024).