Loading...
Loading...
Found 487 Skills
Expert at diagnosing and fixing performance bottlenecks across the stack. Covers Core Web Vitals, database optimization, caching strategies, bundle optimization, and performance monitoring. Knows when to measure vs optimize. Use when "slow page load, performance optimization, core web vitals, bundle size, lighthouse score, database slow, memory leak, optimize performance, speed up, reduce load time, performance, optimization, core-web-vitals, caching, profiling, bundle-size, database" mentioned.
Retrieves MLflow traces using CLI or Python API. Use when the user asks to get a trace by ID, find traces, filter traces by status/tags/metadata/execution time, query traces, or debug failed traces. Triggers on "get trace", "search traces", "find failed traces", "filter traces by", "traces slower than", "query MLflow traces".
Load PROACTIVELY when task involves optimizing speed, reducing bundle size, or improving responsiveness. Use when user says "make it faster", "reduce bundle size", "fix slow queries", "optimize rendering", or "check Core Web Vitals". Covers bundle analysis and tree-shaking, database query optimization (N+1, indexing), React rendering performance (re-renders, memoization), network waterfall optimization, memory leak detection, server-side performance, and Core Web Vitals (LCP, FID, CLS) improvement.
The slogan unpacked — seven readings of 'Manufacturing Intelligence'
Optimize application performance and scalability. Use when investigating slow applications, scaling bottlenecks, or improving response times. Use for profiling, caching, database optimization, frontend performance, and backend tuning.
Use when starting a new Xiaohongshu account from zero, launching first content on fresh account, accelerating initial growth phase, reaching first 1000 followers, or overcoming slow start on new account
Comprehensive guide and toolkit for diagnosing Rspack build issues. Quickly identify where crashes/errors occur, or perform detailed performance profiling to resolve bottlenecks. Use when the user encounters build failures, slow builds, or wants to optimize Rspack performance.
Design meeting rhythms, metric reporting, quarterly planning, and decision-making velocity for scaling companies. Use when decisions are slow, planning is broken, the company is growing but alignment is worse, or leadership meetings consume all time without producing decisions.
Use this skill when implementing SRE practices, defining error budgets, reducing toil, planning capacity, or improving service reliability. Triggers on SRE, error budgets, SLOs, SLAs, toil automation, incident management, postmortems, on-call rotation, capacity planning, chaos engineering, and any task requiring reliability engineering decisions.
Expert knowledge for Azure App Service development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing plans/ASE, configuring auth/TLS/Key Vault, CI/CD slots, VNet integration, or managed identity access, and other Azure App Service related development tasks. Not for Azure Functions (use azure-functions), Azure Container Apps (use azure-container-apps), Azure Spring Apps (use azure-spring-apps), Azure Static Web Apps (use azure-static-web-apps).
Guides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one node', 'need more throughput', 'cluster is slow', 'too many tenants', 'vertical or horizontal', 'how to shard', or 'need to add capacity'.
Databricks SQL query optimizer: analyzes a slow SQL query, rewrites it for speed using SQL-level optimizations only, validates byte-for-byte result equivalence, and benchmarks both versions with statistical significance testing. Use this skill whenever the user wants to optimize, speed up, tune, or benchmark a SQL query on Databricks. Trigger on: "/databricks-sql-autotuner", "optimize this SQL", "make this query faster", "tune my Databricks query", "benchmark SQL on Databricks", "speed up this spark SQL", "SQL performance on Databricks", "EXPLAIN this query", "why is my query slow on Databricks", "SQL query optimization Databricks", or whenever a user pastes a SQL query and mentions performance, slowness, or runtime.