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Found 1,760 Skills
Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.
Comprehensive marketing expertise combining campaign execution, strategy frameworks, psychological principles, and 140+ marketing tactics. Use when planning campaigns, developing content strategy, applying behavioral science, generating marketing ideas, or building go-to-market strategies. Covers email, social, SEO, paid ads, content, and psychology-driven optimization.
Analyze VictoriaMetrics time series cardinality to find optimization opportunities — unused metrics, high-cardinality labels, problematic label values, histogram bloat. Produces actionable report with relabeling and stream aggregation recommendations. Use whenever the user mentions cardinality analysis, series reduction, unused metrics, high cardinality labels, TSDB optimization, storage cost reduction, metric cleanup, too many time series, or wants to reduce cardinality. Also trigger when discussing relabeling strategies, streaming aggregation opportunities, or "which metrics can we drop".
Expert-level Snowflake data warehouse platform, virtual warehouses, data sharing, streams, tasks, and SQL optimization
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of managing token budgets and session longevity.
Apply Sociotechnical Systems Theory to analyze and design work systems through joint optimization of social and technical subsystems. Use this skill when the user needs to diagnose why a technology implementation disrupted work practices, design IT-enabled work systems that balance human and technical needs, or when they ask 'why did this system hurt productivity despite being technically sound', 'how do we design work around new technology', or 'why are people resisting this technically superior system'.
Application performance profiling and bottleneck identification — Node.js profiling, Chrome DevTools, flame graphs, memory leak detection, CPU profiling, React rendering performance. Activate on "profiling", "performance bottleneck", "flame graph", "memory leak", "slow app", "CPU profiling", "heap snapshot", "React re-renders", "EXPLAIN ANALYZE", "event loop lag", "clinic.js", "Core Web Vitals". NOT for infrastructure monitoring or observability (use logging-observability), load testing (use a load-testing skill), or database schema optimization.
Designs production-grade RAG pipelines with chunking optimization, retrieval evaluation, and pipeline architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, designing embedding pipelines, or evaluating RAG performance with RAGAS metrics.
When the user wants to plan production or distribution capacity, analyze capacity requirements, optimize resource utilization, or balance capacity with demand. Also use when the user mentions "capacity analysis," "resource planning," "bottleneck analysis," "capacity expansion," "load balancing," "throughput planning," "utilization optimization," or "capacity modeling." For production scheduling, see master-production-scheduling. For long-term network capacity, see network-design.
When the user wants to model inventory systems with uncertain demand, optimize safety stock levels, implement (s,S) or (Q,r) policies, or analyze service levels under uncertainty. Also use when the user mentions "stochastic inventory," "probabilistic inventory," "(Q,r) policy," "(s,S) policy," "base stock policy," "safety stock optimization," "service level constraints," "lead time demand distribution," "fill rate calculation," or "inventory with demand uncertainty." For deterministic models, see economic-order-quantity or lot-sizing-problems. For single-period uncertainty, see newsvendor-problem.
Think like Intel's legendary CEO. Apply Andy Grove's management operating system to maximize your team's output through leverage, OKRs, and systematic decision-making. Use when: **Scaling a team** when individual contribution isn't enough; **Performance management** to measure and improve output; **Meeting optimization** to make meetings productive; **Decision-making** in management contexts; **New manager transition** from individual contributor
NCU-driven iterative optimization workflow for CUDA/CUTLASS/Triton/CuTe DSL kernels. MANDATORY: every optimization MUST start with NCU profiling, followed by multi-dimensional analysis, then targeted code modification, then re-profiling to verify. Supports roofline, memory hierarchy, warp stalls, instruction mix, occupancy, divergence analysis. Provides implementation-specific code modifications: Native CUDA (launch config, memory patterns, async copy, Tensor Core), CUTLASS (ThreadblockShape, stages, epilogue, schedule policy, alignment), Triton (autotune params, compiler hints, tl.* API patterns), CuTe DSL (threads_per_cta, elems_per_thread, tiled_copy, copy atom, shared memory, warp/cta reduce). Use when optimizing any CUDA kernel performance.