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Found 213 Skills
Grafana Pyroscope continuous profiling platform. Covers instrumentation of Go/Java/Python/Ruby/Node.js/ .NET/Rust apps via SDKs or eBPF (Alloy), flame graph analysis, ProfileQL queries, server configuration and architecture, Grafana Cloud Profiles integration, and trace-profile linking (Span Profiles). Use when working with profiling data, instrumenting apps for Pyroscope, analyzing performance profiles, or deploying Pyroscope server.
Use this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.
Generate end-to-end investment proposals covering risk profiling, model portfolio recommendation, fee illustration, projections, and compliance review. Use when the user asks about creating a proposal for a prospect, mapping risk questionnaire scores to model portfolios, building fee illustrations with tiered costs, producing Monte Carlo or scenario projections, analyzing a prospect's current portfolio for improvement opportunities, reviewing proposals for SEC Marketing Rule compliance, or designing proposal templates for a multi-advisor firm. Also trigger when users mention 'investment proposal', 'proposal generation', 'risk profiling', 'Riskalyze', 'Nitrogen', 'fee illustration', 'transition analysis', 'current vs proposed portfolio', or 'proposal compliance review'.
Guide for adding a new benchmark or training environment to NeMo-Gym. Use when the user asks to add, create, or integrate a benchmark, evaluation, training environment, or resources server into NeMo-Gym. Also use when wrapping an existing 3rd-party benchmark library. Covers the full workflow: data preparation, resources server implementation, agent wiring, YAML config, testing, and reward profiling (baselining). Triggered by: "add benchmark", "new resources server", "integrate benchmark", "wrap benchmark", "add training environment", "add eval".
Flutter Performance. Use when optimizing performance or profiling code.
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, and provides recommendations. Activate on 'learn patterns', 'execution patterns', 'what worked', 'optimize based on history', 'pattern analysis'. NOT for failure analysis (use dag-failure-analyzer) or performance profiling (use dag-performance-profiler).
Automates benchmark test creation for C++ projects using Google Benchmark with consistent software testing patterns. Use when creating performance benchmarks, profiling tests, or when the user mentions benchmarking, Google Benchmark, or performance testing.
Apply systematic performance optimization techniques when writing or reviewing code. Use when optimizing hot paths, reducing latency, improving throughput, fixing performance regressions, or when the user mentions performance, optimization, speed, latency, throughput, profiling, or benchmarking.
Correlates performance targets with actual profiling results. Identifies bottlenecks and validates against non-functional requirements.
Comprehensive epigenomics and gene regulation analysis integrating ENCODE functional genomics data, JASPAR transcription factor binding motifs, SCREEN cis-regulatory elements, ReMap TF binding sites, RegulomeDB variant regulatory scoring, 4D Nucleome chromatin conformation, and Ensembl regulatory features. Performs regulatory element cataloging, transcription factor analysis, variant regulatory impact scoring, chromatin conformation mapping, and gene-centric regulatory landscape profiling. Use when asked about gene regulation, enhancers, promoters, transcription factor binding, epigenetic modifications, chromatin structure, regulatory variants, or non-coding genome function.
Exploratory Data Analysis (EDA): profiling, visualization, correlation analysis, and data quality checks. Use when understanding dataset structure, distributions, relationships, or preparing for feature engineering and modeling.