Results for “profiling”

Performance profiling principles. Measurement, analysis, and optimization techniques.

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Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.

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Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.

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Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.

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Optimize application performance through code splitting, lazy loading, caching strategies, bundle size reduction, render optimization, and profiling. Use when improving page load times, reducing bundle sizes, optimizing React rendering, implementing code splitting, configuring caching strategies, lazy loading components and routes, optimizing images and assets, profiling performance bottlenecks, implementing virtual scrolling for large lists, or improving Core Web Vitals and Lighthouse scores.

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Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.

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Performance optimization and measurement for .NET applications. Navigation skill covering Span, ArrayPool, memory management, benchmarking, profiling, Native AOT, and optimization patterns. For building high-performance applications. Keywords: performance, optimization, span, arraypool, benchmarking, profiling, memory, gc, aot, native-aot

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Identifies API latency hotspots and bottlenecks with profiling tools, slow endpoint detection, suspected causes, and fix roadmap. Use for "latency profiling", "performance bottlenecks", "slow APIs", or "backend performance".

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Find and fix slow page loads, API responses, and database queries using measurements. Compare results before and after each change, discard changes without clear gains, and add checks or monitoring to catch future slowdowns.

• Investigating slow page loads and sluggish interactions
• Reducing API delays and repeated database queries
• Checking whether a recent change caused a slowdown

Measure code performance and run benchmarks to investigate slow operations, regressions, or memory pressure. Establish a baseline, compare results under matching conditions, report variability, and rerun correctness tests after optimization.

• Investigate slow operations
• Compare performance before and after a change
• Measure performance when memory pressure occurs

Research competitors from their website URLs and turn findings into consistent Markdown profiles. Compare features, pricing, positioning, and available SEO data, with dated sources and a summary that brings multiple competitors together.

• Compare competitors' features and pricing
• Research competitors' messaging and search visibility
• Create sourced competitor profiles for the team

Find and qualify prospective customers for B2B, SaaS, local businesses, or early-stage products. Define target criteria, gather supporting sources, verify email contacts, and prioritize prospects in a lead sheet or an evidence report for first-customer discovery.

• Build a prioritized list of B2B prospects
• Find local businesses in a target area
• Discover first customers from public demand signals

Create product comparison and alternative pages that help buyers evaluate options. Organize pricing, features, use cases, and migration details, and audit existing competitive claims for outdated information, missing evidence, or wording that overstates the facts.

• Creating a page comparing your product with a competitor
• Planning a list of alternative products
• Checking existing comparison claims for accuracy

Find biological pathways and functions enriched in gene lists or ranked gene data. Choose an appropriate statistical method, align gene identifiers, define comparison backgrounds, and turn results into interpretable tables and plots with multiple-testing corrections.

• Finding enriched pathways among differentially expressed genes
• Running GSEA on a complete ranked gene list
• Calculating gene-set scores for individual samples or cells

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs

Coordinate specialist agents to investigate and improve application performance across databases, backend services, frontend code, and delivery infrastructure. Establish baseline measurements, address observed bottlenecks, validate changes with load tests, and set up monitoring to detect future slowdowns.

• Finding bottlenecks across an entire application
• Coordinating database, backend, and frontend improvements
• Validating performance changes with load tests

Improve workflows involving multiple AI agents by measuring bottlenecks, adjusting workload distribution, and managing model costs. This design guide helps compare changes against a measured baseline while checking quality, latency, and resource use.

• Finding bottlenecks in workflows involving multiple agents
• Reviewing task distribution and parallel execution
• Reducing model costs within quality thresholds

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

Investigate bugs and slow applications through reproducible examples, logs, environment comparisons, and hypothesis testing. The skill structures root cause analysis and performance measurement, helping developers narrow down failures and verify that a fix works.

• Investigate bugs that occur only occasionally
• Measure and locate performance bottlenecks
• Collect evidence and reproduce production failures

Prepare concise Word profiles of public or private companies using S&P Capital IQ data. It adapts business and financial information for equity research, M&A, acquisition planning, or sales meetings, with checked calculations and clearly marked missing data.

• Preparing a company summary for equity research
• Profiling an M&A or acquisition target
• Preparing company background for a sales meeting

Control and inspect a live Chrome browser through Chrome DevTools MCP. Automate page interactions, capture screenshots, inspect errors and network requests, and record performance data to investigate slow loading and unexpected layout changes.

• Automating navigation and form entry
• Capturing page screenshots and text snapshots
• Investigating console errors and failed requests

Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.

• Measuring diffusion denoising latency
• Ranking slow operations from profiler traces
• Checking whether existing optimizations address a bottleneck

Analyze LLM serving traces to identify GPU time usage, opportunities to run operations concurrently, and known patterns for combining operations. Produce three consistent tables from saved traces or live captures on supported servers.

• Identify GPU operations that dominate inference time
• Compare input processing and token generation
• Trace GPU operations back to Python code

Plan and run authorized security tests that simulate deceptive emails, calls, text messages, and in-person approaches. Measure employee responses, compare results across test methods, and report gaps in security awareness training and access controls.

• Planning an authorized social engineering assessment
• Measuring responses to simulated phishing
• Evaluating phone and physical access checks