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End-to-end shotgun metagenomics workflow from FASTQ to taxonomic and functional profiles, orchestrating controls/host depletion, Kraken2+Bracken classification, MetaPhlAn marker profiling, and HUMAnN functional profiling. Covers the controls-first ordering, why Kraken2 read counts are not abundances and MetaPhlAn cell fractions do not equal Bracken read fractions, and the consistent-pipeline framing. Use when profiling shotgun metagenomic samples end to end, or chaining classification, abundance, and function. For resistome see metagenomics/amr-detection; for strains see metagenomics/strain-tracking; for assembly see genome-assembly/metagenome-assembly.

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Launch an SGLang server, run a quick accuracy check, and capture an execution trace for performance inspection. Return the trace location and generated files, ready to open in Perfetto or Chrome tracing.

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• Capture an execution trace of SGLang inference
• Check basic accuracy before profiling
• Record input processing and token generation separately

Map threat groups and their attack techniques to MITRE ATT&CK, a catalog of adversary behavior. Generate Navigator visualization files, compare groups, and overlay detection coverage to identify gaps that can guide defensive planning.

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• Profiling the attack techniques used by a specific threat group
• Comparing techniques across multiple threat groups
• Finding detection gaps against a target adversary

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Guides logging for PyTorch and TensorFlow, including weight distributions and performance profiling to investigate training problems and processing bottlenecks.

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• Monitoring training loss and accuracy
• Comparing experiments with different training settings
• Inspecting weight and gradient distributions

Evaluate NVIDIA Cosmos Policy for robot manipulation in LIBERO and RoboCasa simulations. Follow GPU environment setup, configure rendering without a display, run small checks before larger benchmarks, inspect success rates, and profile inference latency.

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• Running a small LIBERO evaluation
• Evaluating kitchen tasks in RoboCasa
• Setting up simulation on a GPU cluster without a display

Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.

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• Set up application dashboards and alerts
• Investigate production issues using logs, metrics, and traces
• Run load tests before traffic increases

Investigate organizations, domains, media locations, and cryptocurrency flows using open-source intelligence, or OSINT. The skill helps select relevant research tools, connect findings across sources, and preserve evidence with timestamps, screenshots, file hashes, and reproducible logs.

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• Mapping an organization's publicly exposed infrastructure
• Connecting public company records and social profiles
• Investigating image locations and video evidence

Profile app launches and individual user flows in iOS Simulator with ETTrace. Collect traces with readable function names, identify CPU-heavy call stacks, and compare performance changes when recording conditions are consistent.

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• Investigating slow app launches
• Finding CPU-heavy work during a specific interaction
• Comparing performance before and after a change

Investigate slow Apple apps, memory growth, launch delays, and battery drain using Instruments and Xcode diagnostics. Choose measurements for each symptom, identify the responsible code, and compare results before and after targeted fixes.

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• Investigate UI freezes and stuttering
• Find memory leaks and growing memory use
• Measure and improve app launch time

Investigate observed slowdowns or increased CPU, memory allocation, and IO costs in a process you control. Use controlled timing and runtime-specific profiling tools to test a cause, then check performance and behavior after a fix.

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• Investigate a command that has become slower
• Locate CPU or memory allocation hotspots
• Separate transport waits from backend processing costs

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Guides logging for PyTorch and TensorFlow, including weight distributions and performance profiling to investigate training problems and processing bottlenecks.

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• Monitoring training loss and accuracy
• Comparing experiments with different training settings
• Inspecting weight and gradient distributions

Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while `samber/cc-skills-golang@golang-performance` provides the optimization patterns.

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Marker gene-based taxonomic profiling using MetaPhlAn 4. Provides accurate species-level relative abundances using clade-specific markers. Use when accurate taxonomic profiling is needed and computational resources are limited, or for comparison with HMP/other MetaPhlAn studies.

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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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Expert blueprint for performance profiling and optimization (frame drops, memory leaks, draw calls) using Godot Profiler, object pooling, visibility culling, and bottleneck identification. Use when diagnosing lag, optimizing for target FPS, or reducing memory usage. Keywords profiling, Godot Profiler, bottleneck, object pooling, VisibleOnScreenNotifier, draw calls, MultiMesh.

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Expert debugging and profiling for leaks, GPU/Visual Profiler, headless CI QA, orphan nodes, thread-safe logs, and custom Debugger monitors — not print/breakpoint tutorials. Trigger on OBJECT_ORPHAN_NODE_COUNT, ObjectDB growth, Visual Profiler GPU spikes, flaky headless exits, or remote device consoles. Keywords: orphan nodes, Performance.get_monitor, Visual Profiler, Time.get_ticks_usec, EditorDebuggerPlugin, headless QA, push_error, backtrace.

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Expert blueprint for performance profiling and optimization (frame drops, memory leaks, draw calls) using Godot Profiler, object pooling, visibility culling, and bottleneck identification. Use when diagnosing lag, optimizing for target FPS, or reducing memory usage. Keywords profiling, Godot Profiler, bottleneck, object pooling, VisibleOnScreenNotifier, draw calls, MultiMesh.

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Expert debugging and profiling for leaks, GPU/Visual Profiler, headless CI QA, orphan nodes, thread-safe logs, and custom Debugger monitors — not print/breakpoint tutorials. Trigger on OBJECT_ORPHAN_NODE_COUNT, ObjectDB growth, Visual Profiler GPU spikes, flaky headless exits, or remote device consoles. Keywords: orphan nodes, Performance.get_monitor, Visual Profiler, Time.get_ticks_usec, EditorDebuggerPlugin, headless QA, push_error, backtrace.

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Guide performance profiling with Instruments, diagnose hangs, memory issues, slow launches, and energy drain. Use when reviewing app performance or investigating specific bottlenecks.

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When validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for capacity planning, regression detection, and establishing performance SLOs.

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Use dotnet-diagnostics-mcp for MCP-driven .NET CPU, allocation, GC, and test-host profiling. USE FOR: profiling unit tests or investigating runtime bottlenecks. DO NOT USE FOR: writing test assertions, coverage collection, or precise microbenchmark timing. INVOKES: connect the MCP server, select target processes, collect bounded profiles, and attribute hotspots to source.

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Use the open-source free `Asynkron.Profiler` dotnet tool for CLI-first CPU, allocation, exception, contention, and heap profiling of .NET commands or existing trace artifacts. USE FOR: Asynkron.Profiler setup; automation-friendly profiling output; CPU, allocation, exception, contention, and heap investigation. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Use when you need to CAPTURE / PRODUCE XProf (jax.profiler) profiling artifacts from sgl-jax (SGLang on JAX/TPU) for later performance analysis: driving a live server (/start_profile, stage-separated prefill/decode), offline bench_one_batch --profile, or kernel microbench. This skill produces the trace files; it does not analyze them.

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Profiles Python code for performance bottlenecks and memory issues. Use when Python code is slow or when profiling for optimization before a release.

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