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Native macOS/iOS app performance profiling via xctrace/Time Profiler and CLI-only analysis of Instruments traces. Use when asked to profile, attach, record, or analyze Instruments .trace files, find hotspots, or optimize native app performance without opening Instruments UI.

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Instruments authentication, authorization, and input-handling code paths to monitor security-relevant events and states at runtime. Use this skill when developers need to add security monitoring and logging to their code, including tracking authentication attempts (login/logout), authorization decisions (access control checks), input validation failures, session management events, and other security-critical operations. Supports Python, JavaScript/TypeScript, and Java with structured logging patterns. Triggers when users ask to add security instrumentation, monitor security events, log authentication/authorization, track security-sensitive operations, or add security observability to their codebase.

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Instruments programs to record execution information for deterministic replay debugging. Use when debugging hard-to-reproduce bugs (race conditions, timing issues, intermittent failures, heisenbugs), reproducing production failures, or analyzing complex execution sequences. Records non-deterministic events (I/O, threading, randomness, time) to enable exact replay of program executions. Supports Python, JavaScript, Java, and C/C++ with both custom instrumentation and existing replay tools.

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Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.

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Think and work like an expert Instrumentation Engineer. Use when a task calls for Instrumentation Engineer judgment. Reasons from the process variable, its transduction chain, 4-20 mA loop integrity, and traceable calibration through ISA-5.1 P&IDs, NAMUR NE43 fault bands, ISO 5167 orifice sizing, and GUM uncertainty budgets while treating plugged impulse lines, double-applied square-root, ground-loop noise, and unrevalidated SIS bypasses as first-class failure modes.

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Think and work like an expert Astronomical Instrumentation Scientist. Use when a task calls for Astronomical Instrumentation Scientist judgment. Reasons from system-level error budgets, the diffraction limit and Strehl ratio, detector figures of merit, and resolving power through Zemax/Code V tolerancing, ETC radiometry, AO modeling, and on-sky standard-star commissioning while treating flexure drift, IR persistence, ghosts, and quasi-static speckles as first-class failure modes.

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Plan, implement, or review observability and instrumentation work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests observability and instrumentation work.

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Plan, implement, or review application insights instrumentation work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests application insights instrumentation work.

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Designs or reviews logs, metrics, traces, alerts, correlation, dashboards, and diagnostic instrumentation so failures and performance changes can be detected and explained at component boundaries. Use for observability work, incident readiness, instrumentation, or production diagnostics. Not for fixing a specific incident before reproducing it or for adding noisy logging without an operational question.

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Instrumentation, arranging, and timbral analysis for ensemble and orchestral writing. Covers instrument families (strings, woodwinds, brass, percussion, keyboards), ranges, transpositions, timbral characteristics, scoring techniques, doubling and voicing, Rimsky-Korsakov's orchestration principles, Ravel's and Messiaen's timbral innovations, jazz big band voicing, popular music arranging, and notation conventions. Use when choosing instruments, scoring for ensembles, analyzing orchestral texture, or arranging music for different forces.

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Use when a task needs the judgment of an Automotive Engineering Technician — setting up and instrumenting test equipment (strain-gauge bridges, thermocouples, load/torque sensors) to an engineer's written test plan, selecting DAQ sample rate and an SAE J211 CFC filter class for a vehicle test channel, verifying instrument calibration (torque wrench, thermocouple, load cell, shunt-cal check on a strain bridge) against a traceable standard before a test run, reducing dyno or road-load test data (SAE J1349 power correction, rainflow-counted durability damage fraction) into engineering units, and documenting test results against the engineer's target with stated margin. Distinct from an automotive engineer (owns the system-level design decisions — structure, powertrain sizing, suspension geometry, NVH) — this role installs the sensors, configures the DAQ, executes the instrumented test, and reduces the data under a design and test plan the engineer specified.

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Proposes the piping content a P&ID adds to an accepted PFD process model: line inventory with off-page continuity and required ancillary lines, line identification elements per the project line numbering procedure, connection items at nozzles matched to a quoted schedule, per quoted standards, specification break questions and observations. Every proposal cites its source and revision; no source, no proposal; never sizes a line or selects a class. Use when the user asks to "run the piping enrichment", "start the discipline enrichment", "list the lines the P&ID adds to this PFD" or "fill the line identifiers from the numbering procedure" after gate G2. Do not use for instruments, loops or instrument tags, use instrumentation-and-control-enrichment instead; do not use before gate G2, use process-model-check. Drafts for human review; never approves, authorises or signs off.

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Reads a process flow diagram (image, PDF or native export) with its job header and returns a provenance-tagged extraction register: equipment, streams, tags, connectivity, instruments already shown, notes and legends, connectivity findings and an explicit UNKNOWN list, then holds at gate G1. Extracts and records only; never enriches, tags or proposes. Use when the user asks to "start a PFD to P&ID job", "read in the attached PFD", "extract the equipment and streams from this PFD" or "read this PFD into a register". Do not use for checking the model, use process-model-check instead; do not use to propose lines, instruments or tags, use piping-enrichment or instrumentation-and-control-enrichment after gate G2. Drafts for human review; never approves, authorises or signs off.

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Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.

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Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.

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Instrument a webapp to send useful telemetry data to Azure App Insights

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Pair trading strategy. Trades mean reversion using the spread/ratio Z-score of two correlated instruments. Requires at least two instruments.

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Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"

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Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation. Triggers: "azure-monitor-opentelemetry", "configure_azure_monitor", "Application Insights", "OpenTelemetry distro", "auto-instrumentation".

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Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.

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Builds, configures, debugs, and optimizes AWS observability - operator-symptom questions and detecting Omni vs classic CloudWatch. CloudWatch on an already-reporting service: Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints/snapshots), the Application Signals service map, and fleet health views. CloudWatch Omni on an existing Space: SQL over logs and traces, PromQL over metrics, Omni dashboards, Omni alerts, context graph for root cause, programmatic/IaC access (API/SDK/CLI/CloudFormation), driving Omni from a coding agent or skills, and evaluating AI agent quality from traces - on-demand and online scoring of live traffic, readback, custom evaluators. For first-time setup - creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals (ServiceEvents, CI/CD metadata) or Omni - use setting-up-cloudwatch-observability. Not for app logging or threat detection.

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Makes systems debuggable and reliably operable — instrumentation, alerting that is worth waking for, service objectives, and learning from failure. Use this to instrument a service, fix alerting that is ignored, set error budgets or reliability targets, prepare for on-call, or run a blameless post-incident review.

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Provides guidance for implementing OpenTelemetry instrumentation in .NET codebases, covering tracing (Activities/Spans), metrics, logs, naming conventions, error handling, performance, SDK setup, resources, context propagation, and API design best practices.

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Profile, diagnose, and remediate SwiftUI runtime performance using code review, Instruments, and repeatable measurements. Use when a SwiftUI screen renders slowly, scrolling or animations hitch, view bodies update excessively, list identity churns, layout work spikes, or broad Observation dependencies raise CPU cost. Covers evidence-based triage, SwiftUI Instruments lanes, lazy-container guardrails, state lifetime, and before/after verification.

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