'diagnostic' 검색 결과

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

Interpret MSBuild performance summaries when ResolveProjectReferences appears unusually expensive. Separate time spent waiting for dependent projects from actual task work, then use task self-time rankings to identify a concrete optimization target.

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• Investigating a high ResolveProjectReferences time
• Separating dependency wait time from task work
• Finding the actual bottleneck in a build log

Capture MSBuild execution records as binary logs during .NET builds, tests, packaging, publishing, or restores. It explains unique filenames, PowerShell quoting, and file checks so logs remain available for later failure or performance analysis.

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• Saving logs before investigating a build failure
• Keeping separate logs for Debug and Release builds
• Choosing log filenames for CI artifact uploads

Establish repeatable performance measurements for MSBuild and .NET builds before optimizing. Compare clean-output, changed-source, and unchanged builds, record timing ranges, and assess output layouts, dependency references, and parallel build options against the measured baseline.

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• Measure slow builds before choosing an optimization
• Compare build times before and after changes
• Check rebuild behavior after a small source edit

Fit regression and time series models in Python, then examine coefficients, uncertainty, and predictions. Check model assumptions, investigate unusual observations, and compare alternatives using detailed statistical summaries and diagnostic tests.

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• Interpret regression coefficients and confidence intervals
• Model binary outcomes or event counts
• Forecast time series with uncertainty intervals

Diagnose a Diverga installation by checking plugin registration, skill and cache synchronization, hooks, configuration, API key setup, and research project state. Receive pass, warning, or error results with suggested repair commands, without modifying files.

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• Diagnose Diverga setup problems
• Check skill synchronization and stale plugin caches
• Verify hook execution and configuration

Analyze U.S. electoral district plans in R with redistverse. Guides data preparation, simulations under population and geographic constraints, diagnostic checks, and comparisons of district shape, partisan fairness, and administrative splits through maps and summary tables.

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• Preparing census, election, and geographic data
• Simulating district plans under population constraints
• Comparing enacted plans with simulated alternatives

Plan, run, and interpret linear regression with guidance on model choices, diagnostic checks, and reporting. Python, R, and Stata examples help examine coefficients, assess assumptions, and choose standard errors that account for different error patterns.

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• Specify a regression model for an empirical study
• Check residuals and correlations among explanatory variables
• Choose robust or clustered standard errors

Plan and implement instrumental variable estimation and propensity score matching, methods for estimating causal effects under different assumptions. Includes Python, R, and Stata examples, diagnostic checks, and guidance on interpreting and reporting results.

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• Choose between instrumental variables and matching
• Implement two-stage least squares estimation
• Check weak instruments and estimation diagnostics

Build and evaluate Bayesian models with PyMC and ArviZ, from choosing initial assumptions to checking predictions. The workflow covers sampling diagnostics, model comparison, and reports that explain results with uncertainty intervals.

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• Choose and check prior distributions
• Investigate sampling and convergence problems
• Compare hierarchical or regression models

Build a reproducible Stata analysis from data cleaning through modeling, diagnostic checks, and robustness tests. Generate runnable .do files, regression tables, and figures for empirical papers, with additional workflows for epidemiology and machine learning approaches to causal inference.

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• Building a reproducible Stata analysis pipeline
• Comparing regression specifications in a paper table
• Checking causal estimates with robustness tests

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

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• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Use this skill when editing or creating CLI output, logging, warnings, error messages, progress indicators, or diagnostic summaries in the APM codebase. Activate whenever code touches console helpers (_rich_success, _rich_warning, _rich_error, _rich_info, _rich_echo), DiagnosticCollector, STATUS_SYMBOLS, CommandLogger, or any user-facing terminal output — even if the user doesn't mention "logging" or "UX" explicitly.

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Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.

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Golang semantic code intelligence via `gopls`, the official Go language server — go-to-definition, find references, call/implementation hierarchy, workspace symbol search, package API discovery, diagnostics, safe rename, refactors (extract/inline/fill/rewrite code actions), formatting, and generated tests. Reaches an agent via gopls's own MCP server (`go_*` tools), Claude Code's native `LSP` tool, or the `gopls` CLI. Use when navigating or refactoring Go code — jumping to a definition, finding call sites before a rename, understanding a file's or package's dependencies, running diagnostics after an edit, or extracting/inlining/renaming. Not for the published ecosystem — packages not in your `go.mod`, versions, licenses, importers — → See `samber/cc-skills-golang@golang-pkg-go-dev` skill (`godig`). Not for a whole-tree vulnerability audit → See `samber/cc-skills-golang@golang-security` skill (`govulncheck`).

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Use when the user asks to "set up my founder social-selling routine", "build a daily engagement block for target accounts", or "turn funding / hiring signals into selling plays"; produces the founder/seller daily operating block — a time-boxed engagement-block spec (substantive value-add comments on target-account posts, never a pitch), warm-touch-before-ask cadence rules, trigger-response plays consuming the social-pulse-monitor B2B trigger watchlist (funding / hiring / launch signals), and a quarterly diagnostic that reads LinkedIn SSI as an input diagnostic only (Estimated, vendor-defined — never a target metric). All volume norms are Estimated/community-derived; all execution is human — zero mass-DM, zero connection-request automation, zero engagement automation (LinkedIn UA §8.2 red line); 1:1 pitch and DM mechanics route to outreach-manager. Not for cold email sequences — use cold-outbound-sequencer. 社交销售/创始人日常互动块/热身触达节奏/触发信号剧本

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Collect Apollo.io debug evidence for support. Use when preparing support tickets, documenting issues, or gathering diagnostic information for Apollo problems. Trigger with phrases like "apollo debug", "apollo support bundle", "collect apollo diagnostics", "apollo troubleshooting info".

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Collect Anima SDK debug evidence for support tickets and troubleshooting. Use when filing Anima support requests, debugging code generation issues, or collecting diagnostic data for the Anima team. Trigger with: "anima debug bundle", "anima support ticket", "anima diagnostics".

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Collect Anthropic Claude API debug evidence for support and troubleshooting. Use when encountering persistent API issues, preparing support tickets, or collecting diagnostic information including request IDs and rate limit headers. Trigger with phrases like "anthropic debug", "claude debug bundle", "collect anthropic logs", "anthropic diagnostic", "claude support ticket".

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Use when evaluating diagnostic biomarker performance from case-control expression data with logistic regression and ROC curves, exporting coefficient and AUC tables together with a ROC PDF. NOT for: survival analysis, time-to-event outcomes, multiclass classification, calibration curves, decision-curve analysis, or nomogram construction.

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Gangtise (岗底斯投研) OpenAPI skill suite installer and diagnostic tool. One-click install 19 official skills (data, research, utility), configure accessKey/secretAccessKey, run health diagnostics. Trigger when user mentions Gangtise, 岗底斯, any gangtise-* skill, credential setup, or reports errors like 'token is invalid' / '接口地址错误'.

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Optimize MATLAB design files for GPU Coder to generate faster CUDA code. Iteratively profiles, rewrites, and benchmarks until performance targets are met or diagnostics are resolved. Use when asked to: optimize for GPU Coder, improve GPU codegen performance, profile generated GPU/CUDA code, profile GPU MEX, fix gpuPerformanceAnalyzer diagnostics, speed up GPU MEX, reduce GPU memory transfers, improve kernel parallelism, rewrite MATLAB for CUDA, or run gpuPerformanceAnalyzer.

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Triage TestFlight crashes, beta feedback, and performance diagnostics using asc. Use when the user asks about TF crashes, TestFlight crash reports, beta tester feedback, app hangs, disk writes, launch diagnostics, or wants a crash summary for a build or app.

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Design a diagnostic hinge question that reveals whether students understand enough to move on. Use when planning key checkpoints mid-lesson during explicit or direct instruction.

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