Validate all intermediate calculations during inference are correct using source of truth values
Skills(SKILL.md)は、AIエージェント(Claude Code、Cursor、Codexなど)に特定の能力を追加するための設定ファイルです。
詳しく見る →Validate all intermediate calculations during inference are correct using source of truth values
Debugging techniques and tools for IntelliJ development. Use when debugging IDE issues or investigating failures.
[COMMUNITY] Generate French public procurement documentation aligned with code de la commande publique, UGAP catalogue, and DINUM digital standards
Roll Playwright Java to a new version
Create, refine, and benchmark agent skills. Use when building a new skill, updating an existing one, running evals, checking trigger quality, or improving a skill description.
Create Claude Code custom slash commands with proper structure and best practices.
Capture technical breakthroughs and transform them into actionable, reusable knowledge assets while context is fresh.
A structured, multi-agent workflow for thorough code reviews on GitHub PRs. The approach uses parallel specialized reviewers, confidence scoring, and false positive filtering to produce high-signal, a
'Analyze development sessions, capture learnings, and improve Claude Code instructions. Use when the user wants to reflect on a session, improve CLAUDE.md, extract learnings, or optimize AI-human collaboration. Supports two modes: quick (default) focuses on CLAUDE.md improvements, deep mode performs comprehensive session analysis with learning capture.'
Use when synthesizing perf findings into evidence-backed recommendations and decisions.
Use when managing perf baselines, consolidating results, or comparing versions. Ensures one baseline JSON per version.
Use when running performance benchmarks, establishing baselines, or validating regressions with sequential runs. Enforces 60s minimum runs (30s only for binary search) and no parallel benchmarks.
Use when mapping code paths, entrypoints, and likely hot files before profiling.
Use when profiling CPU/memory hot paths, generating flame graphs, or capturing JFR/perf evidence.
Use when running controlled perf experiments to validate hypotheses.
Use when generating performance hypotheses backed by git history and code evidence.
Use when completing a plan, finishing a development branch, wrapping up a session, or at any natural transition between work phases — reviews skill-bus telemetry to identify subscription gaps and suggest improvements
Before proposing a fix:
Generate a professional, client-ready marketing services proposal. This skill produces a complete proposal document that positions the agency/consultant as the clear choice, frames pricing with anchor
Creates VS Code custom agent files (.agent.md) for specialized AI personas with tools, instructions, and handoffs. Use when scaffolding new custom agents, configuring agent workflows, or setting up agent-to-agent handoffs.
Scaffolds eval.yaml test files for agent skills in the dotnet/skills repository. Use when creating skill tests, writing evaluation scenarios, defining assertions and rubrics, or setting up test fixture files. Handles eval.yaml generation, fixture organization, and overfitting avoidance. Do not use for running or debugging existing tests nor for skills authoring.
Scaffolds new agent skills for the dotnet/skills repository. Use when creating a new skill, generating SKILL.md files, or setting up skill directory structures. Handles frontmatter generation, section templates, and validation guidance.
A skill with no eval.yaml for testing discovery behavior.
A sample skill for testing the validator. Helps with greeting generation.
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Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
Optimize Entity Framework Core queries by fixing N+1 problems, choosing correct tracking modes, using compiled queries, and avoiding common performance traps. Use when EF Core queries are slow, generating excessive SQL, or causing high database load.
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Symbolicate the .NET runtime frames in an Android tombstone file. Extracts BuildIds and PC offsets from the native backtrace, downloads debug symbols from the Microsoft symbol server, and runs llvm-symbolizer to produce function names with source file and line numbers. USE FOR triaging a .NET MAUI or Mono Android app crash from a tombstone, resolving native backtrace frames in libmonosgen-2.0.so or libcoreclr.so to .NET runtime source code, or investigating SIGABRT, SIGSEGV, or other native signals originating from the .NET runtime on Android. DO NOT USE FOR pure Java/Kotlin crashes, managed .NET exceptions that are already captured in logcat, or iOS crash logs. INVOKES Symbolicate-Tombstone.ps1 script, llvm-symbolizer, Microsoft symbol server.
Symbolicate .NET runtime frames in Apple platform .ips crash logs (iOS, tvOS, Mac Catalyst, macOS). Extracts UUIDs and addresses from the native backtrace, locates dSYM debug symbols, and runs atos to produce function names with source file and line numbers. Automatically downloads .dwarf symbols from the Microsoft symbol server using Mach-O UUIDs. USE FOR triaging a .NET MAUI or Mono app crash from an .ips file on any Apple platform, resolving native backtrace frames in libcoreclr or libmonosgen-2.0 to .NET runtime source code, retrieving .ips crash logs from a connected iOS device or iPhone, or investigating EXC_CRASH, EXC_BAD_ACCESS, SIGABRT, or SIGSEGV originating from the .NET runtime. DO NOT USE FOR pure Swift/Objective-C crashes with no .NET components, or Android tombstone files. INVOKES Symbolicate-Crash.ps1 script, atos, dwarfdump, idevicecrashreport.
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Guide developers through capturing diagnostic artifacts to diagnose production .NET performance issues. Use when the user needs help choosing diagnostic tools, collecting performance data, or understanding tool trade-offs across different environments (Windows/Linux, .NET Framework/modern .NET, container/non-container).
Configure and collect crash dumps for modern .NET applications. USE FOR: enabling automatic crash dumps for CoreCLR or NativeAOT, capturing dumps from running .NET processes, setting up dump collection in Docker or Kubernetes, using dotnet-dump collect or createdump. DO NOT USE FOR: analyzing or debugging dumps, post-mortem investigation with lldb/windbg/dotnet-dump analyze, profiling or tracing, or for .NET Framework processes.
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Analyzes the variety and depth of assertions across .NET test suites. Use when the user asks to evaluate assertion quality, find shallow testing, identify tests with only trivial assertions, measure assertion coverage diversity, or audit whether tests verify different facets of correctness. Produces metrics and actionable recommendations. Works with MSTest, xUnit, NUnit, and TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), detecting anti-patterns (use test-anti-patterns), or fixing existing assertions.
Audits .NET test mock usage by tracing each mock setup through the production code's execution path to find dead, unreachable, redundant, or replaceable mocks. Use when the user asks to audit mock usage, find unused or unnecessary mock setups, check if mocks are needed, reduce mock duplication or over-mocking, simplify test setup, or review whether mock configurations like ILogger/IOptions should use real implementations instead. Supports Moq, NSubstitute, and FakeItEasy.
Optimizes hot-path scalar loops in .NET 8+ with cross-platform Vector128/Vector256/Vector512 SIMD intrinsics, or replaces manual math loops with single TensorPrimitives API calls. Covers byte-range validation, character counting, bulk bitwise ops, cross-type conversion, fused multi-array computations, and float/double math operations.
Performs pseudo-mutation analysis on .NET production code to find gaps in existing test suites. Use when the user asks to find weak tests, discover untested edge cases, check if tests would catch a bug, or evaluate test effectiveness through mutation-style reasoning. Analyzes production code for mutation points (boundary conditions, boolean flips, null returns, exception removal, arithmetic changes) and checks whether existing tests would detect each mutation. Works with MSTest, xUnit, NUnit, and TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), detecting test anti-patterns (use test-anti-patterns), measuring assertion diversity (use exp-assertion-quality), or running actual mutation testing tools.
Detects duplicate boilerplate, copy-paste tests, and structural maintainability issues across .NET test suites. Use when the user asks to reduce repetition, consolidate similar test methods, convert copy-paste tests to data-driven parameterized tests, suggest a better test structure, or identify refactoring opportunities. Identifies repeated construction, assertion patterns, copy-paste methods convertible to DataRow/Theory/TestCase, redundant setup/teardown, and shared infrastructure. Produces an analysis report with concrete before/after suggestions. Works with MSTest, xUnit, NUnit, and TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), reviewing test quality or anti-patterns (use test-anti-patterns), or deep mock auditing (use exp-mock-usage-analysis).
Deep formal test smell audit based on academic research taxonomy (testsmells.org). Detects 19 categorized smell types — conditional logic, mystery guests, sensitive equality, eager tests, and more — with calibrated severity and research-backed remediation. Use for comprehensive test suite health assessments. For a quick pragmatic review, use test-anti-patterns instead. DO NOT USE FOR: writing new tests (use writing-mstest-tests), evaluating assertion quality specifically (use exp-assertion-quality), or finding test duplication and boilerplate (use exp-test-maintainability).
Analyzes test suites and tags each test with a standardized set of traits (e.g., positive, negative, critical-path, boundary, smoke, regression). Use when the user wants to categorize, audit, or label tests with traits. Do not use for writing new tests, running tests, or migrating test frameworks.
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Analyze MSBuild binary logs to diagnose build failures by replaying binlogs to searchable text logs. Only activate in MSBuild/.NET build context. USE FOR: build errors that are unclear from console output, diagnosing cascading failures across multi-project builds, tracing MSBuild target execution order, investigating common errors like CS0246 (type not found), MSB4019 (imported project not found), NU1605 (package downgrade), MSB3277 (version conflicts), and ResolveProjectReferences failures. Requires an existing .binlog file. DO NOT USE FOR: generating binlogs (use binlog-generation), build performance analysis (use build-perf-diagnostics), non-MSBuild build systems. INVOKES: dotnet msbuild binlog replay, grep, cat, head, tail for log analysis.
Generate MSBuild binary logs (binlogs) for build diagnostics and analysis. Only activate in MSBuild/.NET build context. USE FOR: adding /bl:{} to any dotnet build, test, pack, publish, or restore command to capture a full build execution trace, prerequisite for binlog-failure-analysis and build-perf-diagnostics skills, enabling post-build investigation of errors or performance. Requires MSBuild 17.8+ / .NET 8 SDK+ for {} placeholder; PowerShell needs -bl:{{}}. DO NOT USE FOR: non-MSBuild build systems (npm, Maven, CMake), analyzing an existing binlog (use binlog-failure-analysis instead). INVOKES: shell commands (dotnet build /bl:{}).
Guide for optimizing MSBuild build parallelism and multi-project scheduling. Only activate in MSBuild/.NET build context. USE FOR: builds not utilizing all CPU cores, speeding up multi-project solutions, evaluating graph build mode (/graph), build time not improving with -m flag, understanding project dependency topology. Note: /maxcpucount default is 1 (sequential) — always use -m for parallel builds. Covers /maxcpucount, graph build for better scheduling and isolation, BuildInParallel on MSBuild task, reducing unnecessary ProjectReferences, solution filters (.slnf) for building subsets. DO NOT USE FOR: single-project builds, incremental build issues (use incremental-build), compilation slowness within a project (use build-perf-diagnostics), non-MSBuild build systems. INVOKES: dotnet build -m, dotnet build /graph, binlog analysis.
Establish build performance baselines and apply systematic optimization techniques. Only activate in MSBuild/.NET build context. USE FOR: diagnosing slow builds, establishing before/after measurements (cold, warm, no-op scenarios), applying optimization strategies like MSBuild Server, static graph builds, artifacts output, and dependency graph trimming. Start here before diving into build-perf-diagnostics, incremental-build, or build-parallelism. DO NOT USE FOR: non-MSBuild build systems, detailed bottleneck analysis (use build-perf-diagnostics after baselining).
Diagnose MSBuild build performance bottlenecks using binary log analysis. Only activate in MSBuild/.NET build context. USE FOR: identifying why builds are slow by analyzing binlog performance summaries, detecting ResolveAssemblyReference (RAR) taking >5s, Roslyn analyzers consuming >30% of Csc time, single targets dominating >50% of build time, node utilization below 80%, excessive Copy tasks, NuGet restore running every build. Covers timeline analysis, Target/Task Performance Summary interpretation, and 7 common bottleneck categories. Use after build-perf-baseline has established measurements. DO NOT USE FOR: establishing initial baselines (use build-perf-baseline first), fixing incremental build issues (use incremental-build), parallelism tuning (use build-parallelism), non-MSBuild build systems. INVOKES: dotnet msbuild binlog replay with performancesummary, grep for analysis.
Guide for diagnosing and improving MSBuild project evaluation performance. Only activate in MSBuild/.NET build context. USE FOR: builds slow before any compilation starts, high evaluation time in binlog analysis, expensive glob patterns walking large directories (node_modules, .git, bin/obj), deep import chains (>20 levels), preprocessed output >10K lines indicating heavy evaluation, property functions with file I/O ($([System.IO.File]::ReadAllText(...))), multiple evaluations per project. Covers the 5 MSBuild evaluation phases, glob optimization via DefaultItemExcludes, import chain analysis with /pp preprocessing. DO NOT USE FOR: compilation-time slowness (use build-perf-diagnostics), incremental build issues (use incremental-build), non-MSBuild build systems. INVOKES: dotnet msbuild -pp:full.xml for preprocessing, /clp:PerformanceSummary.