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Use when using the LimboAI addon — behavior trees and hierarchical state machines (C++ GDExtension) with a visual editor, BTTask subclassing, and a blackboard

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This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged in

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syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-bas

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Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration (AVX512 with branching trial loop), runtime CPU dispatch, OpenMP threading, and brgemm integration for GEMM-heavy kernels.

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用于配置 Visual Studio 属性表（.props），通过设置绝对路径解决项目引用后 DLL 运行时找不到的问题，且不修改全局环境变量，实现引入即用。

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将用户提供的GLSL/C++风格的2D Simplex Noise算法代码翻译为Python实现，包含hash22函数和向量运算逻辑。

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使用 GLM 库实现 Vector3TransformNormal 和 Vector3TransformCoord 函数，模拟 DirectXMath 的行为，并使用 const T& 模板参数以支持多种向量类型。

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使用OpenCV对图像进行骨架化处理，并去除游离孤点及基于距离阈值过滤连接时的异常点。

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Grade a curated list of individual tests for readiness, A-F quality, and concrete improvements. ALWAYS USE FOR: grade tests, review only a named test, per-test readiness decisions, or quality bands for supplied methods, bodies, file spans, or bounded PR diffs, including existing tests. Produce a PR-ready Pass, Failed, Uncertain, or Not applicable table; unresolved or empty scopes omit the grade. Compose read-only per-test mutation evidence when available. Polyglot: .NET, Python, TS/JS, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, C++. DO NOT USE FOR: suite-wide audits (test-engineer or test-anti-patterns), writing or fixing tests, or measuring coverage.

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Audits existing tests in any language using formal, research-backed test smell names and the testsmells.org 19-smell academic taxonomy. Use when the caller asks for an academic or citable test-smell review, named smell categories, or a formal severity-ranked smell assessment. Covers Assertion Roulette, Conditional Test Logic, Mystery Guest, Eager Test, Sleepy Test, Unknown Test, Sensitive Equality, and the rest of the catalog across .NET, Python, JavaScript/TypeScript, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, and C++. DO NOT USE FOR a quick pragmatic test review (use test-anti-patterns), writing or running tests, framework migration, coverage, or assertion-diversity metrics.

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MANDATORY for static source-to-test pairing: find or list source files/modules without corresponding tests, or suggest test locations from repository structure. Invoke even for a tiny package; do not substitute manual globbing. Uses Roslyn for C#/.NET and tree-sitter for Python, TS/JS, Go, Java, Rust, Ruby, Kotlin, Swift, PowerShell, and C++. DO NOT USE FOR: real line/branch/Cobertura data, coverage-backed test priorities, CRAP risk, or grading existing tests.

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Provides file paths to language-specific extension files for the code-testing pipeline. Read this reference-only catalog for extension guidance files (e.g., dotnet.md for .NET, cpp.md for C++). Agents and skills read its bundled files directly; do not invoke this catalog through the model's skill tool.

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Invoke when the user asks to review, check, audit, or look over Qt6 C++ code — or suggest before committing. Runs deterministic linting (60+ rules) then six parallel deep- analysis agents covering model contracts, ownership, threading, API correctness, error handling, and performance. Reports only high-confidence issues (>80/100) with structured mitigations. Read-only — never modifies code.

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Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.

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Use when app, service, or user-mode driver C++ coroutine code holds a thread-affine lock across suspension and later hangs or fails. Not for all coroutine crashes or choosing lock performance.

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Use when a native C/C++ app, service, or user-mode driver host (including UMDF) crashes with a structured exception in a dump or WinDbg session, including native faults inside managed processes. Not for managed .NET exceptions, WinUI/XAML app errors, or kernel bugchecks.

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Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.

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Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++. Use scikit-image for scientific morphometry/regionprops; OpenCV for real-time, video, classical feature extraction.

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Analyze C++ build performance using vcperf.exe and ETL traces. Use this skill when the user wants to profile a build, understand compile bottlenecks, identify expensive headers or templates, generate JSON analysis reports, or interpret vcperf output. Covers tracing workflows, the /jsonAnalysis flag, reading the resulting JSON schema, and actionable remediation strategies for each bottleneck category.

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Push records from apps, services, devices, or scripts directly into Unity Catalog Delta tables with Zerobus Ingest: SDKs (Python, TypeScript, Go, Java, Rust, C++, .NET), REST, OTLP, MQTT, Arrow Flight, or Apache Kafka-compatible producer APIs, with no message bus to run. Use to stream events, telemetry, logs, IoT data, CDC, or columnar batches into Databricks in near real time; connect existing collectors and log shippers (OpenTelemetry, Debezium, Vector, Telegraf, Cribl); replace a message bus that only feeds the lakehouse; or debug Zerobus auth, schema, throughput, or delivery issues.

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Use Brian2 for clock-driven spiking-neural-network modeling, simulation, synaptic connectivity, input generation, recording, physical-unit equations, spatial neurons, runtime code generation, and C++ standalone workflows.

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Use BoxMOT for multi-object tracking, benchmark replay, ReID model lifecycle workflows, and native C++ tracker backends.

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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

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Automatically generate TLA+ specifications from source code (C/C++, Python) for formal verification of distributed systems. Use when users need to: (1) Generate TLA+ specs from program implementations, (2) Model distributed systems, consensus protocols, or concurrent algorithms, (3) Extract state variables, actions, and invariants from code, (4) Create formal specifications for model checking with TLC, (5) Verify safety and liveness properties of distributed systems. Particularly effective for message-passing systems, replication protocols, consensus algorithms, and distributed transactions.

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