python-patterns
Pythonic イディオム、PEP 8標準、型ヒント、堅牢で効率的かつ保守可能なPythonアプリケーションを構築するためのベストプラクティス。
Skill content language: Japanese
Browse agent skills for framework development, compiler optimization, and low-level components
Categories, GitHub stars, and source recency help organize and order the catalog. These signals are not an endorsement, quality certification, safety review, or guarantee of fit. Open a skill detail and inspect its source before use.
26,423 results
Pythonic イディオム、PEP 8標準、型ヒント、堅牢で効率的かつ保守可能なPythonアプリケーションを構築するためのベストプラクティス。
Skill content language: Japanese
PyTorch深度学习模式与最佳实践,用于构建稳健、高效且可复现的训练流程、模型架构和数据加载。
Skill content language: Chinese
选择在解析结构化文本时使用正则表达式还是大型语言模型的决策框架——从正则表达式开始,仅在低置信度的边缘情况下添加大型语言模型。
Skill content language: Chinese
Convert 'make it faster' requests into a bounded measured optimization loop — baseline first, generate one-hypothesis variants, benchmark each against a correctness gate, and promote the fastest safe variant with reproducible commands. Use when asked to speed something up, try many variants, run recursive optimization, benchmark latency/throughput/cost, or pick the best implementation by repeated measured tests.
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading. Use when writing or reviewing PyTorch training loops, model architectures, or data loading, or when a run will not reproduce.
Expert guidance for developing with the tinystruct Java framework. Use when working on the tinystruct codebase or any project built on tinystruct — including generating the bin/dispatcher and bin/dispatcher.cmd launcher scripts when a project lacks them, creating Application classes, @Action-mapped routes, unit tests, ActionRegistry, HTTP/CLI dual-mode handling, the built-in HTTP server, the event system, JSON with Builder/Builders, database persistence with AbstractData, POJO generation, Server-Sent Events (SSE), file uploads, and outbound HTTP networking.
Run PyTorch training across GPUs with minimal changes.
Fine-tune large LLMs with LoRA on limited GPU memory.
Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.
Evolve prompts/regex/SQL/code with Imbue's evolution loop.
Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.
How to add or modify Next.js experimental feature flags end-to-end. Use when editing config-shared.ts, config-schema.ts, define-env-plugin.ts, next-server.ts, export/worker.ts, or module.compiled.js. Covers type declaration, zod schema, build-time injection, runtime env plumbing, and the decision between runtime env-var branching vs separate bundle variants.
Guide for migrating PowerToys modules from WPF to WinUI 3 (Windows App SDK). Use when asked to migrate WPF code, convert WPF XAML to WinUI, replace System.Windows namespaces with Microsoft.UI.Xaml, update Dispatcher to DispatcherQueue, replace DynamicResource with ThemeResource, migrate imaging APIs from System.Windows.Media.Imaging to Windows.Graphics.Imaging, convert WPF Window to WinUI Window, migrate .resx to .resw resources, migrate custom Observable/RelayCommand to CommunityToolkit.Mvvm source generators, handle WPF-UI (Lepo) to WinUI native control migration, or fix installer/build pipeline issues after migration. Keywords: WPF, WinUI, WinUI3, migration, porting, convert, namespace, XAML, Dispatcher, DispatcherQueue, imaging, BitmapImage, Window, ContentDialog, ThemeResource, DynamicResource, ResourceLoader, resw, resx, CommunityToolkit, ObservableProperty, WPF-UI, SizeToContent, AppWindow, SoftwareBitmap.
Guide for breaking cyclic dependencies in V8 headers and resolving cascade IWYU failures.
Provides expert guidance for navigating, implementing, and verifying V8 Torque (.tq) builtins. Use when modifying or debugging Torque files. Do not use for C++ macro builtins.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
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.
Choose 32-bit vs 64-bit index math in PyTorch CUDA kernels. Use when fixing large-tensor indexing overflows, deciding whether to use int64_t, canUse32BitIndexMath, CUDA_KERNEL_LOOP_TYPE, or AT_DISPATCH_INDEX_TYPES, and when considering binary-size or performance impact of index-type templating.
Deprecate, freeze, or remove PyTorch APIs; choose a removal version, handle won't-fix frozen components, or audit overdue removals.
Write Metal/MPS kernels for PyTorch operators. Use when adding MPS device support to operators, implementing Metal shaders, or porting CUDA kernels to Apple Silicon. Covers native_functions.yaml dispatch, host-side operators, and Metal kernel implementation.
Debug PyTorch 2 compiler stack failures including Dynamo graph breaks, Inductor codegen errors, AOTAutograd crashes, and accuracy mismatches. Use when encountering torch.compile errors, BackendCompilerFailed exceptions, recompilation issues, Triton kernel failures, FX graph problems, or when the user mentions debugging PT2, Dynamo, Inductor, or compiled model issues.
Turn a feature request into a minimal, file-level implementation plan before any code.
Expert guidance for Vite development with modern build tooling, HMR, framework integrations, and performance optimization
Playbook for changing vLLM's Transformers version bounds. Use when bumping the pinned or maximum Transformers version, raising the minimum Transformers version, or deleting vendored configs, processors, or compat shims that upstream Transformers now provides.
Write or review Triton kernels for vLLM, with practical guidance for generated-code inspection, launch grids, indexing, specialization, tuning, and representative performance validation. Use when adding, modifying, optimizing, or reviewing a `@triton.jit` kernel in vLLM.
Discover the smallest capable tool set and confirm argument schemas before acting
Use for external-agent launchers and execution identity, Claude Code/Codex adapters, IPC, event mapping, sessions, persistence and tool-call chains.
Implement, migrate or review Go go.d framework V2 collectors, CollectorV2 lifecycle, metrix metric stores, charts.yaml authoring (defaults, families, ordering, statesets, labels), charttpl/chartengine, Functions and host scopes/vnodes. Use affected contracts and source owners; collector product/config design uses collectors-go-design.
Configure RuVLLM local inference helpers: generate a config, and create and adapt in-process MicroLoRA and SONA instances from quality signals. Use when you need local, air-gapped inference tooling, not for fine-tuning hosted models.
Install and wire registry blocks and components into HyperFrames compositions. Use when running hyperframes add, installing a block or component, wiring an installed item into index.html, or working with hyperframes.json. Covers the add command, install locations, block sub-composition wiring, component snippet merging, registry discovery, and authoring a new block or component to contribute upstream (idea → scaffold → validate → PR).
Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks. Also use when the user says "openspec apply", "opsx apply", or "openspec implement".
Repo-specific dedupe guidance for warp. Only the categories declared overridable by the core dedupe-issue skill may be specialized here.
Authors, reviews, migrates, simulates, and troubleshoots official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow must support multiple robot vendors.
Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for open-system dynamics use qutip.
Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
Drive the per-module internal-API codebase test, then refine the suggested annotations down to `internal` (or up to `@KaImplementationDetail`) based on actual external usage
Extract the inner ELF from a ClickHouse self-extracting `clickhouse` binary, including when its architecture differs from the host (e.g. to load an aarch64 CI core dump on an x86 workstation). Use when gdb/lldb needs real symbols from a downloaded CI/release binary, or when self-extraction by running the binary is not possible because of an architecture mismatch.
Deciding where frontend code lives — tier model, move mechanics, extension points, side effects, naming, and the traps. Use when moving code between modules, carving new modules, fixing boundary violations, adding a barrel or an endpoint, or reviewing module-shape decisions.
Use when modifying, debugging, or extending the upcoming changes framework code and system itself.
Tạo hoặc cập nhật các file CLAUDE.md theo các thực hành tốt nhất để onboarding tác nhân AI tối ưu
Skill content language: Vietnamese
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for kernel attack surface, namespace and cgroup boundaries, container isolation assumptions, syscall paths, and escape primitive verification. Use when the user asks to analyze container-to-host escape paths, kernel exploit prerequisites, namespace crossover, capability misuse, or prove whether an exploit primitive crosses the sandbox boundary. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for malware configuration recovery, staged payload boundaries, beacon parameter extraction, and IOC decoding. Use when the user asks to recover a malware config, decode C2 or beacon fields, unpack staged payloads, extract bot or campaign IDs, or tie recovered config to observed protocol behavior under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for reverse engineering, malware, DFIR, firmware, pwnable, and native exploit challenges. Use when the user asks to reverse a binary, unpack a sample, inspect a memory dump or PCAP, recover malware behavior, debug a crash, or build or verify an exploit chain under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
.NET / C# 二进制逆向。当目标是 .NET assembly(PE 头含 CLR、.exe/.dll 托管程序)、C# 编译产物(含 NativeAOT)、红队 Sharp* 工具(Rubeus / SharpHound / SharpHound 等)、.NET 混淆程序(ConfuserEx / SmartAssembly / Babel / Eazfuscator)、.NET loader / info-stealer / 套壳 malware 时使用。优先用 dnSpyEx + de4dot,需要 AI 直接操作时联动 dnSpy MCP。不用于纯 native 二进制(走 reverse-engineering / ida-reverse)。
Skill content language: Chinese
Use for free/open reverse engineering with Ghidra (headless or GUI), including decompile, cross-refs, and optional Ghidra MCP workflows when IDA is unavailable.
Skill content language: Chinese
Use for authorized reverse engineering of custom binary protocols, Protobuf/gRPC, WebSocket frames, and PCAP-driven protocol recovery.
Skill content language: Chinese
Plan and execute moving non-host-specific code (and its tests) from the OpenHuman core (crates/openhuman-core) into the vendored tiny* submodule libraries (vendor/tiny*), then release the submodule and re-pin the registry digests. Use when asked to "move X out of core", "upstream this into tinyfoo", "extract a crate", or to shrink the host to composition/policy only.