「runtime」の検索結果

This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.

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Embed Mermaid diagrams in generated wiki pages. Use whenever documenting a runtime or request flow, a call sequence, a state machine or lifecycle, a data model or entity relationships, or non-trivial control flow, since these are clearer as a diagram than as prose. Also use when an update run touches a page that already contains a mermaid fence, or a page that contains a text fence a previous run degraded.

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MNN CPU 后端性能归因分支（`skills/cpu/` 下，另一分支是 `cpu/kernel` kernel 开发）。按五层（Runtime 线程 / Executor 调度 / Layout 内存 / Dispatch 函数表 / Kernel ISA）定位瓶颈，含 bound 类型判定、op 级实验回路、跨框架 op 对 op 对比、跨层不一致的事后定位，以及 ARM / x86_64 / RISC-V 三侧「我到底跑在哪条 ISA 路径上」的诊断面。CPU 上算子或 LLM prefill/decode 慢、线程数或内存占用异常、出现性能回归、要与外部推理框架逐算子对比时使用。

日本語の概要は準備中です。原文の説明を表示しています。

Maintain and validate OpenShell's build-only Windows MSVC lane for x64 and ARM64. Use when working on Windows compilation, `windows:*` mise tasks, unsupported Windows compute-driver contracts, or Windows build reports. This skill does not implement Docker, Kubernetes, Podman, VM, MXC driver, policy translation, MSI, service, or supervisor runtime support on Windows.

日本語の概要は準備中です。原文の説明を表示しています。

Create, migrate, improve, or debug detailed vehicle assets for CARLA using Blender and Unreal, including reference-based geometry, materials, wheels, physics, lights, and runtime validation. Use for vehicle asset authoring rather than ordinary traffic spawning or map decoration.

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Gets a custom map into CARLA on UE 5.8 — the four distinct routes and which one to pick — via Util/Tools/Import.py for FBX+OpenDRIVE assets into a source build, CARLA_MAPS_TO_COOK to ship a map inside a package, or client.generate_opendrive_world() for an OpenDRIVE-only map at runtime with no build at all. Covers where imported assets land, why an imported map cannot be loaded from a package by name, and the four capabilities the UE4 skill has that ue58 lacks — standalone asset packages, their installer, semantic-material tagging, and a pedestrian navmesh that is generated but silently skipped. Use when the user asks to "import a map", "add a custom map", "load my .xodr", "package my own town", or "create a standalone map package".

日本語の概要は準備中です。原文の説明を表示しています。

OpenCreator 内部 Creator Agent 的稳定运行规则，仅由应用自动安装和激活。

日本語の概要は準備中です。原文の説明を表示しています。

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

日本語の概要は準備中です。原文の説明を表示しています。

Assess an immutable patch artifact's program impact, regression risk, and auto-merge eligibility. Use for generated patch files, provider pull-request diffs, or commit ranges when reviewers need evidence about affected runtime paths, contracts, tests, and recoverability. This skill is read-only and does not generate, edit, apply, push, or merge the patch.

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Triage supplied or imported security findings against a repository using its security policy and static code evidence. Accepts scanner reports, advisories, GitHub findings, and Jira or Linear tickets. Do not use for discovery, duplicate triage, runtime validation, or fixes.

日本語の概要は準備中です。原文の説明を表示しています。

Apply ClawHub-specific Convex conventions and route to the right managed Convex skill. Use for any change under convex/, Convex commands or deployment targeting, query performance, migrations, retention, runtime validation, or skill stat reads and writes in the ClawHub repository.

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Discovery skill for the Native SDK, the complete toolkit for building native desktop applications. Apps are authored in TypeScript + declarative Native markup (.native) by default and compiled to native code with no JS runtime in the binary; Zig cores are an explicit alternative, and WebViews are the optional web-content path. Use when the user asks what the Native SDK is, how to build a Native SDK app, author native UI, scaffold an app, configure app.json or legacy app.zon, add bridge commands, embed web content, package an app, test a running app, or automate a Native SDK app.

日本語の概要は準備中です。原文の説明を表示しています。

Core Native SDK guide for AI agents. Read this before explaining the Native SDK or changing a Native SDK app. Establishes TypeScript + Native markup as the default app-authoring path, routes default app work to the native-ui and ts-core skills, and covers the shared foundation: project structure, app.json and legacy app.zon, lower-level App and Runtime patterns, frontend integration, web engines, JavaScript bridge commands, permissions, windows, WebViews, dialogs, packaging, debugging, and testing. Use when the user asks what the Native SDK is, how to build or modify an app, how to package or debug it, or how to add native capabilities.

日本語の概要は準備中です。原文の説明を表示しています。

Automation and verification guide for running Native SDK apps. Use when the user asks to test a running app, inspect runtime state, list windows, wait for readiness, drive widgets, take deterministic screenshots, send bridge commands, debug why automation is not connected, create smoke tests, or verify a Native SDK example in a GUI-capable session.

日本語の概要は準備中です。原文の説明を表示しています。

Performs comprehensive Rust security review for safe/unsafe boundary issues, memory safety in unsafe blocks, concurrency hazards, panic-induced DoS, FFI safety, and async runtime mistakes. Use when auditing Rust crates, services, or libraries — particularly those with `unsafe`, FFI, or concurrent code.

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Scans Substrate/Polkadot pallets for 7 critical vulnerabilities including arithmetic overflow, panic DoS, incorrect weights, and bad origin checks. Use when auditing Substrate runtimes or FRAME pallets.

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IoT and embedded device security testing methodology. Covers hardware reconnaissance (UART, JTAG, SWD, SPI flash, I2C EEPROM, eMMC chip-off), firmware acquisition (vendor portals, OTA capture, flash dump, binwalk extraction), firmware analysis (filesystem mounting, binary triage, hardcoded secrets, default credential discovery), bootloader attacks (U-Boot console, secure-boot bypass, fault injection), runtime attacks on embedded Linux/RTOS (busybox CVEs, MTD writes, /dev/mem), wireless protocol attacks (Zigbee, BLE, Z-Wave, LoRaWAN, Thread/Matter, sub-GHz), MQTT/CoAP/Modbus/BACnet/OPC-UA exploitation, mobile companion app analysis, cloud-IoT API abuse, and side-channel/glitching basics. Use for IoT pentest, smart-home assessment, ICS/OT testing, or embedded vulnerability research.

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Mobile (Android + iOS) application penetration testing methodology. Covers static analysis (apktool/jadx for Android, class-dump/Hopper/IDA for iOS), dynamic instrumentation with Frida and Objection, SSL pinning bypass strategies, root/jailbreak detection bypass, deep-link / URL-scheme abuse, exported component attacks (Android activities, services, providers, receivers; iOS XPC, URL schemes, universal links), insecure data storage (SharedPrefs, KeyStore misuse, NSUserDefaults, Keychain ACL bypass), IPC / Intent redirection, WebView vulnerabilities (JavaScriptInterface, file:// access), Firebase/AWS/Azure misconfiguration leakage, mobile API testing, biometric/Face ID/Touch ID bypass, app-cloning and runtime patching, and mobile malware/RAT analysis primitives. Use for mobile pentest, bug bounty mobile triage, or app-store reconnaissance.

日本語の概要は準備中です。原文の説明を表示しています。

Container escape and breakout techniques targeting Docker, containerd, and Podman runtimes. Covers privileged container breakout via host filesystem mount and nsenter, Docker socket abuse through /var/run/docker.sock, Linux capability exploitation including CAP_SYS_ADMIN, CAP_SYS_PTRACE, and CAP_NET_ADMIN, cgroup v1 notify_on_release escape, runc CVEs such as CVE-2019-5736 and CVE-2024-21626 Leaky Vessels, kernel exploits from within containers, and Dockerfile misconfigurations like --privileged and host namespace sharing. Includes enumeration with capsh, amicontained, deepce, CDK, and nsenter. Maps to MITRE ATT&CK T1611 Escape to Host. Use this skill when the engagement scope includes container breakout, Docker escape, container privilege escalation, host access from container, or when you land inside a containerized environment and need to reach the underlying host.

日本語の概要は準備中です。原文の説明を表示しています。

Practical offensive fuzzing methodology covering target identification, fuzzer selection (AFL++, libFuzzer, Honggfuzz, Boofuzz, syzkaller), harness writing, corpus curation, mutation strategies, coverage measurement, and crash triage. Use when setting up or running fuzz campaigns against any target: file parsers, network protocols, kernel drivers, EDR engines, embedded firmware, or language runtimes.

日本語の概要は準備中です。原文の説明を表示しています。

Use when an OpenAI API request fails and Codex needs to classify the likely cause, explain the next step, and route to the right follow-up. Covers common runtime failures such as blocked outbound network access, invalid credentials, exhausted API quota or credits, rate limits, and model, project, or organization access issues; delegate key provisioning to openai-platform-api-key and current documentation lookups to openai-docs.

日本語の概要は準備中です。原文の説明を表示しています。

Top-level workflow skill for USD performance diagnosis and optimization. Use for slow loading, high memory, low FPS, or 'optimize my scene' requests; delegates auth/runtime setup to Phase 0 owners.

日本語の概要は準備中です。原文の説明を表示しています。

Check whether public NGS tools and packages already exist before downloading, installing, or running a sequencing pipeline.

日本語の概要は準備中です。原文の説明を表示しています。