“low-level”的搜索结果

Azure.AI.Agents.Persistent (.NET) workflow skill. Use this skill when the user needs Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Azure AI Agents Persistent SDK for Java workflow skill. Use this skill when the user needs Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Azure AI Agents Persistent SDK for Java workflow skill. Use this skill when the user needs Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Azure.AI.Agents.Persistent (.NET) workflow skill. Use this skill when the user needs Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Assembly programming and low-level debugging skill for reading, writing, explaining, optimizing, and reviewing assembly across x86, x86-64, ARM, AArch64, RISC-V, calling conventions, ABI boundaries, inline assembly, disassembly, stack frames, registers, and binary-level behavior. Use for .s/.asm files, compiler output, reverse-engineering snippets, low-level performance, and crash analysis.

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Plan HyperFrames videos so motion flows continuously between scenes. Match movement direction and speed across cuts, connect actions through visible causes, and verify scene boundaries while keeping each scene active through meaningful reveals or interactions.

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• Plan consistent movement across scene transitions
• Connect scenes with a shared visual element
• Replace idle motion with reveals or interactions

Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.

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• Comparing outcomes between experimental groups
• Examining relationships in survey data
• Planning the number of study participants

Build and simulate quantum circuits in Python with Cirq. Compare noise effects, test parameter values, transform circuits for hardware constraints, and prepare experiments for Google Quantum AI or supported providers when the required access is available.

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• Building and simulating quantum circuits
• Comparing measurement results at different noise levels
• Testing circuits across a range of parameter values

Inspect FCS files from flow cytometry, a method for measuring cell properties, using FlowIO. Extract measurement data into NumPy arrays, review channel metadata, export tables, and create FCS 3.1 files for downstream work.

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• Inspect measurement settings and channel labels in FCS files
• Extract measurement data into NumPy arrays or CSV tables
• Read legacy FCS files containing multiple datasets

Create and manage persistent Azure AI agents from Java. Covers conversation threads that retain messages, agent execution, status checks, response retrieval, and cleanup, using preview SDK examples with guidance on authentication and cloud resource handling.

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• Building a Java app with reusable AI agents
• Managing conversation threads and messages
• Checking execution states and handling timeouts

Helps build AI agents in .NET with persistent conversation threads. Covers messages, execution status, streaming responses, document search, and custom function calls using Azure.AI.Agents.Persistent, with examples for creating agents and cleaning up resources.

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• Build an AI assistant that retains conversation threads
• Answer questions using uploaded documents
• Connect an agent to custom functions

Inspect Android APKs from the command line to understand app logic, permissions, and components. Supports Java decompilation, low-level code edits, rebuilding and signing, plus Frida observation of running methods and routing to native code analysis.

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• Inspecting APK entry points, components, and permissions
• Locating login, signing, and certificate checks
• Editing smali code and rebuilding an APK

Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.

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• Measuring diffusion denoising latency
• Ranking slow operations from profiler traces
• Checking whether existing optimizations address a bottleneck

Develop or port CPU kernels, the low-level calculation routines used by MNN. Follow staged implementation guidance, check data layout agreements, register functions, and verify correctness across processor instruction sets and numerical precision settings.

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• Implementing or porting an MNN CPU kernel
• Adding support for a processor instruction set
• Changing packed data layouts or tile sizes

Assesses smart contract and blockchain codebases against Trail of Bits’ nine-category maturity framework. Produces evidence-backed ratings covering areas such as arithmetic, access controls, documentation, and testing, plus a prioritized improvement roadmap with effort estimates.

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• Assessing the maturity of a smart contract project
• Identifying gaps in tests and documentation
• Reviewing access controls and operational practices together

Build browser-based 3D games with Three.js, TypeScript, and Vite. Structure scene and camera control, model loading, physics, and interface overlays separately from game rules, with guidance for WebGL rendering diagnostics and performance.

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• Build a browser-based 3D exploration demo
• Prototype vehicle movement or character traversal
• Load GLB models and integrate Rapier physics

Base-layer skill for the SenseNova-Skills project, providing low-level APIs for image generation, recognition (VLM), and text optimization (LLM). This skill does not preprocess inputs; it only calls backend services and returns results. This skill is not user-facing and is intended for upper-layer skills only.

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Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.

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Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.

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Low-level support skill for hand-editing the raw XML inside a .docx or .pptx file (both formats are just ZIP archives of XML parts). Use this directly only when you need to unpack, inspect, or repack Open XML yourself — for example fixing a validation error in generated markup, or making one small targeted edit without regenerating a whole document from scratch. The docx and pptx skills already use this internally for their 'edit an existing file' workflow, so you rarely need to load this on its own.

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Compare current benchmark results against historical baselines to spot performance regressions. Use as an explicit/manual helper for build-to-build degradation review, not for broad optimization strategy or low-level profiling ownership.

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Provides binary exploitation techniques for CTF challenges. Use when you already have a vulnerable native target or service and need to turn memory corruption or low-level primitives into code execution or privilege escalation, such as buffer overflows, format strings, heap bugs, ROP, ret2libc, shellcode, kernel exploitation, seccomp bypass, sandbox escape, or Windows/Linux exploit chains. Do not use it when the main blocker is understanding what the binary does; use reverse engineering first. Do not use it for pure web bugs, disk or packet forensics, or standalone crypto/math challenges.

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Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".

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Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.

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