'python' 검색 결과

Python과 Netmiko로 네트워크 장비 정보 수집을 자동화합니다. 연결 수 제한, 출력 분석, 오류 처리와 확인 절차를 갖춘 설정 변경의 구현 및 검토를 돕습니다.

• 여러 장비의 show 명령 출력 수집
• 감사용 인터페이스 및 경로 정보 기록
• SSH 처리의 시간 제한 검토

API, 데이터베이스, 파일 처리를 AI 에이전트가 호출할 수 있는 MCP 서버로 구현합니다. Python 개발부터 로컬 검증, 클라이언트 등록, HTTP 공개까지 돕습니다.

• 기존 API를 AI 에이전트에 연결
• SQLite 읽기 전용 쿼리 제공
• 텍스트 파일 확인과 검색을 도구로 구현

Helps you write Python code for browser automation with browser-use. Its reference guides cover installation, agent and browser settings, custom tools, model connections, and monitoring so you can configure and extend your automation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up browser automation in Python
• Configure agents and browser authentication
• Add custom tools and execution hooks

Verify RuView changes with Rust and Python tests, output hashes that detect processing changes, and firmware hash records. Packages test logs and audit evidence into a witness bundle that recipients can check with a single command.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify a RuView build after significant changes
• Check tests and documentation before merging a PR
• Prepare a verification bundle for a recipient

Build and review numerical workflows for MATLAB or GNU Octave, including arrays, tables, graphics, tests, and MAT files. Helps plan release migrations, reproducible projects, and Python integration while checking runtime, compatibility, and license requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing array operations and numerical tolerances
• Planning MATLAB release migrations and compatibility checks
• Preparing reproducible projects and unit test scaffolds

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs

Build and operate bioinformatics workflows on Latch using Python, Nextflow, or Snakemake. Configure computing resources and input forms, move data, connect Registry records, and register, debug, launch, and monitor analysis runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create and register Python analysis workflows
• Package Nextflow or Snakemake pipelines for Latch
• Connect Latch Data and Registry records

Check whether tests detect faults in changed Python code by deliberately modifying the changed lines. Add tests for missed behavior, confirm which tests fail against the previous code, and report measured evidence alongside any remaining gaps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify test coverage for a Python feature or fix
• Add tests for faults the current suite misses
• Confirm new tests fail against the previous code

Build Python code for Azure Cosmos DB's NoSQL API to create, read, update, delete, and query documents. Covers authentication, containers for storing data, partition keys for distributing records, throughput settings, asynchronous operations, and error handling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implementing document operations in Python
• Writing parameterized queries for filtered searches
• Configuring containers, partition keys, and throughput

Create and organize Python tests with pytest. Use reusable setup, mock replacements for external services, controlled test time, and coverage reports to check normal behavior, failures, retries, and interactions between application components.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add unit and integration tests to Python code
• Replace external services with mocks in tests
• Check retry behavior and time-dependent logic

Build a PowerPoint file with Python when no Office app is open. It uses python-pptx, applies a supplied firm template when available, and saves the deck locally with a path for collection by the surrounding workflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating slides in an environment without an open Office app
• Building a deck from a supplied firm template
• Delivering model-based slides as a local file

Turn descriptions of 3D shapes and CAD tasks into FreeCAD Python scripts. Create models with adjustable dimensions, automate repeated operations with macros, convert meshes and solids, or build custom dialogs and workbench tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create 3D models from written descriptions
• Build models with adjustable dimensions
• Automate repeated CAD operations with macros

Build an end-to-end evaluation pipeline for Python AI applications using pixie-qa and real language-model calls. Define criteria, prepare realistic test data, score application runs, and analyze the evidence to produce a prioritized improvement plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up quality checks for Python AI applications
• Creating realistic scenarios for known failure modes
• Scoring full application runs with real language-model calls

Generate concise Python examples for working with Microsoft Dataverse. Cover SDK installation, browser-based authentication, creating, reading, updating, and deleting records, plus bulk operations and retrieving results in pages using official example patterns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up the SDK and browser-based authentication
• Create, read, update, and delete individual records
• Create or update records in bulk

Generate Python code for Dataverse integrations with error handling, retries, logging, and shared connection management. Include type hints, usage examples, and queries that retrieve only the records and columns needed for the task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build Python integrations with Dataverse
• Add retries and logging to API operations
• Limit retrieved records and columns

Turn a Python script into a beginner-friendly project by improving its structure and naming, adding comments that explain the reasoning, and creating a README.md tutorial covering setup, code logic, and example usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a Python script into learning material
• Explaining code logic to beginners
• Documenting setup steps and example usage

Set up cognee and run a first Python example that stores information and retrieves it with a question. Covers virtual environments, API key configuration, optional packages, and basic troubleshooting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Installing cognee in a new Python environment
• Trying a minimal example that stores and retrieves information
• Separating stored information by project

Convert laboratory instrument files into Allotrope Simple Model (ASM) JSON, a standardized data format, or tabular CSV. Detect instrument types, validate converted data, and export Python parsing code for analysis workflows or handoff to data engineers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Standardizing instrument data for a laboratory management system
• Converting ELISA results into an importable CSV
• Exporting Python code to parse NanoDrop files

Measure Python SDK test coverage, the code exercised by tests, and identify meaningful gaps in user-visible behavior. Report proposed scenarios or add authorized tests, then verify the changes and report remaining gaps alongside coverage results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing Python SDK test coverage
• Investigating a decline in coverage metrics
• Finding untested error and cancellation paths

Check Agent Skills for documentation structure and Python script behavior, then score quality and security posture. Get grades and improvement priorities, and use pass criteria in pre-commit checks or continuous integration, which runs checks automatically.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a newly authored skill before release
• Auditing existing skills and prioritizing improvements
• Testing bundled Python scripts and sample outputs

Build AI applications in Python with Genkit, including conversational agents, generation workflows, streaming responses, and tool use. The skill also guides setup and debugging through traces, recorded details of model inputs, outputs, and actions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a chatbot that keeps conversation history
• Implement streaming responses and tool calls
• Investigate Genkit import and API errors

Guide shell, Python, and Node.js execution on the computer running a connected A0 CLI. It explains session reuse, interactive input, output retrieval, and recovery steps when execution permissions, timeouts, or disconnections interrupt the work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run shell commands on the connected A0 CLI host
• Execute Python or Node.js on the host
• Retrieve output or answer an interactive program

Install Scenic, a language for describing scenarios with randomized conditions, into the Python environment used by CARLA. Check command-line tools and version compatibility, optionally download example scenarios and map assets, and save the installation paths.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing Scenic to work with CARLA
• Diagnosing scenario model import failures
• Checking missing vehicle definition tables

Build Python APIs with FastAPI and Pydantic V2, including input validation, database operations that avoid blocking other work, and token-based authentication. Implement REST or WebSocket endpoints, then check their behavior with automated tests and generated API documentation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build REST APIs with FastAPI
• Validate input data with Pydantic V2
• Implement JWT authentication and authorization