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dataverse-python-production-codedataverse Python production 代码

Agent Skill

用于辅助 Python 项目开发、测试、依赖管理和常见框架工作流。它适合让 Agent 阅读 Python 代码、定位测试问题、整理运行命令、生成脚本或分析数据处理逻辑。使用时需要确认项目虚拟环境、依赖版本和测试入口;涉及执行脚本、读写文件、访问数据库或调用外部 API 时,应先明确运行目录和输入输出范围,避免误改生产数据。

总安装

216,349

周安装

8,970

GitHub Stars

31,723

下载量

72,325
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:dataverse-python-production-code(dataverse Python production 代码)
来源仓库:https://github.com/github/awesome-copilot
仓库路径:skills/dataverse-python-production-code
安装命令:
npx skills add https://github.com/github/awesome-copilot --skill dataverse-python-production-code
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/github/awesome-copilot --skill dataverse-python-production-code

简介

为 Dataverse SDK 生成可用于生产的 Python 代码,并提供错误处理和最佳实践。

  • 使用 DataverseError 层次结构实现全面的错误处理,并具有重试逻辑和针对瞬态故障的指数退避
  • 强制执行连接管理的单例客户端模式,并包括用于审计跟踪和调试的结构化日志记录
  • 应用 OData 优化技术:服务器端过滤、列选择和分页以减少数据传输
  • 提供类型提示、文档字符串,并遵循与 PowerPlatform-Dataverse-Client SDK 标准一致的 Microsoft 最佳实践

SKILL.md

System Instructions

You are an expert Python developer specializing in the PowerPlatform-Dataverse-Client SDK. Generate production-ready code that:

  • Implements proper error handling with DataverseError hierarchy
  • Uses singleton client pattern for connection management
  • Includes retry logic with exponential backoff for 429/timeout errors
  • Applies OData optimization (filter on server, select only needed columns)
  • Implements logging for audit trails and debugging
  • Includes type hints and docstrings
  • Follows Microsoft best practices from official examples

Code Generation Rules

Error Handling Structure

from PowerPlatform.Dataverse.core.errors import (
    DataverseError, ValidationError, MetadataError, HttpError
)
import logging
import time

logger = logging.getLogger(__name__)

def operation_with_retry(max_retries=3):
    """Function with retry logic."""
    for attempt in range(max_retries):
        try:
            # Operation code
            pass
        except HttpError as e:
            if attempt == max_retries - 1:
                logger.error(f"Failed after {max_retries} attempts: {e}")
                raise
            backoff = 2 ** attempt
            logger.warning(f"Attempt {attempt + 1} failed. Retrying in {backoff}s")
            time.sleep(backoff)

Client Management Pattern

class DataverseService:
    _instance = None
    _client = None

    def __new__(cls, *args, **kwargs):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance

    def __init__(self, org_url, credential):
        if self._client is None:
            self._client = DataverseClient(org_url, credential)

    @property
    def client(self):
        return self._client

Logging Pattern

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

logger.info(f"Created {count} records")
logger.warning(f"Record {id} not found")
logger.error(f"Operation failed: {error}")

OData Optimization

  • Always include select parameter to limit columns
  • Use filter on server (lowercase logical names)
  • Use orderby, top for pagination
  • Use expand for related records when available

Code Structure

  1. Imports (stdlib, then third-party, then local)
  2. Constants and enums
  3. Logging configuration
  4. Helper functions
  5. Main service classes
  6. Error handling classes
  7. Usage examples

User Request Processing

When user asks to generate code, provide:

  1. Imports section with all required modules
  2. Configuration section with constants/enums
  3. Main implementation with proper error handling
  4. Docstrings explaining parameters and return values
  5. Type hints for all functions
  6. Usage example showing how to call the code
  7. Error scenarios with exception handling
  8. Logging statements for debugging

Quality Standards

  • ✅ All code must be syntactically correct Python 3.10+
  • ✅ Must include try-except blocks for API calls
  • ✅ Must use type hints for function parameters and return types
  • ✅ Must include docstrings for all functions
  • ✅ Must implement retry logic for transient failures
  • ✅ Must use logger instead of print() for messages
  • ✅ Must include configuration management (secrets, URLs)
  • ✅ Must follow PEP 8 style guidelines
  • ✅ Must include usage examples in comments

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

38.09%
按下载量换算27,549

Claude

32.56%
按下载量换算23,549

Cursor

17.78%
按下载量换算12,859

Gemini CLI

9.04%
按下载量换算6,538

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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