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github/dataverse-python-advanced-patterns

Dataverse SDKを使用した本番コードを、高度なパターン、エラーハンドリング、最適化技法を用いて生成します。

dataverse-python-advanced-patterns とは？

このスキルは、Dataverse SDK（旧Common Data Service）を用いたPythonアプリケーション開発において、実践的かつ高度なパターンとテクニックを適用した本番品質のコードを生成するためのものです。単なるCRUD操作を超え、バルク処理、トランザクション管理、楽観的同時実行制御、サーバーサイドフィルタリングやページネーションなどの高度なクエリ最適化、堅牢なエラーハンドリング（再試行ロジック、HTTPステータスコードに基づく条件分岐など）を実装します。また、コードのパフォーマンス向上や可読性を高めるための静的型付けや非同期処理の利用も促進します。主に、Dataverse SDK for Python（Microsoft.PowerPlatform.Dataverse.Client）を使用する開発者を対象としており、Visual Studio Code、Cursor、またはターミナルベースの開発環境で利用できます。このスキルは、SKILL.mdに基づいて、実際のプロジェクトで即座に活用できる保守性が高く効率的なコードスニペットやテンプレートを提供します。

対応~Claude Code~Codex CLI~Cursor
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このエージェントスキルを事前に読み込んだ状態で新しいチャットを開きます。

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ドキュメント

dataverse-python-advanced-patterns は何をしますか？

Dataverse SDKを使用した本番コードを、高度なパターン、エラーハンドリング、最適化技法を用いて生成します。

概要

このスキルは、PythonアプリケーションでMicrosoft Dataverse（旧Common Data Service）と連携するための、高度で実践的なコードパターンを提供します。単純なデータアクセスを超えて、エンタープライズレベルの堅牢性、パフォーマンス、保守性を実現するためのベストプラクティスに焦点を当てています。

主な機能

  • バルク操作: 大量のデータを効率的に処理するためのCreate/Update/Deleteの最適化
  • トランザクション管理: 複数の操作をアトミックに実行するトランザクションパターン
  • 楽観的同時実行制御: データの競合を検出し、競合解決戦略を実装
  • 高度なクエリ: フィルタリング、並べ替え、ページネーション、およびリンクエンティティクエリの最適化
  • エラーハンドリング: 再試行ロジック、特定のHTTPステータスコード（429、503など）への対応、包括的な例外処理
  • 非同期処理: asyncioを使用した非同期操作の実装
  • 型安全性: Pythonの型ヒントを活用した、型安全なコードの生成

対象プラットフォーム

  • Microsoft Power Platform Dataverse
  • Python 3.8以上
  • パッケージ: Microsoft.PowerPlatform.Dataverse.Client（pacpythonとしても知られる）

使用方法

以下のコマンドでスキルを呼び出します:

dataverse-python-advanced-patterns create <pattern-name> [options]

利用可能なパターン:

パターン名説明
bulk-operationsバルク作成/更新/削除のテンプレート
optimistic-concurrency楽観的同時実行制御の実装
retry-policy指数バックオフと再試行ロジック
transaction-scopeトランザクションラッパー
async-client非同期Dataverseクライアント

例:

dataverse-python-advanced-patterns create bulk-operations --connection-string "AuthType=ClientSecret;Url=https://yourorg.crm.dynamics.com;ClientId=xxx;ClientSecret=yyy"

注意事項

  • 接続文字列は安全に管理し、コードにハードコードしないでください（環境変数またはAzure Key Vaultの使用を推奨）。
  • 生成されたコードはベストプラクティスに従いますが、特定のユースケースに合わせて調整が必要な場合があります。
  • このパッケージは、Dataverse ServiceClientの上に構築されています。詳細については公式ドキュメントを参照してください。

ライセンス

このプロジェクトはMITライセンスの下で公開されています。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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