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OracleからPostgreSQLへのマスタ移行計画作成

.NETソリューション内の全プロジェクトを検出し、それぞれのOracleからPostgreSQLへの移行適性を分類し、永続的なマスタ移行計画を生成します。複数プロジェクトのOracleからPostgreSQLへの移行を開始する際、移行インベントリを作成する際、またはOracle依存関係を含む.NETプロジェクトを評価する際に使用します。

OracleからPostgreSQLへのマスタ移行計画作成 とは?

このスキルは、.NETソリューション内の全プロジェクトをスキャンし、OracleからPostgreSQLへの移行適性を自動的に評価して分類します。これにより、大規模なデータベース移行プロジェクトの初期段階で、どのプロジェクトに移行が必要か、どの程度の作業量が必要かを迅速に把握できます。スキルはソリューション内の各プロジェクトを分析し、Oracle固有の依存関係(例:Oracle.ManagedDataAccess)、SQL文、接続文字列などを検出して、移行難易度を「簡単」「中程度」「困難」などに分類します。結果は永続的なマスタ移行計画として保存され、チーム全体で共有・更新可能な基準となります。対応プラットフォームは主に.NET開発環境(Windows、Linux、macOS)で動作し、Visual Studioや.NET CLIと連携して使用します。

対応~Claude Code~Codex CLI~Cursor
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ドキュメント

OracleからPostgreSQLへのマスタ移行計画作成 は何をしますか?

.NETソリューション内のすべてのプロジェクトを検出し、各プロジェクトのOracleからPostgreSQLへの移行適性を分類し、永続的なマスタ移行計画を生成します。複数プロジェクトのOracleからPostgreSQLへの移行を開始する際、移行インベントリを作成する際、またはOracle依存関係を含む.NETプロジェクトを評価する際に使用します。

概要

このスキルは、OracleデータベースからPostgreSQLへの移行プロジェクトを管理するための基盤を提供します。ソリューション全体をスキャンし、移行が必要なプロジェクトを特定し、各プロジェクトの複雑さを評価します。

使用方法

  1. スキルを実行する前に、対象の.NETソリューションが存在することを確認してください。
  2. スキルを起動すると、自動的にソリューション内のプロジェクトがスキャンされます。
  3. 各プロジェクトのOracle依存関係が分析され、移行計画が生成されます。

出力

スキルは以下の情報を含むマスタ移行計画を生成します:

  • プロジェクト名とパス
  • Oracle依存関係の有無
  • 移行難易度(簡単/中程度/困難)
  • 推奨される移行手順
  • 注意点や既知の問題

注意事項

  • このスキルは.NET Frameworkおよび.NET Core/5+ プロジェクトをサポートしています。
  • スキルは読み取り専用操作を実行し、既存のコードや構成を変更しません。
  • 生成された計画はJSON形式で保存され、後続の移行ステップで利用できます。

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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