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github/OracleからPostgreSQLへの移行バグレポート作成

OracleからPostgreSQLへの移行中に発見された不具合の構造化バグレポートを作成します。OracleとPostgreSQL間の動作の違いを、重要度、根本原因、是正手順を含む実用的なバグレポートとして文書化する際に使用します。

OracleからPostgreSQLへの移行バグレポート作成 とは？

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において、開発者やテスターが発見した互換性の問題や動作の差異を、標準化されたフォーマットのバグレポートとして体系的に文書化するためのツールです。主なユースケースは、SQLの構文、データ型、組み込み関数、ストアドプロシージャ、トリガーなどの動作の違いを特定し、それをエンジニアがすぐにアクションを起こせる形で報告することです。このスキルは、Claude CodeやCursorなどのAIエージェントプラットフォーム上で動作し、ユーザーが入力した問題の説明を解析し、重要度（Critical, High, Medium, Low）、根本原因（例：互換性のない関数、データ型のマッピングミス）、推奨される修正手順（ALTER文、コードの書き換え、回避策など）を含む詳細なバグレポートを自動生成します。これにより、移行作業の追跡性とコミュニケーションの効率が大幅に向上し、同じ問題の再発防止にも役立ちます。

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

OracleからPostgreSQLへの移行バグレポート作成 は何をしますか？

OracleデータベースからPostgreSQLへの移行中に発見された不具合について、構造化されたバグレポートを作成します。OracleとPostgreSQLの動作の違いを、重要度、根本原因、是正手順を含む実用的なバグレポートとして文書化する際に使用します。

概要

このスキルは、OracleからPostgreSQLへの移行プロジェクトにおいて、互換性の問題や動作の差異を体系的に記録するためのものです。発見されたすべての不具合は、以下のセクションを含む標準化されたフォーマットで報告されます。

  • タイトル: 問題を簡潔に説明
  • 重要度: Critical, High, Medium, Lowのいずれか
  • 環境: 対象のOracleバージョンとPostgreSQLバージョン
  • 再現手順: 問題を再現するための具体的なステップ
  • 実際の結果: PostgreSQLで発生するエラーや予期しない動作
  • 期待される結果: Oracleでの正常な動作
  • 根本原因: 互換性のない関数、データ型のマッピングミス、SQL構文の違いなど
  • 是正手順: 推奨される修正（ALTER文、コードの書き換え、回避策など）

使用方法

AIエージェントに以下の情報を提供してください。

  1. 問題の簡潔な説明
  2. 関連するSQLコードまたはスキーマ定義
  3. Oracleでの期待される動作
  4. PostgreSQLで実際に発生した動作またはエラーメッセージ
  5. 影響を受けるテーブル、ビュー、またはストアドプロシージャ

例

入力: OracleのTO_CHAR関数がPostgreSQLでエラーになる

出力:

# バグレポート: TO_CHAR関数の互換性問題

- **重要度**: High
- **環境**: Oracle 19c, PostgreSQL 15
- **再現手順**: `SELECT TO_CHAR(SYSDATE, 'YYYY-MM-DD HH24:MI:SS') FROM dual;`
- **実際の結果**: PostgreSQLで関数`to_char`は存在するが、日付フォーマットモデルが異なるため予期しない出力
- **期待される結果**: フォーマットされた日付文字列
- **根本原因**: Oracleのフォーマットモデル（'YYYY-MM-DD HH24:MI:SS'）はPostgreSQLでもほぼ同じだが、`SYSDATE`が存在しない
- **是正手順**:
    1. `SYSDATE`を`CURRENT_TIMESTAMP`に置き換え
    2. `SELECT TO_CHAR(CURRENT_TIMESTAMP, 'YYYY-MM-DD HH24:MI:SS');`

注意事項

  • 機密情報（実際のパスワードや接続文字列など）はレポートに含めないでください
  • 可能な限り、問題を最小限の再現ケースに絞り込んでください
  • このスキルは移行時のバグ報告を効率化するためのものであり、根本的な問題解決を自動的に行うものではありません

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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