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github/shuffle-json-data

JSONデータ内の繰り返しオブジェクトを安全にシャッフルし、シャッフル前にスキーマの一貫性を検証します。

shuffle-json-data とは?

このスキルは、JSONデータ内の繰り返しオブジェクトをランダムな順序に並べ替える(シャッフルする)機能を提供します。特に、データの順序に依存しないテストや分析を行う際に有用です。シャッフルを実行する前に、すべてのオブジェクトが同じスキーマ(同じキーとデータ型)を持っているかを検証することで、データ構造が損なわれるのを防ぎます。このスキルは、JSONファイルやAPIレスポンスなど、プログラムからアクセス可能な任意のJSONデータセットに適用できます。開発者は、データのランダム化が必要なテストケースやデモデータの作成、またはバイアスを排除したサンプリングを行うために、このスキルをエージェント(例:Claude Code, Cursor)に指示して利用できます。スキーマ検証により、意図しないデータ破壊を防ぐため、安全に使用できます。

対応~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/shuffle-json-data

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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。

ドキュメント

shuffle-json-data は何をしますか?

JSONデータ内の繰り返しオブジェクトを安全にシャッフルし、シャッフル前にスキーマの一貫性を検証します。

このスキルは、以下のことを行います。

  • JSONデータ内のオブジェクト配列を特定します。
  • すべてのオブジェクトが同一のスキーマ(キー、値の型)を持っているか検証します。
  • 検証が成功した場合、オブジェクトの順序をランダムに並べ替えます。
  • シャッフルされた結果のJSONデータを出力します。

使用例

# ファイルをシャッフルする
shuffle-json-data input.json > output.json

# 標準入力からデータを受け取り、シャッフルする
cat data.json | shuffle-json-data -

スキーマ検証

shuffle-json-data は、シャッフルを実行する前にすべてのオブジェクトが以下の点で同一であることを確認します。

  1. キーのセット: すべてのオブジェクトが同じキーを持っていること。
  2. 値の型: 対応するキーの値がすべて同じデータ型(文字列、数値、真偽値、配列、オブジェクト、null)であること。

検証に失敗した場合、エラーメッセージが表示され、シャッフルは実行されません。

環境変数

  • SHUFFLE_SEED: シャッフルのための乱数シードを設定します。同一のシード値を使用することで、再現可能なシャッフル結果を得られます。

注意事項

  • このスキルは、JSONオブジェクトの配列を想定しています。トップレベルがオブジェクトやプリミティブ値の場合、シャッフルは実行されません。
  • シャッフルは配列内のオブジェクトのみを対象とします。配列内にオブジェクト以外の値が含まれている場合、それらはそのまま保持されます。
  • スキーマの検証は、最初のオブジェクトのスキーマを基準として行われます。

プラットフォーム

このスキルは、AIエージェント(例:Claude Code, Cursor, Codex)から利用できるように設計されています。

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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