코드베이스 전반에 GDPR 준수 엔지니어링 관행을 적용합니다. API 설계, 데이터 모델 작성, 인증 흐름 구축, 로깅 구현, 사용자 데이터 처리, 보존/삭제 작업 작성, 클라우드 인프라 설계, 개인정보 보호 규정 준수를 위한 풀 리퀘스트 검토 시 이 스킬을 사용하세요.
이 스킬은 GDPR(일반 데이터 보호 규칙) 준수를 코드베이스 전반에 자동화하고 적용하기 위한 AI 에이전트 도구입니다. CNIL 개발자 가이드라인과 GDPR 조항 5, 25, 32, 33, 35에서 영감을 받았으며, 개인 데이터, 사용자 계정, 쿠키, 분석, 이메일, 감사 로그, 암호화, 가명처리, 익명화, 데이터 내보내기, 침해 대응, 실제 데이터를 처리하는 CI/CD 파이프라인 등 GDPR과 관련된 모든 작업에서 트리거됩니다. 주로 Claude Code, Cursor, Codex 등의 AI 어시스턴트와 함께 사용되며, 개발자에게 GDPR 준수 여부를 질문하거나 관련 코드를 작성/검토할 때 자동으로 활성화되어 적절한 지침과 모범 사례를 제공합니다. API 설계부터 데이터 모델, 인증 흐름, 로깅, 사용자 데이터 처리, 보존/삭제 작업, 클라우드 인프라 설계 및 풀 리퀘스트 검토에 이르기까지 모든 단계에서 개인정보 보호를 보장하도록 돕습니다. '이것이 GDPR을 준수하나요?'라는 질문에 답변하거나 관련 코드를 생성할 때 특히 유용합니다.
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/gdpr-compliant코드베이스 전반에 GDPR 준수 엔지니어링 관행을 적용합니다. API를 설계하거나, 데이터 모델을 작성하거나, 인증 흐름을 구축하거나, 로깅을 구현하거나, 사용자 데이터를 처리하거나, 보존/삭제 작업을 작성하거나, 클라우드 인프라를 설계하거나, 개인정보 보호 규정 준수를 위해 풀 리퀘스트를 검토할 때마다 이 스킬을 사용하세요. 개인 데이터, 사용자 계정, 쿠키, 분석, 이메일, 감사 로그, 암호화, 가명처리, 익명화, 데이터 내보내기, 침해 대응, 실제 데이터를 처리하는 CI/CD 파이프라인과 관련된 작업이나 '이것이 GDPR을 준수하나요?'라는 질문이 포함된 모든 작업에 대해 이 스킬을 트리거하세요. CNIL 개발자 가이드라인과 GDPR 조항 5, 25, 32, 33, 35에서 영감을 받았습니다.
This repo contains 20 individual skills — each has its own dedicated page.
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.
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
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)
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
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"
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.
Instrument a webapp to send useful telemetry data to Azure App Insights
Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.
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.
Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
ElevenLabs text-to-speech with mac-style say UX.
Oracle CLI second-model review/debug/refactor/design with selected files, dry-run token checks, API or browser engine.
Capture and automate macOS UI with the Peekaboo CLI.
Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
Build a throwaway prototype to flesh out a design — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route.
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".