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github/power-bi-dax-optimization

Power BI DAX 수식 최적화를 위한 포괄적인 프롬프트로, DAX 계산의 성능, 가독성 및 유지보수성을 향상시킵니다.

power-bi-dax-optimization란 무엇인가요?

이 스킬은 Power BI DAX 수식을 최적화하기 위한 전문 프롬프트입니다. DAX 계산의 성능(Performance), 가독성(Readability), 유지보수성(Maintainability)을 개선하는 데 초점을 맞추며, 특히 복잡한 비즈니스 로직이나 대용량 데이터 모델에서 자주 발생하는 느린 측정값, 비효율적인 패턴, 중복 계산 등을 식별하고 수정하는 방법을 안내합니다. GitHub 저장소 기반으로 제공되며, Claude Code, Cursor, Codex 등 AI 코딩 어시스턴트와 함께 사용할 수 있습니다. 이 스킬은 SKILL.md에 정의된 최적화 지침을 따르며, 사용자가 제시한 DAX 코드를 분석하고 구체적인 개선 제안과 대체 코드를 제공합니다. 주요 기능으로는 필터 컨텍스트 최적화, CALCULATE 사용법 개선, 변수 선언을 통한 성능 향상, 불필요한 반복 계산 제거 등이 포함됩니다.

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문서

power-bi-dax-optimization은(는) 무엇을 하나요?

Power BI DAX 수식 최적화를 위한 포괄적인 프롬프트로, DAX 계산의 성능, 가독성 및 유지보수성을 향상시킵니다.

개요

이 스킬은 Power BI DAX 코드를 분석하고 최적화하기 위한 상세한 지침을 제공합니다. 느린 측정값, 비효율적인 패턴, 중복 계산을 식별하고 수정하여 보고서 성능을 개선합니다.

사용 방법

  1. 최적화하려는 DAX 코드 또는 측정값을 선택합니다.
  2. 프롬프트를 실행하여 코드 분석 및 개선 제안을 받습니다.
  3. 제안된 최적화를 검토하고 적용합니다.

주요 최적화 영역

  • 필터 컨텍스트 최적화: CALCULATE, FILTER 함수 사용 최적화
  • 변수 사용: VAR를 활용한 중간 계산 저장 및 재사용
  • 반복 계산 제거: 불필요한 SUMX, AVERAGEX 등 반복 함수 최적화
  • 데이터 모델 개선: 관계 및 계산 열 최적화
  • 시간 인텔리전스: DATEADD, SAMEPERIODLASTYEAR 등 효율적 사용

예시

비효율적인 코드:

Total Sales = SUMX(Sales, Sales[Quantity] * Sales[Unit Price])

최적화된 코드:

Total Sales Optimized = 
CALCULATE(
    SUM(Sales[Amount]),
    Sales[Quantity] > 0
)

참고 사항

  • 항상 성능 분석기(Performance Analyzer)를 사용하여 개선 효과를 확인하세요.
  • 최적화는 데이터 모델 크기와 복잡성에 따라 달라질 수 있습니다.
  • 모든 DAX 함수를 완전히 대체하는 것이 아니라, 적절한 맥락에서 최적의 방법을 선택합니다.

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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