Community程式設計與開發github.com

github/power-bi-performance-troubleshooting

系統化的 Power BI 效能疑難排解提示，用於識別、診斷和解決 Power BI 模型、報表及查詢中的效能問題。

power-bi-performance-troubleshooting 是什麼？

此技能是一個針對 Power BI 環境的系統化效能疑難排解工具，專為開發者與資料分析師設計。它能協助使用者識別、診斷並解決 Power BI 模型、報表及查詢中的效能瓶頸。主要功能包括分析 DAX 查詢、評估資料模型設計、建議報表最佳化策略，以及識別潛在的資源衝突。此技能支援在 Claude Code、Cursor 或 Codex 等 AI 代理環境中使用，透過結構化的提示引導逐步偵錯流程。其運作方式是根據使用者提供的效能問題描述或匯入的診斷資料（如 DMV 查詢結果或執行計畫），自動產生深入分析與具體改善建議，從而加速問題解決並提升 Power BI 解決方案的整體效能。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/power-bi-performance-troubleshooting

Installed? Explore more 程式設計與開發 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →

查看原文→瀏覽所有技能

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

說明文件

power-bi-performance-troubleshooting 是做什麼的？

系統化的 Power BI 效能疑難排解提示，用於識別、診斷和解決 Power BI 模型、報表及查詢中的效能問題。

概述

此技能提供一個結構化的方法來解決 Power BI 效能問題，涵蓋從資料模型設計到 DAX 查詢最佳化的各個層面。無論是緩慢的報表載入、僵滯的篩選器互動，還是高資源消耗的重新整理作業，此提示都能引導使用者有條理地進行分析與改善。

使用方式

  1. 描述問題：說明您在 Power BI 中遇到的具體效能狀況（例如：「報表篩選後載入時間超過 30 秒」或「倉儲模型的查詢執行時間過長」）。
  2. 提供診斷資訊：若有可用的診斷資料（如 DAX Studio 或 SQL Server Profiler 的輸出、DMV 查詢結果、執行計畫），可一併提供以獲得更精準的分析。
  3. 接收建議：AI 代理將根據提供的資訊，產生逐步的疑難排解建議，包括可能的根本原因、最佳化方案以及具體的實施步驟。

支援的平台

  • Claude Code
  • Cursor
  • Codex
  • 其他相容的 AI 代理環境

關鍵功能

  • DAX 查詢分析：辨識低效的 DAX 公式與查詢模式。
  • 模型設計審查：評估資料模型中的關聯性、維度與事實資料表設計。
  • 報表最佳化：針對視覺效果、交叉篩選與頁面重新整理提供建議。
  • 資源瓶頸識別：分析 CPU、記憶體與儲存體的使用情況。
  • 閘道與資料來源：檢查資料重新整理管線與閘道組態。

環境變數

  • PBI_WORKSPACE_ID：目標 Power BI 工作區的識別碼。
  • PBI_DATASET_ID：要分析的資料集識別碼。
  • PBI_REPORT_ID：要最佳化的報表識別碼。

注意事項

  • 確保您擁有足夠的 Power BI 服務權限以執行診斷。
  • 對於雲端服務問題，請確認網路連線與閘道狀態。
  • 此提示提供的建議為通用指導，可能需要根據實際環境進行調整。

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.

相關技能

steipete/bluebubbles

Send and manage iMessages via BlueBubbles, including attachments, tapbacks, edits, replies, and groups.

community

steipete/eightctl

Control Eight Sleep pods (status, temperature, alarms, schedules).

community

steipete/blucli

BluOS CLI (blu) for discovery, playback, grouping, and volume.

community

steipete/bear-notes

Create, search, and manage Bear notes via grizzly CLI.

community

steipete/camsnap

Capture frames or clips from RTSP/ONVIF cameras.

community

steipete/gifgrep

Search GIF providers with CLI/TUI, download results, and extract stills/sheets.

community
← More 程式設計與開發 skills