Community研究與資料分析github.com

github/code-exemplars-blueprint-generator

技術中立提示詞生成器,可建立可自訂的 AI 提示詞,用於掃描程式碼庫並識別高品質的程式碼範例。支援多種程式語言(.NET、Java、JavaScript、TypeScript、React、Angular、Python),具備可設定的分析深度、分類方法與文件格式,協助建立編碼標準並維持開發團隊之間的一致性。

code-exemplars-blueprint-generator 是什麼?

這是一個技術中立的提示詞生成工具,專為 AI 代理(如 Claude Code、Cursor、Codex)設計,用於自動掃描程式碼庫並提取高品質的程式碼範例。它提供可完全自訂的提示詞模板,支援 .NET、Java、JavaScript、TypeScript、React、Angular 與 Python 等多種程式語言。使用者可以根據專案需求調整分析深度(簡潔、標準、詳盡)、分類方法(依目錄、語言、設計模式或框架)以及文件輸出格式(YAML、JSON、Markdown)。此工具的核心價值在於幫助團隊建立一致的編碼標準,減少重複評估工作,並確保選出的範例真正反映專案的最佳實務。它與任何 AI 代理或開發環境相容,只需提供專案名稱、程式語言與額外上下文即可快速生成專屬提示詞。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/code-exemplars-blueprint-generator

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說明文件

code-exemplars-blueprint-generator 是做什麼的?

技術中立提示詞生成器,可建立可自訂的 AI 提示詞,用於掃描程式碼庫並識別高品質的程式碼範例。支援多種程式語言(.NET、Java、JavaScript、TypeScript、React、Angular、Python),具備可設定的分析深度、分類方法與文件格式,協助建立編碼標準並維持開發團隊之間的一致性。


技能說明

github/code-exemplars-blueprint-generator 是一個智慧提示詞生成器,旨在協助團隊建立一致且高品質的編碼標準。它會根據使用者輸入的專案名稱、主要程式語言與額外上下文,產生一個結構化的 AI 提示詞。此提示詞可用於任何支援自訂技能或工具的 AI 代理(如 Claude Code、Cursor、Codex 等),以掃描給定的程式碼庫,並識別出其中的優秀程式碼範例。

支援的語言與框架

  • .NET
  • Java
  • JavaScript
  • TypeScript
  • React
  • Angular
  • Python

可設定參數

  • 分析深度:簡潔、標準、詳盡
  • 分類方法:依目錄、依語言、依設計模式、依框架
  • 文件格式:YAML、JSON、Markdown

使用方法

  1. 啟用此技能。
  2. 輸入專案名稱、主要程式語言與任何其他上下文(例如專案目的、架構或特定關注點)。
  3. 技能將產生一個完整的 AI 提示詞,包含分析範圍、分類規則與輸出格式。
  4. 將此提示詞複製到您的 AI 代理或工作流程中,以掃描專案並產生結構化的程式碼範例。

範例

輸入:

專案名稱:MyWebApp
程式語言:TypeScript, React
上下文:使用 Next.js 與 Prisma

輸出(部分提示詞):

你正在分析 MyWebApp 專案。主要技術:TypeScript、React、Next.js、Prisma。
請掃描整個專案並找出高品質的程式碼範例。
分類方法:依目錄。
分析深度:標準。
輸出格式:JSON。

請在以下類別中分別提供 2-3 個範例:
- 元件結構
- 資料存取模式
- 錯誤處理

注意事項

  • 產生的提示詞僅供參考,建議視實際專案需求調整。
  • 確保在執行掃描前已提供足夠的專案上下文。
  • 高品質的範例應包含清晰的註解與良好的程式碼風格。

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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