create-technical-spike

tarafından github

Zaman kutusuyla sınırlı teknik spike belgeleri, uygulama öncesinde kritik geliştirme kararlarını araştırmak için. Net hedefler, araştırma soruları, inceleme planları ve karar çerçeveleri içeren yapılandırılmış markdown spike dosyaları oluşturur. Altı spike kategorisini destekler: API Entegrasyonu, Mimari ve Tasarım, Performans ve Ölçeklenebilirlik, Platform ve Altyapı, Güvenlik ve Uyumluluk, ve Kullanıcı Deneyimi. Araştırma görevleri, başarı kriterleri, bulgu dokümantasyonu için yerleşik kontrol listeleri içerir,...

npx skills add https://github.com/github/awesome-copilot --skill create-technical-spike

Create Technical Spike Document

Create time-boxed technical spike documents for researching critical questions that must be answered before development can proceed. Each spike focuses on a specific technical decision with clear deliverables and timelines.

Document Structure

Create individual files in ${input:FolderPath|docs/spikes} directory. Name each file using the pattern: [category]-[short-description]-spike.md (e.g., api-copilot-integration-spike.md, performance-realtime-audio-spike.md).

---
title: "${input:SpikeTitle}"
category: "${input:Category|Technical}"
status: "🔴 Not Started"
priority: "${input:Priority|High}"
timebox: "${input:Timebox|1 week}"
created: [YYYY-MM-DD]
updated: [YYYY-MM-DD]
owner: "${input:Owner}"
tags: ["technical-spike", "${input:Category|technical}", "research"]
---

# ${input:SpikeTitle}

## Summary

**Spike Objective:** [Clear, specific question or decision that needs resolution]

**Why This Matters:** [Impact on development/architecture decisions]

**Timebox:** [How much time allocated to this spike]

**Decision Deadline:** [When this must be resolved to avoid blocking development]

## Research Question(s)

**Primary Question:** [Main technical question that needs answering]

**Secondary Questions:**

- [Related question 1]
- [Related question 2]
- [Related question 3]

## Investigation Plan

### Research Tasks

- [ ] [Specific research task 1]
- [ ] [Specific research task 2]
- [ ] [Specific research task 3]
- [ ] [Create proof of concept/prototype]
- [ ] [Document findings and recommendations]

### Success Criteria

**This spike is complete when:**

- [ ] [Specific criteria 1]
- [ ] [Specific criteria 2]
- [ ] [Clear recommendation documented]
- [ ] [Proof of concept completed (if applicable)]

## Technical Context

**Related Components:** [List system components affected by this decision]

**Dependencies:** [What other spikes or decisions depend on resolving this]

**Constraints:** [Known limitations or requirements that affect the solution]

## Research Findings

### Investigation Results

[Document research findings, test results, and evidence gathered]

### Prototype/Testing Notes

[Results from any prototypes, spikes, or technical experiments]

### External Resources

- [Link to relevant documentation]
- [Link to API references]
- [Link to community discussions]
- [Link to examples/tutorials]

## Decision

### Recommendation

[Clear recommendation based on research findings]

### Rationale

[Why this approach was chosen over alternatives]

### Implementation Notes

[Key considerations for implementation]

### Follow-up Actions

- [ ] [Action item 1]
- [ ] [Action item 2]
- [ ] [Update architecture documents]
- [ ] [Create implementation tasks]

## Status History

| Date   | Status         | Notes                      |
| ------ | -------------- | -------------------------- |
| [Date] | 🔴 Not Started | Spike created and scoped   |
| [Date] | 🟡 In Progress | Research commenced         |
| [Date] | 🟢 Complete    | [Resolution summary]       |

---

_Last updated: [Date] by [Name]_

Categories for Technical Spikes

API Integration

  • Third-party API capabilities and limitations
  • Integration patterns and authentication
  • Rate limits and performance characteristics

Architecture & Design

  • System architecture decisions
  • Design pattern applicability
  • Component interaction models

Performance & Scalability

  • Performance requirements and constraints
  • Scalability bottlenecks and solutions
  • Resource utilization patterns

Platform & Infrastructure

  • Platform capabilities and limitations
  • Infrastructure requirements
  • Deployment and hosting considerations

Security & Compliance

  • Security requirements and implementations
  • Compliance constraints
  • Authentication and authorization approaches

User Experience

  • User interaction patterns
  • Accessibility requirements
  • Interface design decisions

File Naming Conventions

Use descriptive, kebab-case names that indicate the category and specific unknown:

API/Integration Examples:

  • api-copilot-chat-integration-spike.md
  • api-azure-speech-realtime-spike.md
  • api-vscode-extension-capabilities-spike.md

Performance Examples:

  • performance-audio-processing-latency-spike.md
  • performance-extension-host-limitations-spike.md
  • performance-webrtc-reliability-spike.md

Architecture Examples:

  • architecture-voice-pipeline-design-spike.md
  • architecture-state-management-spike.md
  • architecture-error-handling-strategy-spike.md

Best Practices for AI Agents

  1. One Question Per Spike: Each document focuses on a single technical decision or research question

  2. Time-Boxed Research: Define specific time limits and deliverables for each spike

  3. Evidence-Based Decisions: Require concrete evidence (tests, prototypes, documentation) before marking as complete

  4. Clear Recommendations: Document specific recommendations and rationale for implementation

  5. Dependency Tracking: Identify how spikes relate to each other and impact project decisions

  6. Outcome-Focused: Every spike must result in an actionable decision or recommendation

Research Strategy

Phase 1: Information Gathering

  1. Search existing documentation using search/fetch tools
  2. Analyze codebase for existing patterns and constraints
  3. Research external resources (APIs, libraries, examples)

Phase 2: Validation & Testing

  1. Create focused prototypes to test specific hypotheses
  2. Run targeted experiments to validate assumptions
  3. Document test results with supporting evidence

Phase 3: Decision & Documentation

  1. Synthesize findings into clear recommendations
  2. Document implementation guidance for development team
  3. Create follow-up tasks for implementation

Tools Usage

  • search/searchResults: Research existing solutions and documentation
  • fetch/githubRepo: Analyze external APIs, libraries, and examples
  • codebase: Understand existing system constraints and patterns
  • runTasks: Execute prototypes and validation tests
  • editFiles: Update research progress and findings
  • vscodeAPI: Test VS Code extension capabilities and limitations

Focus on time-boxed research that resolves critical technical decisions and unblocks development progress.

github tarafından daha fazla skill

console-rendering
github
Go'da struct etiketi tabanlı konsol renderlama sistemini kullanma talimatları
official
acquire-codebase-knowledge
github
Bu beceriyi, kullanıcı açıkça mevcut bir kod tabanını haritalamayı, belgelemeyi veya bu kod tabanına dahil olmayı istediğinde kullanın. "Bu kod tabanını haritala", "belgele…" gibi ifadeler için tetikleyin.
official
acreadiness-assess
github
Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc…
official
acreadiness-generate-instructions
github
AgentRC talimatları komutu aracılığıyla özelleştirilmiş AI ajan talimat dosyaları oluşturur. .github/copilot-instructions.md dosyasını üretir (varsayılan, VS'de Copilot için önerilir…
official
acreadiness-policy
github
Kullanıcının bir AgentRC politikası seçmesine, yazmasına veya uygulamasına yardımcı olun. Politikalar, ilgisiz kontrolleri devre dışı bırakarak, etki/seviyeyi geçersiz kılarak, ayarlayarak…
official
add-educational-comments
github
We need to translate the given English text into Turkish, preserving the name "add-educational-comments" if it appears. The text is a description of an agent skill. We must not add any extra commentary, labels, or formatting. The translation should be accurate and natural in Turkish. The text: "Add educational comments to code files to transform them into effective learning resources. Adapts explanation depth and tone to three configurable knowledge levels: beginner, intermediate, and advanced Automatically requests a file if none is provided, with numbered list matching for quick selection Expands files by up to 125% using educational comments only (hard limit: 400 new lines; 300 for files over 1,000 lines) Preserves file encoding, indentation style, syntax correctness, and..." It seems cut off at the end. The original might have more, but we only have this. We'll translate what's given. Note: The name "add-educational-comments" does not appear in the text, so we don't include it. Translation: "Kod dosyalarına
official
adobe-illustrator-scripting
github
ExtendScript (JavaScript/JSX) kullanarak Adobe Illustrator otomasyon betiklerini yazın, hata ayıklayın ve optimize edin. Oluştururken veya değiştirirken kullanın…
official
agent-governance
github
Yapay zeka aracı erişimi ve davranışını kontrol etmek için bildirimsel politikalar, niyet sınıflandırması ve denetim izleri. Birleştirilebilir yönetişim politikaları, izin verilen/engellenen araçları, içerik filtrelerini, hız sınırlarını ve onay gereksinimlerini tanımlar — kod değil yapılandırma olarak saklanır. Anlamsal niyet sınıflandırması, araç yürütülmeden önce desen tabanlı sinyaller kullanarak tehlikeli istemleri (veri sızdırma, ayrıcalık yükseltme, istem enjeksiyonu) tespit eder. Araç düzeyinde yönetişim dekoratörü, politikaları işlevde u
official