create-technical-spike

作成者: github

タイムボックス化された技術スパイクドキュメントで、実装前の重要な開発判断を調査します。明確な目的、調査質問、調査計画、意思決定フレームワークを含む構造化されたマークダウンスパイクファイルを生成します。API統合、アーキテクチャと設計、パフォーマンスとスケーラビリティ、プラットフォームとインフラストラクチャ、セキュリティとコンプライアンス、ユーザーエクスペリエンスの6つのスパイクカテゴリをサポートします。調査タスク、成功基準、発見事項の文書化のための組み込みチェックリストを含みます。

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のその他のスキル

console-rendering
github
Goにおける構造体タグベースのコンソールレンダリングシステムの使用手順
official
acquire-codebase-knowledge
github
ユーザーが既存のコードベースのマッピング、ドキュメント化、またはオンボーディングを明示的に依頼した場合にこのスキルを使用します。「このコードベースをマッピングして」「ドキュメント化して…」といったプロンプトで起動します。
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のinstructionsコマンドを使用して、カスタマイズされたAIエージェント指示ファイルを生成します。.github/copilot-instructions.md(デフォルト、VS CodeのCopilotに推奨)を出力します…
official
acreadiness-policy
github
ユーザーがAgentRCポリシーを選択、作成、または適用するのを支援します。ポリシーは、関連性のないチェックを無効にしたり、影響度/レベルを上書きしたり、設定することで、レディネススコアリングをカスタマイズします。
official
add-educational-comments
github
コードファイルに教育的なコメントを追加し、効果的な学習リソースに変換します。説明の深さとトーンを、設定可能な3つの知識レベル(初心者、中級、上級)に適応させます。ファイルが提供されない場合は自動的にリクエストし、番号付きリストで素早く選択できます。教育的なコメントのみを使用してファイルを最大125%拡張します(ハードリミット:新しい行400行、1,000行を超えるファイルは300行)。ファイルのエンコーディング、インデントスタイル、構文の正確性を保持し、...
official
adobe-illustrator-scripting
github
ExtendScript(JavaScript/JSX)を使用して、Adobe Illustratorの自動化スクリプトの作成、デバッグ、最適化を行います。スクリプトを作成または修正して操作する際に使用します…
official
agent-governance
github
宣言的なポリシー、意図分類、および監査証跡により、AIエージェントのツールアクセスと動作を制御します。構成可能なガバナンスポリシーは、許可/ブロックされたツール、コンテンツフィルター、レート制限、承認要件を定義し、コードではなく設定として保存されます。セマンティック意図分類は、パターンベースのシグナルを使用して、ツール実行前に危険なプロンプト(データ流出、権限昇格、プロンプトインジェクション)を検出します。ツールレベルのガバナンスデコレーターは、関数にポリシーを適用します...
official