Create-Technical-Spike
Create-Technical-Spike是一款code方向的AI技能,核心价值是Create time-boxed technical spike documents for researching and resolving critical development decisions before implementation,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
Create time-boxed technical spike documents for researching and resolving critical development decisions before implementation.
mkdir -p ./skills/create-technical-spike && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/create-technical-spike/SKILL.md -o ./skills/create-technical-spike/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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,
🎯 Best For
- Technical writers
- API documentation teams
- Claude users
- GitHub Copilot users
- Software engineers
💡 Use Cases
- Generating JSDoc/TSDoc comments
- Writing README files for new projects
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Create-Technical-Spike to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Does it follow my documentation style?
Most documentation skills respect existing style. Provide a style guide or example in your prompt.
Is Create-Technical-Spike compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Create-Technical-Spike?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Create-Technical-Spike?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/create-technical-spike/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Auto-generating without reviewing
AI documentation can contain inaccuracies. Always verify technical accuracy.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.