
create-technical-spike
Create time-boxed technical spike documents for researching and resolving critical development decisions before implementation.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/github/awesome-copilot --skill create-technical-spike- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads create-technical-spike automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.mdapi-azure-speech-realtime-spike.mdapi-vscode-extension-capabilities-spike.md
Performance Examples:
performance-audio-processing-latency-spike.mdperformance-extension-host-limitations-spike.mdperformance-webrtc-reliability-spike.md
Architecture Examples:
architecture-voice-pipeline-design-spike.mdarchitecture-state-management-spike.mdarchitecture-error-handling-strategy-spike.md
Best Practices for AI Agents
-
One Question Per Spike: Each document focuses on a single technical decision or research question
-
Time-Boxed Research: Define specific time limits and deliverables for each spike
-
Evidence-Based Decisions: Require concrete evidence (tests, prototypes, documentation) before marking as complete
-
Clear Recommendations: Document specific recommendations and rationale for implementation
-
Dependency Tracking: Identify how spikes relate to each other and impact project decisions
-
Outcome-Focused: Every spike must result in an actionable decision or recommendation
Research Strategy
Phase 1: Information Gathering
- Search existing documentation using search/fetch tools
- Analyze codebase for existing patterns and constraints
- Research external resources (APIs, libraries, examples)
Phase 2: Validation & Testing
- Create focused prototypes to test specific hypotheses
- Run targeted experiments to validate assumptions
- Document test results with supporting evidence
Phase 3: Decision & Documentation
- Synthesize findings into clear recommendations
- Document implementation guidance for development team
- 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.
FAQ
What does the create-technical-spike skill do?
Create time-boxed technical spike documents for researching and resolving critical development decisions before implementation.
How do I install create-technical-spike?
Run npx skills add https://github.com/github/awesome-copilot --skill create-technical-spike in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/create-technical-spike/ (personal) or .claude/skills/create-technical-spike/ (project).
Which AI agents can use create-technical-spike?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is create-technical-spike safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk flagged items to review; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains create-technical-spike?
It is published by github in github/awesome-copilot, and it is an official skill from the maker of the technology it covers. It has 8,988 recorded installs.
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