AI Collaboration Standards

Language: English | 繁體中文

Version: 1.1.0 Last Updated: 2026-01-25 Applicability: Claude Code Skills

AI Skills Hierarchy | AI 技能層級

This skill is part of a three-layer AI collaboration system:

Purpose

This skill ensures AI assistants provide accurate, evidence-based responses without hallucination.

Quick Reference

Unified Tag System

This skill uses two complementary tag categories:

Category 1: Certainty Tags (for analyzing existing content)

Category 2: Derivation Tags (for generating new content)

When to Use Which:

Source Types

Core Rules

1. Evidence-Based Only: Only analyze content that has been explicitly read
2. Cite Sources: Include file path and line number for code references
3. Classify Certainty: Tag all statements with certainty level
4. Always Recommend: When presenting options, include a recommended choice with reasoning

Detailed Guidelines

For complete standards, see:

• Anti-Hallucination Guidelines
• Certainty Labels Reference

Examples

✅ Correct Response

[Confirmed] src/auth/service.ts:45 - JWT validation uses 'jsonwebtoken' library
[Inferred] Based on repository pattern in src/repositories/, likely using dependency injection
[Need Confirmation] Should the new feature support multi-tenancy?

❌ Incorrect Response

The system uses Redis for caching (code not reviewed)
The UserService should have an authenticate() method (API not verified)

✅ Correct Option Presentation

There are three options:
1. Redis caching
2. In-memory caching
3. File-based caching

**Recommended: Option 1 (Redis)**: Given the project already has Redis infrastructure
and needs cross-instance cache sharing, Redis is the most suitable choice.

❌ Incorrect Option Presentation

There are three options:
1. Redis caching
2. In-memory caching
3. File-based caching

Please choose one.

Checklist

Before making any statement:

• Source Verified - Have I read the actual file/document?
• Source Type Tagged - Did I specify [Source: Code], [Source: External], etc.?
• Reference Cited - Did I include file path and line number?
• Certainty Classified - Did I tag as [Confirmed], [Inferred], etc.?
• No Fabrication - Did I avoid inventing APIs, configs, or requirements?
• Recommendation Included - When presenting options, did I include a recommended choice?

Configuration Detection

This skill supports project-specific language configuration for certainty tags.

Detection Order

1. Check CONTRIBUTING.md for "Certainty Tag Language" section
2. If found, use the specified language (English / 中文)
3. If not found, default to English tags

First-Time Setup

If no configuration found and context is unclear:

1. Ask the user: "This project hasn't configured certainty tag language preference. Which would you like to use? (English / 中文)"
2. After user selection, suggest documenting in CONTRIBUTING.md:

## Certainty Tag Language

This project uses **[English / 中文]** certainty tags.
<!-- Options: English | 中文 -->

Configuration Example

In project's CONTRIBUTING.md:

## Certainty Tag Language

This project uses **English** certainty tags.

### Tag Reference
- [Confirmed] - Direct evidence from code/docs
- [Inferred] - Logical deduction from evidence
- [Assumption] - Based on common patterns
- [Unknown] - Information not available
- [Need Confirmation] - Requires user clarification

Next Steps Guidance | 下一步引導

After /ai-collaboration completes, the AI assistant should suggest:

AI 協作行為規範已掌握。建議下一步 / AI collaboration behavior standards understood. Suggested next steps:

• 執行 /ai-instruction-standards 建立或更新 CLAUDE.md 等 AI 指令檔案 ⭐ Recommended / 推薦 — 將協作標準寫入專案配置 / Write collaboration standards into project configuration
• 執行 /ai-friendly-architecture 設計 AI 友善架構 — 從長期架構層面優化 AI 協作 / Optimize AI collaboration at the architecture level
• 執行 /code-review 運用確定性標籤進行程式碼審查 — 實踐基於證據的分析 / Practice evidence-based analysis

Related Standards

• Anti-Hallucination Standards
• Code Review Checklist
• Testing Standards

Version History

License

This skill is released under CC BY 4.0.

Source: universal-dev-standards