Prevents AI hallucination by enforcing evidence-based responses with certainty and derivation tags. Use when analyzing code, making recommendations, or discussing confidence levels.
How this skill is triggered — by the user, by Claude, or both
Slash command
/universal-dev-standards:ai-collaboration-standardsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
> **Language**: English | [繁體中文](../../locales/zh-TW/skills/ai-collaboration-standards/SKILL.md)
Language: English | 繁體中文
Version: 1.1.0 Last Updated: 2026-01-25 Applicability: Claude Code Skills
This skill is part of a three-layer AI collaboration system:
| Layer | Skill | Question it Answers | 回答的問題 |
|---|---|---|---|
| Behavior (Immediate) | /ai-collaboration (this) | "How should AI respond accurately?" | 「AI 如何準確回應?」 |
| Configuration (Session) | /ai-instruction-standards | "What to write in CLAUDE.md?" | 「CLAUDE.md 該寫什麼?」 |
| Architecture (Long-term) | /ai-friendly-architecture | "How to structure code for AI?" | 「如何讓專案對 AI 友善?」 |
This skill ensures AI assistants provide accurate, evidence-based responses without hallucination.
This skill uses two complementary tag categories:
Category 1: Certainty Tags (for analyzing existing content)
| Tag | Use When |
|---|---|
[Confirmed] | Direct evidence from code/docs |
[Inferred] | Logical deduction from evidence |
[Assumption] | Based on common patterns (needs verification) |
[Unknown] | Information not available |
[Need Confirmation] | Requires user clarification |
Category 2: Derivation Tags (for generating new content)
| Tag | Use When |
|---|---|
[Source] | Direct content from spec/requirement |
[Derived] | Transformed from source content |
[Generated] | AI-generated structure |
[TODO] | Requires human implementation |
When to Use Which:
| Workflow | Primary Tags |
|---|---|
| Code analysis | Certainty Tags |
| Reverse engineering | Certainty Tags |
| Forward derivation | Derivation Tags |
| Spec generation | Derivation Tags |
| Source Type | Tag | Reliability |
|---|---|---|
| Project Code | [Source: Code] | ⭐⭐⭐⭐⭐ Highest |
| Project Docs | [Source: Docs] | ⭐⭐⭐⭐ High |
| External Docs | [Source: External] | ⭐⭐⭐⭐ High |
| Web Search | [Source: Search] | ⭐⭐⭐ Medium |
| AI Knowledge | [Source: Knowledge] | ⭐⭐ Low |
| User Provided | [Source: User] | ⭐⭐⭐ Medium |
For complete standards, see:
[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?
The system uses Redis for caching (code not reviewed)
The UserService should have an authenticate() method (API not verified)
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.
There are three options:
1. Redis caching
2. In-memory caching
3. File-based caching
Please choose one.
Before making any statement:
[Source: Code], [Source: External], etc.?[Confirmed], [Inferred], etc.?This skill supports project-specific language configuration for certainty tags.
CONTRIBUTING.md for "Certainty Tag Language" sectionIf no configuration found and context is unclear:
CONTRIBUTING.md:## Certainty Tag Language
This project uses **[English / 中文]** certainty tags.
<!-- Options: English | 中文 -->
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
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
| Version | Date | Changes |
|---|---|---|
| 1.1.0 | 2026-01-25 | Added: Unified Tag System with Certainty and Derivation tag categories |
| 1.0.0 | 2025-12-24 | Added: Standard sections (Purpose, Related Standards, Version History, License) |
This skill is released under CC BY 4.0.
Source: universal-dev-standards
npx claudepluginhub mvandermeulen/universal-dev-standards2plugins reuse this skill
First indexed Jun 19, 2026
Prevents AI hallucination by enforcing evidence-based responses with certainty and derivation tags. Use when analyzing code, making recommendations, or discussing confidence levels.
Surfaces AI collaboration protocols when users refer to entities by colloquial labels, ask how to structure a Claude workflow, or describe multi-step work without durable URLs.
Evaluates AI contribution levels in software projects using the AI Assessment Scale (AIAS) v2 framework. Documents AI usage for transparency and compliance.