ai-divination-mcp
MCP server for ai-divination-skills that provides tools for tarot draws, I Ching casts, Xiao Liu Ren casts, and Bazi chart generation, returning auditable JSON results for AI interpretation.
README
AI Divination Skills
<!-- mcp-name: io.github.sapuyou45-bit/ai-divination-skills -->
<p align="center"> <a href="https://pypi.org/project/ai-divination-skills/"> <img src="docs/assets/demo.svg" alt="ai-divination-skills demo: pip install, tarot draw, I Ching cast, MCP server stdio" width="100%"> </a> </p>
✨ Open-source divination skills for AI agents where the tool performs the draw or cast, and AI interprets the concrete result.
ai-divination-skills is a practical skill collection for tarot, I Ching, Xiao Liu Ren, and future symbolic systems. It is built for agent workflows that need auditable randomness, clear method boundaries, and reusable interpretation templates.
This project treats divination as symbolic reasoning and reflection, not deterministic prediction.
⚡ One-line Install for AI Agents
Paste this into your AI agent:
Install AI Divination Skills for this agent: https://raw.githubusercontent.com/sapuyou45-bit/ai-divination-skills/main/docs/install.md
Or install directly for Claude-style local skills:
curl -fsSL https://raw.githubusercontent.com/sapuyou45-bit/ai-divination-skills/main/install.sh | bash
The default target is ~/.claude/skills. Set AI_SKILLS_DIR for another agent skill directory.
✨ Overview
Most AI divination prompts let the model invent the result. This repo separates the two jobs:
- A local script produces the card draw, hexagram, or Xiao Liu Ren position.
- The AI agent interprets that generated result with clear safety boundaries.
That makes readings easier to test, reproduce, audit, and reuse across agents.
🧭 Methodological Rigor
The core rule is simple: scripts or user-provided physical casts generate the divination result; AI interprets that result and does not generate the divination result.
This is not scientific proof of divination efficacy. It is a stricter workflow for symbolic reasoning:
- real readings use system randomness by default
- seeded mode is only for tests and reproducible demos
- traditional methods and limitations are documented per skill
- JSON outputs include enough metadata to audit the method
- approximate modes emit warnings instead of pretending to be traditional
🌐 Multilingual Docs
The GitHub Pages site now defaults to Simplified Chinese. Use the page header switcher there when you want English or Japanese.
Local preview:
python3 -m http.server 8000 -d docs
Published site:
https://sapuyou45-bit.github.io/ai-divination-skills/
🧩 Included Skills
| Skill | What it does | Script |
|---|---|---|
tarot |
Draws tarot cards for reflection, decisions, creative blocks, and project reframing. | skills/tarot/scripts/draw.py |
iching |
Casts six-line I Ching hexagrams with primary and resulting hexagrams. | skills/iching/scripts/cast.py |
xiaoliuren |
Casts Xiao Liu Ren from lunar-style numbers or a Gregorian time fallback. | skills/xiaoliuren/scripts/cast.py |
bazi |
Casts a Bazi (Four Pillars / 八字) chart from a Gregorian birth datetime. Requires the optional lunar-python extra. |
skills/bazi/scripts/cast.py |
🚀 Quick Start
Install from PyPI:
pip install ai-divination-skills
Or from a checkout:
pip install .
Use editable mode while developing:
pip install -e .
Use one command for every system:
ai-divination tarot --deck major --spread three-card --reversals
ai-divination iching --method yarrow
ai-divination xiaoliuren --method numbers --month 3 --day 12 --hour 7
Ask for an agent interpretation template:
ai-divination template tarot
Use the Python API directly:
from ai_divination_skills.tarot import draw
from ai_divination_skills.iching import cast
from ai_divination_skills.xiaoliuren import cast_numbers
You can still run the underlying scripts directly:
python3 skills/tarot/scripts/draw.py --deck major --spread three-card --reversals
python3 skills/iching/scripts/cast.py --method coins
python3 skills/iching/scripts/cast.py --method yarrow
python3 skills/xiaoliuren/scripts/cast.py --method numbers --month 3 --day 12 --hour 7
Use a seed for reproducible demos:
python3 skills/tarot/scripts/draw.py --spread decision --seed demo
python3 skills/iching/scripts/cast.py --method yarrow --seed demo
All scripts output JSON.
📦 Install as Agent Skills
For AI-agent-guided setup, use the remote install runbook:
Install AI Divination Skills for this agent: https://raw.githubusercontent.com/sapuyou45-bit/ai-divination-skills/main/docs/install.md
For direct shell install:
curl -fsSL https://raw.githubusercontent.com/sapuyou45-bit/ai-divination-skills/main/install.sh | bash
The installer copies tarot, iching, and xiaoliuren into ~/.claude/skills by default. To target another agent, set AI_SKILLS_DIR before running it.
Manual install is just copying the folders you want into your agent's skill directory:
mkdir -p ~/.claude/skills
cp -R skills/tarot ~/.claude/skills/tarot
cp -R skills/iching ~/.claude/skills/iching
cp -R skills/xiaoliuren ~/.claude/skills/xiaoliuren
Each skill is self-contained:
skills/name/
SKILL.md
agents/openai.yaml
scripts/
references/
Install individual folders, not the entire repository, when you only want one skill.
Each skill script also works in single-folder mode. If the Python package is installed, the script delegates to the package runtime. If only the skill folder is copied, it falls back to the bundled standalone script in that skill.
Per-host adapters
Every skill ships four adapter files in skills/<skill>/agents/:
| Host | File | How it is invoked |
|---|---|---|
| OpenAI / Codex skills | openai.yaml |
Skill metadata + brand icons. |
| Claude Desktop / claude.ai project skills | claude.yaml |
Tool spec that runs ai-divination <skill>. |
| Gemini CLI / Gemini Extensions | gemini.yaml |
Extension manifest that runs the same CLI. |
| Cursor | cursor.mdc |
Rule file with hard "never invent the draw" guard. |
All four adapters route through the same audited ai-divination <skill> CLI, so the agent host never invents the result.
🧠 Use it from Claude Desktop / Codex / any MCP host
ai-divination-skills ships a built-in MCP server (ai-divination-mcp). Any
Model Context Protocol host — Claude Desktop, Codex,
Continue, Cursor — can mount it with a single config line, and the model gets five tools:
tarot.draw, iching.cast, xiaoliuren.cast, bazi.cast, and interpretation_template.
The model never invents the draw; the server runs the audited scripts locally.
Claude Desktop
Install the package once:
pip install ai-divination-skills
Then edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or
%APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"divination": {
"command": "ai-divination-mcp"
}
}
}
Restart Claude Desktop. Ask "draw three tarot cards for my decision" — Claude will call
tarot.draw and interpret the JSON output.
Codex / Continue / Cursor
Any MCP-aware host follows the same pattern. The server speaks JSON-RPC 2.0 over stdio with no third-party dependencies.
Per-client setup guides
Copy-paste JSON configs and example prompts for each host:
🤖 Agent Behavior
Each skill instructs the agent to:
- generate or accept a concrete draw/cast result
- read concise reference material only when needed
- interpret with the shared response contract
- avoid certainty, fatalism, and professional advice
Shared guidance lives in:
shared/methodology.mdshared/interpretation-protocol.mdshared/response-contract.mdshared/randomness-protocol.mdshared/safety-policy.mdshared/interpretation-style.md
🧪 Examples
examples/tarot-decision.mdexamples/iching-strategy.mdexamples/xiaoliuren-daily.md
🛡️ Safety Boundaries
These skills are not for medical, legal, financial, or crisis guidance.
Good readings should:
- frame the result as symbolic reflection
- connect claims to the generated result
- preserve user agency
- offer small, reversible next steps
- state uncertainty clearly
See ETHICS.md for the full project stance.
🛠️ Development
No runtime dependencies are required beyond Python 3.
Run tests:
python3 -m unittest discover -s tests
Current coverage checks:
- unified CLI routing
- package-only CLI execution
- importable Python APIs
- single-folder skill execution
- skill metadata and asset contracts
- interpretation protocol templates
- tarot spread output
- I Ching cast structure and manual lines
- Xiao Liu Ren number and time fallback behavior
💬 Community
- Releases: https://github.com/sapuyou45-bit/ai-divination-skills/releases
- Roadmap:
ROADMAP.md - Discussions: https://github.com/sapuyou45-bit/ai-divination-skills/discussions
- Issues: pick a
good first issueor propose anew-skill - Security: see
SECURITY.mdfor private vulnerability reporting
🗺️ Roadmap
Near-term:
- Add a published package workflow.
- Expand automated skill validation in CI.
- Add richer reference material for each MVP skill.
- Add more example readings.
- Add more agent integration examples.
Later:
meihualiuyaorunesnumerologyastrology
📄 License
MIT
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。