Audited tarot, I Ching, Xiao Liu Ren, Bazi (Four Pillars). Model never invents results.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Three thousand years ago, Shang kings carved their divinations into bone — the first auditable record of an oracle at work. Oraclebone brings the same discipline to AI agents: audited scripts produce the draw, the hexagram, the pillars; the model only interprets what it is given. It never invents the result.
🔮 Open-source divination toolkit for AI agents, formerly known as ai-divination-skills (renamed in v8.0.0 — the old PyPI package is frozen; install oraclebone instead).
oraclebone 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.
Paste this into your AI agent:
Or install directly for Claude-style local skills:
The default target is ~/.claude/skills. Set AI_SKILLS_DIR for another agent skill directory.
Most AI divination prompts let the model invent the result. This repo separates the two jobs:
That makes readings easier to test, reproduce, audit, and reuse across agents.
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:
The GitHub Pages site ships a six-language switcher — 简体中文, English, 日本語, Português, 한국어, Español. It follows your browser language by default and remembers your manual choice.
Local preview:
Published site:
| 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 |
Install from PyPI:
Or from a checkout:
Use editable mode while developing:
Use one command for every system:
Ask for an agent interpretation template:
Use the Python API directly:
You can still run the underlying scripts directly:
Use a seed for reproducible demos:
All scripts output JSON.
For AI-agent-guided setup, use the remote install runbook:
For direct shell install:
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:
Each skill is self-contained:
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.
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.
oraclebone 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.
Install the package once:
Then edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or
%APPDATA%\Claude\claude_desktop_config.json (Windows):
Restart Claude Desktop. Ask "draw three tarot cards for my decision" — Claude will call
tarot_draw and interpret the JSON output.
Any MCP-aware host follows the same pattern. The server speaks JSON-RPC 2.0 over stdio with no third-party dependencies.
Copy-paste JSON configs and example prompts for each host:
Each skill instructs the agent to:
Shared guidance lives in:
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