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AI Divination Skills

sapuyou45-bit/ai-divination-skills
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Summary

This server puts tarot, I Ching, Xiao Liu Ren, and Bazi readings behind five MCP tools that ensure the model never generates the draw itself. The script handles randomness locally, emits JSON with full metadata, and hands that result to Claude for interpretation. You get `tarot.draw` with configurable spreads and reversals, `iching.cast` with yarrow or coin methods, `xiaoliuren.cast` for lunar calendar divination, `bazi.cast` for Four Pillars charts, and `interpretation_template` for prompt guidance. Built on stdio transport with seeded modes for reproducible demos. Reach for it when you want symbolic reasoning workflows that separate generation from interpretation and need audit trails for every reading.

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Packageai-divination-skills
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UpdatedJun 9, 2026
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AI Divination Skills

English | 简体中文 | 日本語

ai-divination-skills demo: pip install, tarot draw, I Ching cast, MCP server stdio

tests release PyPI PyPI Downloads Latest release License: MIT Python GitHub Discussions

✨ 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.

✨ Overview

Most AI divination prompts let the model invent the result. This repo separates the two jobs:

  1. A local script produces the card draw, hexagram, or Xiao Liu Ren position.
  2. 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.

Open the published site

Local preview:

python3 -m http.server 8000 -d docs

Published site:

https://sapuyou45-bit.github.io/ai-divination-skills/

🧩 Included Skills

SkillWhat it doesScript
tarotDraws tarot cards for reflection, decisions, creative blocks, and project reframing.skills/tarot/scripts/draw.py
ichingCasts six-line I Ching hexagrams with primary and resulting hexagrams.skills/iching/scripts/cast.py
xiaoliurenCasts Xiao Liu Ren from lunar-style numbers or a Gregorian time fallback.skills/xiaoliuren/scripts/cast.py
baziCasts 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

Copy the skill folders you want into your agent's skill directory:

cp -R skills/tarot ~/.codex/skills/tarot
cp -R skills/iching ~/.codex/skills/iching
cp -R skills/xiaoliuren ~/.codex/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/:

HostFileHow it is invoked
OpenAI / Codex skillsopenai.yamlSkill metadata + brand icons.
Claude Desktop / claude.ai project skillsclaude.yamlTool spec that runs ai-divination <skill>.
Gemini CLI / Gemini Extensionsgemini.yamlExtension manifest that runs the same CLI.
Cursorcursor.mdcRule 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:

  • Claude Desktop
  • Codex CLI
  • Cursor
  • Continue

🤖 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.md
  • shared/interpretation-protocol.md
  • shared/response-contract.md
  • shared/randomness-protocol.md
  • shared/safety-policy.md
  • shared/interpretation-style.md

🧪 Examples

  • examples/tarot-decision.md
  • examples/iching-strategy.md
  • examples/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 issue or propose a new-skill
  • Security: see SECURITY.md for 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:

  • meihua
  • liuyao
  • runes
  • numerology
  • astrology

📄 License

MIT