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Numerology Analysis

Community
shizhilya
numerology-analysis

Compute symbolic numerology from a person's name and birthdate using deterministic mappings only. Use for numerology / 数字命理 / 生命路径数 / 姓名映射 requests and structured numerology JSON. Do not use for predictive fortune-telling or non-symbolic advice.

Overview

Publishershizhilya
Repositoryyuan
Skill namenumerology-analysis
Stars
206
Forks
31
Bundled files
6
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by shizhilya on GitHub. Read the source before you install it.

Installation

Install the Numerology Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/shizhilya/yuan/tree/main/references/numerology
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/shizhilya/yuan.git /tmp/yuan
mkdir -p .claude/skills
cp -r /tmp/yuan/references/numerology .claude/skills/numerology-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Numerology Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Numerology Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Numerology Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Purpose

Return a numerology analysis using only the symbolic rules in this skill. The result must be deterministic, reproducible, and limited to symbolic mappings.

Accepted input

Accept either:

  1. A JSON object:

    json
    {
      "input": {
        "name": "",
        "birthdate": ""
      }
    }
  2. A natural-language request that clearly includes both a name and a birthdate.

If one field is missing, ask only for the missing field. If the birthdate is ambiguous (for example 03/04/2001), ask for ISO YYYY-MM-DD before computing.

Flow

  1. Calculate
  2. Map
  3. Output

Rules

1) Normalize birthdate

  • Accept YYYY-MM-DD, YYYY/MM/DD, or YYYYMMDD.
  • Remove all non-digit characters to produce normalized_birthdate.
  • normalized_birthdate must contain exactly 8 digits.
  • If the date cannot be normalized to 8 digits, request correction.

2) Reduce function

Use this deterministic reducer everywhere a reduction step is required:

text
reduce_number(n):
  while n is not 11, 22, or 33 and n > 9:
      n = sum_of_digits(n)
  return n

This preserves the master numbers 11, 22, and 33.

3) Life path number

Compute:

text
life_path_number = reduce_number(sum(digits(normalized_birthdate)))

4) Normalize name

  • Trim leading and trailing whitespace.
  • Ignore spaces, hyphens, underscores, apostrophes, and common punctuation.
  • Convert Latin letters to uppercase before mapping.
  • Do not transliterate non-Latin scripts into Latin. Use the fallback mapping directly.

5) Name mapping

Latin mapping

Use the following fixed mapping:

  • A J S = 1
  • B K T = 2
  • C L U = 3
  • D M V = 4
  • E N W = 5
  • F O X = 6
  • G P Y = 7
  • H Q Z = 8
  • I R = 9

Fallback mapping for non-Latin or unmapped characters

For every character that is not mapped by the Latin table:

  1. Take the character's Unicode decimal code point.
  2. Sum the digits of that decimal code point.
  3. Reduce that sum to a single digit from 1 to 9.
  4. Use that value as the character mapping.

Example:

  • Unicode code point 24352 -> 2 + 4 + 3 + 5 + 2 = 16 -> 1 + 6 = 7

6) Name number

  • Map every valid character after name normalization.
  • Compute:
text
name_number = reduce_number(sum(mapped_character_values))

7) Personality map

Use only this symbolic map:

  • 1 -> 标签 ["独立", "主动", "开创"], 符号 "起点型"
  • 2 -> 标签 ["协调", "敏感", "合作"], 符号 "联结型"
  • 3 -> 标签 ["表达", "创意", "社交"], 符号 "表达型"
  • 4 -> 标签 ["稳定", "秩序", "执行"], 符号 "结构型"
  • 5 -> 标签 ["变化", "自由", "探索"], 符号 "变化型"
  • 6 -> 标签 ["责任", "关怀", "和谐"], 符号 "照护型"
  • 7 -> 标签 ["内省", "分析", "洞察"], 符号 "思辨型"
  • 8 -> 标签 ["目标", "掌控", "成就"], 符号 "成就型"
  • 9 -> 标签 ["理想", "包容", "完成"], 符号 "完成型"
  • 11 -> 标签 ["直觉", "启发", "感召"], 符号 "启发型"
  • 22 -> 标签 ["建构", "整合", "落地"], 符号 "建构型"
  • 33 -> 标签 ["奉献", "滋养", "引导"], 符号 "滋养型"

Output contract

Return JSON with exactly this top-level structure:

json
{
  "核心数字": {
    "生命路径数": 0,
    "姓名数": 0,
    "主导数": 0,
    "辅助数": 0
  },
  "性格": {
    "生命路径": {
      "标签": [],
      "符号": ""
    },
    "姓名映射": {
      "标签": [],
      "符号": ""
    },
    "综合": {
      "标签": [],
      "说明": ""
    }
  }
}

Output rules

  • 主导数 = 生命路径数
  • 辅助数 = 姓名数
  • 生命路径 uses the symbolic map for life_path_number
  • 姓名映射 uses the symbolic map for name_number
  • 综合.标签 is the de-duplicated concatenation of 生命路径.标签 followed by 姓名映射.标签
  • 综合.说明 must be exactly: 以内在核心采用生命路径数的符号映射,以外在表达采用姓名数的符号映射

Constraints

  • Pure symbolic mapping only.
  • No event prediction.
  • No luck, fate, or timing claims.
  • No medical, legal, financial, or safety claims.
  • Do not add astrology, tarot, zodiac, feng shui, MBTI, clinical psychology, or any external system.
  • Do not infer real-world outcomes from the numbers.
  • Do not add unsupported narrative beyond the provided labels and symbols unless the user explicitly asks for a short explanation.

Formatting behavior

  • Default to JSON only.
  • If the user explicitly asks for an explanation, provide the JSON first and then a short explanation that stays fully consistent with the same mapping and constraints.

Canonical machine-readable spec

The canonical internal spec id is numerology_analysis. The runtime skill name is numerology-analysis for broad compatibility across agent tools. Use assets/spec.json as the machine-readable reference when needed.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Numerology Analysis AI skill do?

Compute symbolic numerology from a person's name and birthdate using deterministic mappings only. Use for numerology / 数字命理 / 生命路径数 / 姓名映射 requests and structured numerology JSON. Do not use for predictive fortune-telling or non-symbolic advice.

Why use Numerology Analysis on TypingMind?

Because you install it once and use it with any model. Numerology Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Numerology Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shizhilya/yuan/tree/main/references/numerology. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Numerology Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Numerology Analysis?

As many as you like. As long as a model supports skills, you can use Numerology Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Numerology Analysis AI skill free?

It is published on GitHub by shizhilya. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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