Numerology Analysis Agent Skill

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:

• A JSON object:

   {
     "input": {
       "name": "",
       "birthdate": ""
     }
   }

• 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

• Calculate
• Map
• 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:

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:

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:

• Take the character's Unicode decimal code point.
• Sum the digits of that decimal code point.
• Reduce that sum to a single digit from 1 to 9.
• 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:

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:

{
  "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.