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Vedic Analysis

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shizhilya
vedic-analysis

使用传统吠陀占星(Vedic astrology)流程“星盘 → Nakshatra → Vimshottari Dasha”分析出生信息,输出只含“业力”和“阶段”的结构化 JSON。Use when a user asks for Vedic astrology, Nakshatra, Janma Nakshatra, Dasha, Vimshottari, karmic themes, or life-stage interpretation from birth data. Do not use for Western astrology-only requests or for scientific proof claims.

Overview

Publishershizhilya
Repositoryyuan
Skill namevedic-analysis
Stars
206
Forks
31
Bundled files
7
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.

  • 7 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 Vedic 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/vedic-astrology
  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/vedic-astrology .claude/skills/vedic-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vedic 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 Vedic 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 Vedic 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.

Vedic Analysis Skill

目标

把用户提供的出生资料或已计算星盘资料,按照传统吠陀占星顺序 星盘 → 星宿(Nakshatra)→ Vimshottari Dasha 进行分析,并返回结构化结果。
除非用户明确要求额外解释,否则默认只输出一个 JSON 对象,顶层只有 业力 和 阶段 两个键。

何时使用

  • 用户要做吠陀占星、Nakshatra、Janma Nakshatra、Dasha、Vimshottari、业力主题、人生阶段解读。
  • 用户提供了出生日期/时间/地点,或已提供月亮位置、星宿、Pada、大运/子运等资料。
  • 用户希望得到传统经验式、象征式、结构化的解读。

不适用

  • 只讨论西洋占星、塔罗、数秘,且不需要吠陀占星框架。
  • 用户要求科学证明、统计验证或天文学论文式论证。
  • 缺少可用资料且又要求精确到日期、度数、宫位,但当前环境没有可靠星历/占星计算能力。

必须遵守

  1. 分析顺序固定:星盘 → 星宿 → Dasha。
  2. 解释依赖传统经验:使用传统吠陀占星的象征、经验、守护星、阶段逻辑,不把解读表述成科学因果。
  3. 优先使用月亮:月亮星宿是主轴;上升、土星、木星、罗喉/计都只作补充修正。
  4. 默认使用 Vimshottari Dasha:除非用户明确指定其他 Dasha 系统。
  5. 默认采用恒星黄道的吠陀框架:若必须自行计算且用户未指定 ayanamsa,优先按 Lahiri 处理;如果当前环境无法可靠计算,必须明确说明,不能捏造度数或起运时间。
  6. 避免绝对化与恐吓式表达:使用“倾向、课题、阶段重点、修正方向、窗口期”等措辞,不用“注定、必然、一定发生”等措辞。
  7. 资料不足时做部分分析:输出最大程度可辩护的结果,并在 JSON 的 依据 或 完整度 中标注不确定性。
  8. 不得伪造精确信息:不能编造行星度数、Nakshatra、Pada、Dasha 起止时间、宫位落点。

可接受的输入形式

  • 完整出生资料:出生日期、出生时间、出生地点、时区。
  • 已计算星盘资料:月亮星座与度数、上升、行星落点、当前大运/子运。
  • 最小可用资料:已知 Nakshatra + Pada,或已知当前 Mahadasha / Antardasha。
  • 自然语言或 JSON 都可。

工作流程

1) 星盘

先整理用户输入,判断当前是否足以确定:

  • 恒星黄道下的月亮绝对黄经
  • 月亮所在 Nakshatra
  • Pada
  • 起始 Mahadasha 与其余额

如果当前环境没有可靠的星历/占星计算能力,而用户也没有提供已计算好的关键资料,则不要硬算。只基于用户已给出的数据继续分析,并明确说明限制。

2) 星宿

依据月亮的 Nakshatra(必要时细化到 Pada)提炼:

  • 核心牵引
  • 天赋模式
  • 障碍模式
  • 修正方向

如果有补充资料,可进一步参考:

  • 上升:人格表达与外在路径
  • 土星:责任、业力负担、长期课题
  • 木星:意义、保护、成长资源
  • 罗喉 / 计都:执念、放大、抽离、前后业力轴

3) Dasha

默认使用 Vimshottari Dasha。

必须掌握的规则:

  • 出生起始 Mahadasha 主星 = 月亮出生 Nakshatra 的守护星
  • 出生时剩余 Mahadasha 余额 = 月亮在出生 Nakshatra 中剩余的弧度比例 × 该 Mahadasha 总年数
  • Mahadasha 顺序固定为:
    Ketu 7 → Venus 20 → Sun 6 → Moon 10 → Mars 7 → Rahu 18 → Jupiter 16 → Saturn 19 → Mercury 17
  • Antardasha 顺序从当前 Mahadasha 主星开始,按同样顺序轮转
  • Antardasha 时长 = Mahadasha 总时长 × 子运主星年数 / 120

如果无法可靠推出精确日期,也要给出阶段主题判断,同时把 时间精度 标成 估算 或 无法计算。

输出要求

返回 且只返回 一个 JSON 对象,顶层只允许以下两个键:

json
{
  "业力": {},
  "阶段": {}
}

推荐使用这个扩展结构:

json
{
  "业力": {
    "核心牵引": "",
    "天赋模式": [],
    "障碍模式": [],
    "修正方向": [],
    "依据": {
      "月亮星宿": "",
      "Pada": "",
      "守护星": "",
      "补充因子": []
    },
    "完整度": "高|中|低"
  },
  "阶段": {
    "当前大运": {
      "名称": "",
      "起止": "",
      "主题": []
    },
    "当前子运": {
      "名称": "",
      "起止": "",
      "主题": []
    },
    "阶段说明": [],
    "注意事项": [],
    "依据": {
      "系统": "Vimshottari Dasha",
      "起运依据": "",
      "时间精度": "精确|估算|无法计算"
    }
  }
}

缺失信息时的处理

  • 没有出生时间,且没有月亮度数:不要假装知道 Nakshatra、Pada 或 Dasha 余额。
  • 只知道 Nakshatra,不知道 Pada:可以给出 Nakshatra 层面的判断,并降低 完整度。
  • 只知道当前大运/子运,不知道起止日期:可以分析阶段主题,把时间精度标为 无法计算。
  • 无法判断的字段:优先用 null、空数组,或简短说明如 依据不足。

风格

  • 保持简洁、稳定、传统、可操作。
  • 以“主题”“牵引”“修正”为中心,不做灾难式预言。
  • 如果用户用中文,就用中文填写 JSON 值。
  • 如果用户用其他语言,可以本地化 JSON 值,但键名仍保持中文。

支持文件

只在需要时再读取以下文件,避免把全部细节都塞进当前上下文:

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 Vedic Analysis AI skill do?

使用传统吠陀占星(Vedic astrology)流程“星盘 → Nakshatra → Vimshottari Dasha”分析出生信息,输出只含“业力”和“阶段”的结构化 JSON。Use when a user asks for Vedic astrology, Nakshatra, Janma Nakshatra, Dasha, Vimshottari, karmic themes, or life-stage interpretation from birth data. Do not use for Western astrology-only requests or for scientific proof claims.

Why use Vedic Analysis on TypingMind?

Because you install it once and use it with any model. Vedic 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 Vedic Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shizhilya/yuan/tree/main/references/vedic-astrology. 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 Vedic 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 Vedic Analysis?

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

Is the Vedic 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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