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Chenggu Analysis

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shizhilya
chenggu-analysis

Structured Chinese Chenggu (“bone weight”) fortune analysis for 称骨算命 / 袁天罡称骨 tasks. Use when the user wants a deterministic lookup + structured output pipeline from birth datetime or normalized lunar components.

Overview

Publishershizhilya
Repositoryyuan
Skill namechenggu-analysis
Stars
206
Forks
31
Bundled files
11
Links
Skill sourceRepository
  • 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.

  • 11 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 Chenggu 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/chenggu
  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/chenggu .claude/skills/chenggu-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chenggu 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 Chenggu 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 Chenggu 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.

Goal

你要把“称骨算命”执行成一个 可计算、可结构化、可扩展 的民俗规则流程，而不是自由发挥的玄学散文。

Core contract

  1. 先标准化输入：优先接受 lunar_components；若只有 birth_datetime 且是公历，先做可靠的农历转换。
  2. 再查表：严格从 JSON 查 year/month/day/hour 四项权重。
  3. 只做整数运算：内部统一使用“钱”。
  4. 精确匹配：total_qian 必须一一对应 verse_profiles 的唯一档位。
  5. 结构化输出：必须同时给出机器可读 JSON 与中文展示层。
  6. 不臆测：不会转换就明确限制，不要猜农历日期，不要自造权重。

Scope

仅在以下场景使用：

  • 称骨算命 / 袁天罡称骨 / 骨重 / 几两几钱 / 四两几钱
  • 希望把歌诀拆成事业、财运、婚姻等结构化结果
  • 需要把民俗规则做成可编排的 agent / skill / rule-engine

不要用于：

  • 八字、紫微、星座、塔罗等非称骨体系
  • 缺少可靠农历转换能力却仍要求你硬算公历结果
  • 任何需要科学、医学、法律、投资保证的场景

Resources

  • references/chenggu_skill_spec.json: 完整规则总表（可直接被工具读取）
  • references/lookup_tables.json: 年/月/日/时权重查表
  • references/verse_profiles.json: 51 档歌诀与结构化标签
  • references/skill_contract.json: 输入、派生特征、规则、决策流
  • references/implementation_notes.md: 边界规则、转换要求、限制说明
  • assets/output-template.json: 中文展示模板
  • assets/input.schema.json / assets/output.schema.json: 机器可读 schema
  • references/examples.md: 参考输入输出

Execution flow

  1. 读取输入。
  2. 若存在 lunar_components，直接使用：
    • year_ganzhi
    • month
    • day
    • hour_branch
    • is_leap_month
  3. 若仅有 birth_datetime：
    • 先识别 calendar_type
    • 若为 gregorian，调用宿主环境中可靠的农历转换能力
    • 若为 lunar，提取农历年月日时
  4. 应用边界规则：
    • 夜子时 23:00~23:59 视为次日
    • 闰月 115 按本月，1630 按下月
  5. 查 lookup_tables.json：
    • year_weight_qian_by_ganzhi
    • month_weight_qian
    • day_weight_qian
    • hour_weight_qian
  6. 计算：
    • total_qian = year + month + day + hour
    • total_display = verse_profiles[total_qian].total_display
    • grade = verse_profiles[total_qian].grade
  7. 读取 verse_profiles.json 中对应档位：
    • verse_canonical
    • structured.trajectory_tags
    • structured.career_level
    • structured.wealth_level
    • structured.marriage_level
    • structured.scores
  8. 生成输出：
    • 顶层 JSON 遵循 assets/output.schema.json
    • 同时填充 display_zh，键名与 assets/output-template.json 一致
  9. 给出简短说明：
    • 说明这是民俗解释，不是科学结论
    • 点明不确定性来源：版本差异、农历转换、歌诀解释主观性

Output requirements

  • 必须先输出一个完整 JSON 对象。
  • JSON 至少包括：
    • skill_name
    • api_name
    • input_normalized
    • weights
    • total_weight
    • grade
    • verse_canonical
    • analysis
    • advice
    • display_zh
    • limitations
  • display_zh 内必须包含：
    • 骨重明细
    • 总骨重
    • 等级
    • 原始歌诀
    • 结构化分析
    • 建议

Style

  • 默认跟随用户语言；中文用户优先中文。
  • 结果要简洁、可审计、便于程序后处理。
  • 不要把歌诀扩写成冗长散文；重点是结构化与可复核。

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 Chenggu Analysis AI skill do?

Structured Chinese Chenggu (“bone weight”) fortune analysis for 称骨算命 / 袁天罡称骨 tasks. Use when the user wants a deterministic lookup + structured output pipeline from birth datetime or normalized lunar components.

Why use Chenggu Analysis on TypingMind?

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

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

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

Is the Chenggu 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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