Model Knowledge logo

Model Knowledge

Organization
QFIN-tech
model-knowledge

模型知识库 skill。沉淀建模过程中的业务领域知识、特征资产、历史模型档案与建模经验教训,供后续建模检索复用。当需要查询业务字段定义、查询历史模型/建模经验、复用既有特征或方案、或在建模完成后归档沉淀知识时使用。

Overview

PublisherQFIN-tech
Repositorymodel-evo
Skill namemodel-knowledge
Stars
52
Forks
17
Bundled files
13
LicenseMIT
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.

  • 13 bundled files

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

  • Open source

    Published by QFIN-tech on GitHub. Read the source before you install it.

Installation

Install the Model Knowledge 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/QFIN-tech/model-evo/tree/main/model-skills/model-knowledge
  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/QFIN-tech/model-evo.git /tmp/model-evo
mkdir -p .claude/skills
cp -r /tmp/model-evo/model-skills/model-knowledge .claude/skills/model-knowledge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Model Knowledge 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 Model Knowledge 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 Model Knowledge 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.

模型知识库(model-knowledge)

本 skill 是数据资产库,不产生 session 产物(内容不落 runs/)。所有知识按四个知识域组织在 assets/ 下,供上下游 skill 按需加载。

知识域总览

知识域入口文件说明加载时机
业务领域知识assets/business-domain-knowledge/business-domain-knowledge.md业务字段语义、客群标签、用户状态、常用分析指标建模任务启动时默认加载
特征知识assets/feature-knowledge/feature-knowledge.md各业务域特征宽表与特征清单(feature-list/*.csv)feature-matching / feature-analysis 选特征时
历史模型知识assets/historical-model-knowledge/historical-model-knowledge.md模型台账 model_catalog.csv + 模型报告 reports/classification-model-recommend 检索复用时
建模经验知识assets/modeling-experience-knowledge/modeling-experience-knowledge.md方法论、调参经验、踩坑记录training / tuning 阶段参考;建模完成后归档

目录结构

text
model-knowledge/
├── SKILL.md
└── assets/
    ├── business-domain-knowledge/
    │   └── business-domain-knowledge.md      # 字段对应关系、客群/状态标签解释
    ├── feature-knowledge/
    │   ├── feature-knowledge.md              # 特征表索引(业务域 → 特征表 → 清单)
    │   └── feature-list/                     # 各特征表的特征清单 csv
    ├── historical-model-knowledge/
    │   ├── historical-model-knowledge.md     # 历史模型检索入口与归档规范
    │   ├── model_catalog.csv                 # 模型台账
    │   └── reports/                          # {model_id}_{模型简称}.md(+.json) 模型报告
    └── modeling-experience-knowledge/
        ├── modeling-experience-knowledge.md  # 入口:分类组织 + 条目模板 + 通用经验(EXP-G)
        ├── classification-experience.md      # 分类模型专属经验(EXP-C)
        └── uplift-experience.md              # uplift 模型专属经验(EXP-U)

何时使用

  • 查询:需要理解业务字段/客群定义;检索历史模型、既有特征表、调参方案与踩坑记录。
  • 复用:新建模任务匹配相似历史案例,给出可借鉴的特征、算法与参数方案。
  • 归档:当前建模完成(模型发布/对比结论产出)后,沉淀模型档案与经验教训。

工作流程

1. 检索(新任务启动 / 建模过程中)

  1. 默认读 business-domain-knowledge.md 理解业务字段。
  2. 按任务的业务域在 feature-knowledge.md 中定位特征表与特征清单。
  3. 按预测目标/客群在 model_catalog.csv 中匹配历史模型,模型报告路径 非空的进一步读 reports/ 下报告提取 KS/AUC/PSI 与超参数。
  4. 在 modeling-experience-knowledge.md 中匹配经验条目:通用经验(EXP-G)+ 按任务类型读 classification-experience.md(EXP-C)或 uplift-experience.md(EXP-U)。
  5. 输出:可复用的特征/模型/调参建议 + 注意事项。

2. 归档(建模完成后)

当前归档动作要求手动指定操作或者手动触发。

  1. 模型档案:复制 reports/_template_model_report.md 填写,按 reports/{model_id}_{模型简称}.md 命名落盘;在 model_catalog.csv 追加/更新一行并登记 模型报告路径(规范见 reports/README.md)。
  2. 经验条目:按任务类型追加到对应文件(通用→modeling-experience-knowledge.md、分类→classification-experience.md、uplift→uplift-experience.md),并在该文件索引表登记(任务背景 → 做法 → 结论 → 教训)。
  3. 特征资产:如新挖掘了可复用特征表,在 feature-knowledge.md 登记,并将特征清单 csv 落 feature-list/。

输入

  • 检索意图(业务域、预测目标、客群),或待归档的建模产物与结论(session 的 report.md、评估指标、config.json)。

输出

  • 知识条目(业务定义、特征清单、模型档案、经验案例)。
  • 针对新任务的复用建议与风险提示。

约束

  • 本目录为数据资产,不属于 session,不要往 runs/ 搬,也不要把 session 临时产物直接落入本目录。
  • 台账 model_catalog.csv 为 CSV,字段含逗号时用引号包裹。
  • 报告落盘前脱敏:不含用户 ID、手机号、身份证号等明细数据,仅保留聚合统计与指标。

关联 skill

  • 上游:classification-model-development / classification-model-comparison(归档来源)
  • 下游:model-task-routing、classification-model-task-spec、classification-model-recommend、feature-matching、feature-analysis(检索复用)

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 Model Knowledge AI skill do?

模型知识库 skill。沉淀建模过程中的业务领域知识、特征资产、历史模型档案与建模经验教训,供后续建模检索复用。当需要查询业务字段定义、查询历史模型/建模经验、复用既有特征或方案、或在建模完成后归档沉淀知识时使用。

Why use Model Knowledge on TypingMind?

Because you install it once and use it with any model. Model Knowledge 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 Model Knowledge in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/QFIN-tech/model-evo/tree/main/model-skills/model-knowledge. 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 Model Knowledge?

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 Model Knowledge?

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

Is the Model Knowledge AI skill free?

Yes. It is published on GitHub by QFIN-tech under the MIT license. 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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