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Bzd Model Dictionary

Community
BZDmathclub
bzd-model-dictionary

Query the bundled BZD mathematical-modeling dictionary for a user-selected model, explain its assumptions, inputs, outputs, limitations and validation, then judge whether it fits the current competition problem and data. Use when a user asks whether a proposed model is appropriate, requests model feasibility review, or wants dictionary-based alternatives for a modeling task.

Overview

PublisherBZDmathclub
Repositorybzd-math-modeling-skills
Skill namebzd-model-dictionary
Stars
425
Forks
17
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Bzd Model Dictionary 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/BZDmathclub/bzd-math-modeling-skills/tree/main/skills/论文自查类/bzd-model-dictionary
  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.

Use it in TypingMind

Enable Bzd Model Dictionary 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 Bzd Model Dictionary 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 Bzd Model Dictionary 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.

BZD Model Dictionary

Evaluate a user-selected model against the problem and data supplied in the current request. Base dictionary facts on assets/model-dictionary.json; distinguish those facts from the case-specific professional judgment.

Required inputs

Require:

  • selected model name;
  • complete problem statement or relevant task text;
  • involved data, data file, or enough data description to identify variables, sample unit, sample size, time/space structure, missingness, repeated measures, target and constraints.

If the model name is ambiguous, show dictionary candidates and ask the user to identify the intended one. If data are incomplete, give only a provisional judgment and list the missing evidence; do not invent distribution, sample size or variable structure.

Current-task isolation

Treat each invocation as a fresh assessment. Ignore solution ideas, preferred models, computed results and judgments from earlier turns or other conversations unless the user explicitly includes them in the current request. Do not claim to switch the underlying AI model. Instead, state that the assessment uses only the current problem, current data evidence and bundled dictionary records. When the host supports a genuinely separate fresh task, the user may start one, but this is not required for the Skill to apply isolation.

Workflow

  1. Read the current problem and data evidence first. Extract the mathematical task, observation unit, target, explanatory variables, data type, dependence, sample size, missingness, imbalance, time/space structure, constraints and desired output.
  2. Run scripts/query_dictionary.py --model "<name>". Use --exact when an exact record is known. Do not load or reproduce the entire 5713-record asset in the response.
  3. Preserve distinct records when the same algorithm appears for different purposes. Compare model name, large category, subgroup, model category, use case, inputs and outputs before selecting the relevant record.
  4. Present a compact dictionary table for every materially relevant match. Include record ID, name, category/group, applicable scenarios, data requirements, plain-language principle, inputs, outputs, key assumptions, prohibitions, limitations and validation methods. Preserve any returned 资料使用声明 verbatim.
  5. Read references/fit-assessment.md and assess the current case independently. Test task alignment, data sufficiency, assumptions, dependence/leakage, output alignment, validation feasibility, interpretability, computational feasibility and competition-paper completeness.
  6. Give one primary verdict: 合适, 有条件合适, 不合适, or 证据不足,待人工复核. Explain which evidence drives it. Do not treat popularity or model sophistication as suitability.
  7. If not fully suitable, recommend 2-4 alternatives or companion models. Query each alternative in the dictionary before describing it. State whether the alternative replaces, preprocesses, validates or complements the selected model.
  8. End with an actionable use plan: preprocessing, train/test or estimation design, parameter/constraint choices, validation metrics, sensitivity analysis and what the paper must report.

Output structure

Use this order:

  1. 当前任务隔离声明
  2. 题目与数据结构识别
  3. 字典中的模型信息 — table
  4. 适配性逐项判定 — table with criterion, evidence, status and consequence
  5. 最终判定
  6. 替代或配套模型
  7. 落地与论文写作建议
  8. 信息不足与待人工复核

Copyright and data guardrails

  • The bundled JSON is read-only. Never delete, overwrite or normalize away root copyright fields, embedded BZD attribution, commercial-use prohibitions or any record's 资料使用声明.
  • Do not output the full dictionary, bulk-export records, or create a transformed database unless the user separately requests an authorized non-commercial operation that preserves every declaration.
  • Keep the attribution BZD数模社制作 when quoting dictionary content. Do not describe the dictionary as an absolute authority; encourage checking textbooks, papers and official documentation for consequential decisions.
  • Do not expose or reproduce unrelated records. Return only matches and justified alternatives needed for the current task.
  • For niche, new or ambiguous algorithms, verify against authoritative sources when current accuracy requires it and label unresolved claims 待人工复核.

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 Bzd Model Dictionary AI skill do?

Query the bundled BZD mathematical-modeling dictionary for a user-selected model, explain its assumptions, inputs, outputs, limitations and validation, then judge whether it fits the current competition problem and data. Use when a user asks whether a proposed model is appropriate, requests model feasibility review, or wants dictionary-based alternatives for a modeling task.

Why use Bzd Model Dictionary on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BZDmathclub/bzd-math-modeling-skills/tree/main/skills/论文自查类/bzd-model-dictionary. 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 Bzd Model Dictionary?

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 Bzd Model Dictionary?

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

Is the Bzd Model Dictionary AI skill free?

It is published on GitHub by BZDmathclub. 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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