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Normative Meaning Argumentation

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THUYRan
normative-meaning-argumentation

Analyze the purpose and value orientation of legal norms, and how facts should enter normative evaluation; determine the limits of subsumption (涵摄). Distinguish this appropriately from teleological / normative-purpose interpretation within legal interpretation.

Overview

PublisherTHUYRan
RepositoryLegal-Skills-Chinese
Skill namenormative-meaning-argumentation
Stars
876
Forks
97
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Normative Meaning Argumentation 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/THUYRan/Legal-Skills-Chinese/tree/main/English/skills/Normative-Meaning-Argumentation
  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/THUYRan/Legal-Skills-Chinese.git /tmp/Legal-Skills-Chinese
mkdir -p .claude/skills
cp -r /tmp/Legal-Skills-Chinese/English/skills/Normative-Meaning-Argumentation .claude/skills/normative-meaning-argumentation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Normative Meaning Argumentation 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 Normative Meaning Argumentation 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 Normative Meaning Argumentation 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.

Chinese source (authoritative): ../../skills/Normative-Meaning-Argumentation/SKILL.md

Skill Title

Normative-meaning argumentation in legal reasoning means that, in applying law, one does not stop at explaining what a legal norm “means,” but further argues why that norm can evaluate particular facts, to what extent it may do so, and whether the facts fall within the type of risk, interest, or conduct the norm is meant to regulate. This skill’s focus is not abstract construction of statutory text, but building an argumentative chain of “normative purpose — factual features — evaluative conclusion” in the concrete case.

Capabilities

List what this skill can do:

  • Capability 1: Identify the normative purpose and value orientation of a legal norm. Analyze what interests a norm aims to protect, what risks it aims to prevent, and what legal order it aims to maintain, and distinguish primary purpose, incidental purpose, and institutional background.
  • Capability 2: Judge whether case facts are normatively relevant. From complex facts, screen those related to the norm’s purpose; decide which facts may enter legal evaluation and which are only background or emotionally charged facts.
  • Capability 3: Argue the limits of subsumption (涵摄). Analyze whether facts fall within the norm’s regulatory scope; decide whether there are grounds for expansive application, restrictive application, or exclusion; avoid mechanical subsumption.
  • Capability 4: Connect normative meaning to the adjudicative conclusion. Organize legal argument around normative purpose so that “apply this provision” or “do not apply this provision” is persuasive.
  • Capability 5: Distinguish normative-meaning argumentation from teleological / normative-purpose interpretation. Avoid stopping at “what is the purpose of this article,” and further explain how that purpose performs an evaluative role on the case facts.

How to Use

Step-by-step instructions:

  1. Step 1: Identify the legal norm to be applied. Clarify the statute, rule, or principle that may apply, and extract its constituent elements, legal consequences, and institutional position.
  2. Step 2: Identify normative purpose. Analyze the legal interests protected, risks prevented, and transactional or social order maintained. For example, whether a norm protects personality dignity, transactional security, reliance interests, or vulnerable parties, or controls behavioral risk.
  3. Step 3: Distill the object of normative evaluation. Decide what the norm truly evaluates.
  4. Step 4: Screen normatively relevant facts. Select facts directly related to the norm’s purpose; exclude facts unrelated or only weakly related to normative evaluation.
  5. Step 5: Establish correspondence between “normative purpose — factual features.” Explain why the facts touch the risk, interest, or order problem the norm concerns. For example, conduct that does not fully match the typical pattern may still create the risk the norm seeks to prevent.
  6. Step 6: Judge the boundary of subsumption. Analyze whether the facts should be brought within the norm’s scope of application. If yes, state reasons; if not, explain why they exceed the norm’s purpose, would over-expand it, or would disrupt systematic coherence.
  7. Step 7: Form the normative-meaning conclusion. Output a clear conclusion: how the norm should be understood in this case, how it evaluates the facts, and whether it supports the ultimate legal conclusion.

Input Format

Describe expected input:

  • Format 1: Legal provision + case facts — for normative argumentation in a concrete case. Example: “Under Article 1032 of the Civil Code (民法典), analyze whether secretly photographing the interior of another’s residence constitutes infringement of the right to privacy.”
  • Format 2: Issue in dispute + related provisions — for normative evaluation around a contested question. Example: “Issue: Does disclosing another’s ID number infringe the right to privacy? Related provisions: Articles 1032 and 1034 of the Civil Code.”
  • Format 3: Judicial view + argument to be tested — for assessing whether a court’s reasoning is adequate. Example: “The court held that the conduct did not constitute privacy infringement; please test whether its reasoning is adequate from a normative-meaning perspective.”
  • Format 4: Academic thesis + normative basis — for normative argumentation in scholarly writing. Example: “I want to argue that the right of access to personal data has the character of a personality right; please develop this from a normative-meaning perspective.”

Output Format

Describe what will be produced:

  • Output 1: Normative-purpose analysis — institutional function, protected interests, risk-control goals, and value orientation.
  • Output 2: Object of normative evaluation — what conduct, state of affairs, risk, interest conflict, or legal relationship the norm truly evaluates.
  • Output 3: Fact screening and normative relevance — distinguish core, auxiliary, and irrelevant facts; explain which may enter normative evaluation.
  • Output 4: Correspondence between normative purpose and case facts — argue how the facts do or do not touch the legal interests, risks, or order the norm concerns.
  • Output 5: Judgment on subsumption limits — whether the facts should fall within the norm’s scope, with reasons for expansive, restrictive, or exclusive application.
  • Output 6: Normative-meaning conclusion — a concise legal-argumentative conclusion providing a basis for later interest balancing, analogy, systematic coherence, or the adjudicative result.

Example Usage

Provide concrete examples: “Analyze the application of Article 1032 of the Civil Code (privacy protection) in a case of ‘disclosing another’s ID number.’”

Example output 1

  1. Normative purpose: Article 1032 of the Civil Code aims to protect a natural person’s private life peace and private space, private activities, and private information that they do not wish others to know. Its purpose is not to protect all personal information, but information interests closely related to personality dignity, private-life boundaries, and freedom from interference.
  2. Object of normative evaluation: The norm does not evaluate the bare fact that “information was disclosed,” but whether disclosure invaded the private sphere, undermined control over private information, and was sufficient to impair private-life peace.
  3. Factual relevance: Whether an ID number is privacy depends on disclosure setting, scope, use, combination with other identifying information, and risk of real-world disturbance. Mere disclosure of an ID number does not automatically equal privacy infringement.
  4. Subsumption limits: If disclosure is enough to identify the person and further bring harassment, impersonation, discrimination, or other private-life risks, the facts may enter privacy-norm evaluation. Conversely, if the disclosure scope is minimal, the information is already lawfully public, and there is no risk of private-life disturbance, mechanical application of privacy protection is inappropriate.
  5. Conclusion: Evaluation of disclosing an ID number should center on whether the conduct touches the private-life boundary protected by the privacy norm, not on “personal information was disclosed” alone as a direct finding of privacy infringement.

Example request 2

“Analyze whether quality defects in data under a data-supply contract can apply the rules on defects in goods under sales contracts.”

Example output 2

  1. Normative purpose: Defect-warranty rules for goods in sales contracts mainly ensure that the buyer obtains a subject-matter conforming to agreement or ordinary use. Their institutional background is delivery of corporeal goods and transfer of ownership. The norm concerns whether the delivered thing, at delivery, has the agreed quality, ordinary utility, and transactional security.
  2. Object of normative evaluation: Traditional defect-warranty rules evaluate the quality state of corporeal goods. In data-supply contracts, the core issue is often not physical quality defect, but whether the data is fit for the contractual purpose—including legality, accuracy, completeness, timeliness, and usability.
  3. Factual relevance: In data transactions, data may be continuously updated, dynamically accessed, repeatedly processed, and dependent on interfaces, formats, algorithmic environments, and use scenarios. Normatively relevant facts therefore include not only content at delivery, but also data source, processing, update mechanisms, interface stability, and purpose of use.
  4. Subsumption limits: Normative-meaning argumentation requires understanding “non-conforming quality” as “performance not conforming to contractual purpose,” shifting the evaluative center from static defects in a thing to obstacles to contractual fitness.
  5. Conclusion: Liability for data-quality defects may draw on sales-contract defect-warranty rules, but cannot simply be equated with liability for defects in goods. Normative evaluation should center on whether the data meets contractual purpose, use scenarios, and ongoing supply obligations.

Best Practices

  1. Do not only interpret statutory meaning; explain how the statute evaluates case facts. The key to normative-meaning argumentation is not “what does the text mean,” but “why can this text evaluate these facts, and to what extent.”

  2. Interpret with systematic position. When analyzing a norm, attend to adjacent norms, higher-level principles, and institutional structure; avoid isolated interpretation.

Limitations

  • Limitation 1: Cannot replace complete legal interpretation. This skill mainly serves case-specific argumentation and cannot fully replace textual, systematic, historical, and teleological interpretation as a complete interpretive toolkit.

  • Limitation 2: Cannot alone decide the case outcome. Normative-meaning argumentation only shows whether facts may enter normative evaluation; the final conclusion still requires interest balancing, burden of proof, legal consequences, defenses, and systematic coherence.

  • Limitation 3: Easily misused as bare value judgment. Without statutory basis and institutional background, normative-meaning argumentation may slide into abstract value declaration. Always proceed around a concrete norm.

Frequently asked questions

What does the Normative Meaning Argumentation AI skill do?

Analyze the purpose and value orientation of legal norms, and how facts should enter normative evaluation; determine the limits of subsumption (涵摄). Distinguish this appropriately from teleological / normative-purpose interpretation within legal interpretation.

Why use Normative Meaning Argumentation on TypingMind?

Because you install it once and use it with any model. Normative Meaning Argumentation 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 Normative Meaning Argumentation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/THUYRan/Legal-Skills-Chinese/tree/main/English/skills/Normative-Meaning-Argumentation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Normative Meaning Argumentation?

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 Normative Meaning Argumentation?

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

Is the Normative Meaning Argumentation AI skill free?

It is published on GitHub by THUYRan. 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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