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Invention Structuring

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wanshuiyin
invention-structuring

Structure a raw invention idea into a formal invention disclosure. Use when user says "构建发明", "structure invention", "发明构建", "invention disclosure", or wants to formalize a rough idea into a patent-ready structure.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill nameinvention-structuring
Stars
16.3K
Forks
1.4K
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Invention Structuring 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.
  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/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/invention-structuring .claude/skills/invention-structuring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Invention Structuring 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 Invention Structuring 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 Invention Structuring 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.

Invention Structuring

Structure the invention into a formal disclosure based on: $ARGUMENTS

Adapted from the refinement pattern in /research-refine for patent invention decomposition.

Constants

  • REVIEWER_MODEL = gpt-6-astra — External reviewer for invention decomposition validation
  • MAX_REFINEMENT_ROUNDS = 3 — Maximum structuring iterations

Inputs

  1. Invention description from $ARGUMENTS
  2. patent/INVENTION_BRIEF.md if exists
  3. patent/PRIOR_ART_REPORT.md — prior art landscape
  4. patent/NOVELTY_ASSESSMENT.md — novelty analysis

Shared References

Load ../shared-references/patent-writing-principles.md for the Problem-Solution-Advantage framework and claimable subject matter guidelines.

Workflow

Step 1: Problem-Solution-Advantage Framework

Structure the invention using the universal patent framework:

Technical Problem (要解决的技术问题):

  • Derived from prior art deficiencies identified in NOVELTY_ASSESSMENT.md
  • Must be a specific, technical problem (not a commercial or social problem)
  • Statement format: "The technical problem to be solved is how to [specific technical objective] given [specific technical constraint]."

Technical Solution (技术方案):

  • The invention's specific technical contribution
  • Focus on the mechanism, not the result
  • Must be described at a level that matches the intended claim scope
  • Identify which features are known vs. inventive

Advantages (有益效果):

  • Measurable or quantifiable improvements over prior art
  • Must result from the inventive features, not just good engineering
  • Include specific technical effects if known (e.g., "reduces processing time by 40%")

Step 2: Invention Decomposition

Break the invention into three layers:

Core Inventive Concept (核心发明构思):

  • The minimal set of features that make the invention patentable
  • This maps to the independent claim scope
  • Test: if you remove this feature, the invention is no longer novel

Supporting Features (支撑性特征):

  • Features that make the invention work well in practice
  • These become dependent claim material
  • They narrow the scope but add practical value

Optional Features (可选特征):

  • Implementation details, preferred parameters, alternatives
  • These become embodiment material
  • They support broader claim interpretation

Step 3: Claimable Subject Matter Identification

For the core inventive concept, determine what categories of claims to draft:

CategoryApplicabilityContent
Method/processIf invention involves stepsProcess flow, algorithm, workflow
System/apparatusIf invention involves componentsHardware structure, modules, connections
ProductIf invention is a physical deviceShape, structure, composition
Computer-readable mediumIf software invention (US)Stored instructions, non-transitory medium
Product-by-processIf structure is hard to defineProduct defined by how it is made

Step 4: Drawing Plan

Plan what figures are needed to support the claims and specification:

FigureTypeShowsSupports Claim Elements
FIG. 1Block diagramSystem architectureSystem claim components
FIG. 2FlowchartMethod stepsMethod claim steps
FIG. 3Sequence diagramInteraction between componentsSpecific implementation details

If user has provided figures, reference them here. If figures are missing, note what is needed.

Step 5: Dependency Mapping

Map feature dependencies to plan the claim hierarchy:

Independent Claim 1 (method, broadest scope)
├── Core inventive feature A
├── Core inventive feature B
└── Known feature C (for context)

Dependent Claim 2 → narrows feature A with specific implementation
Dependent Claim 3 → narrows feature B with specific parameters
Dependent Claim 4 → depends on 2, adds optional feature D
Dependent Claim 5 → alternative implementation of feature A

Step 6: Cross-Model Validation

Call REVIEWER_MODEL via mcp__codex__codex with xhigh reasoning:

mcp__codex__codex:
  model: gpt-6-astra
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    You are a patent attorney reviewing an invention disclosure.
    Evaluate the structuring choices:

    INVENTION: [Problem-Solution-Advantage summary]
    DECOMPOSITION: [Core/Supporting/Optional features]
    CLAIM PLAN: [intended claim categories and hierarchy]

    Please assess:
    1. Is the Problem-Solution-Advantage framework correctly applied?
    2. Is the core inventive concept correctly identified? Are there features that should be core but are listed as supporting (or vice versa)?
    3. Are the planned claim categories sufficient to protect the invention?
    4. Is the drawing plan adequate for enablement?
    5. Are there any claimable aspects being missed?

Step 7: Output

Write patent/INVENTION_DISCLOSURE.md:

markdown
## Invention Disclosure

### Title
[invention title]

### Technical Problem
[formal problem statement]

### Technical Solution
[formal solution description]

### Advantages
[measurable advantages]

### Feature Decomposition

#### Core Inventive Concept
[features that define independent claim scope]

#### Supporting Features
[features for dependent claims]

#### Optional Features
[features for embodiments]

### Claimable Subject Matter
[method, system, product, medium claims planned]

### Drawing Plan
[figures needed, what each shows]

### Dependency Map
[claim hierarchy plan]

### Inventor Information
[names, contributions]

### Target Jurisdiction
[CN/US/EP/ALL]

Key Rules

  • The Problem must come from prior art deficiencies, not from commercial needs.
  • The Solution must describe the technical mechanism, not just the result.
  • The core inventive concept must be the minimum set of features for patentability.
  • Supporting features should be independently valuable -- each should provide a meaningful technical benefit even if other supporting features are removed.
  • Never invent embodiments that do not correspond to the actual invention or user-provided materials.
  • If mcp__codex__codex is not available, skip cross-model validation and note it in the output.

Frequently asked questions

What does the Invention Structuring AI skill do?

Structure a raw invention idea into a formal invention disclosure. Use when user says "构建发明", "structure invention", "发明构建", "invention disclosure", or wants to formalize a rough idea into a patent-ready structure.

Why use Invention Structuring on TypingMind?

Because you install it once and use it with any model. Invention Structuring 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 Invention Structuring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/invention-structuring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Invention Structuring?

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 Invention Structuring?

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

Is the Invention Structuring AI skill free?

Yes. It is published on GitHub by wanshuiyin 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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