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Prior Art Search

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wanshuiyin
prior-art-search

Search patent databases and academic literature for prior art relevant to an invention. Use when user says "现有技术检索", "prior art search", "专利检索", "check patents", or wants to find relevant prior art.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill nameprior-art-search
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 Prior Art Search 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/prior-art-search .claude/skills/prior-art-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prior Art Search 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 Prior Art Search 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 Prior Art Search 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.

Prior Art Search

Search patents and literature for prior art relevant to: $ARGUMENTS

Adapted from /research-lit for patent-specific searching.

Constants

  • MAX_PATENT_RESULTS = 20 — Maximum patent documents to analyze in detail
  • MAX_PAPER_RESULTS = 15 — Maximum academic papers to analyze in detail
  • SEARCH_YEARS = 10 — How many years back to search
  • PATENT_DATABASES = "google-patents, espacenet" — Patent databases to search

Inputs

Read the invention description from:

  1. $ARGUMENTS if it contains technical details
  2. patent/INVENTION_BRIEF.md if it exists
  3. INVENTION_BRIEF.md if it exists at project root

Shared References

Load ../shared-references/prior-art-databases.md for search strategy templates and IPC/CPC classification guidance.

Workflow

Step 1: Extract Search Concepts

From the invention description, identify:

  1. Core inventive concept: The primary technical contribution (1-2 sentences)
  2. Technical problem: What problem it solves
  3. Key technical features: 4-6 specific technical elements that define the invention
  4. IPC/CPC classes: Predict relevant classification codes (e.g., G06N, G06F)

Step 2: Patent Search

For EACH search concept, search via:

Google Patents (via WebSearch):

WebSearch: "site:patents.google.com [keywords]"
WebSearch: "[keywords] patent"
  • Try primary keywords + technical problem keywords
  • Search in English regardless of target jurisdiction
  • For CN inventions, also search Chinese keywords via WebSearch

Espacenet (via WebFetch):

  • WebFetch worldwide.espacenet.com/search results for key queries
  • Search by predicted IPC/CPC classes

Assignee/Inventor Search:

  • If known companies/universities work in this area, search their patent portfolios
  • WebSearch: "[assignee name] patent [technical area]"

For each potentially relevant patent found:

  • WebFetch the patent page to extract: title, abstract, representative claims, filing date, assignee, current status
  • Record IPC/CPC classification codes

Step 3: Academic Literature Search

Search the same concepts in academic databases:

  1. Google Scholar (via WebSearch): WebSearch "[keywords] site:scholar.google.com"
  2. arXiv (via /arxiv if available, or WebSearch): Search for preprints
  3. Semantic Scholar (via /semantic-scholar if API key set, or WebSearch)

For each relevant paper found:

  • Extract title, authors, venue, year, key contribution

Step 4: Classification and Analysis

For each reference found, assess:

  1. Relevance: How closely does it relate to the invention?
  2. Overlap Risk: Does it disclose the same or similar technical solution?
    • HIGH: Anticipates one or more claim elements
    • MEDIUM: Discloses a related but different approach
    • LOW: Same general field, different approach
  3. Relationship: Is it anticipating, relevant, or merely background?

Organize results by IPC/CPC classification to see the technical landscape.

Step 5: Freedom-to-Operate Assessment (Preliminary)

Based on the search results:

  • Identify patents with claims that potentially cover the invention
  • Note any expired patents (public domain)
  • Flag areas where claim scope overlap is significant

Disclaimer: This is a preliminary assessment only. A professional freedom-to-operate analysis by a patent attorney is recommended before filing.

Step 6: Output

Write patent/PRIOR_ART_REPORT.md with:

markdown
## Prior Art Search Report

### Invention Summary
[1-2 sentence description of the searched invention]

### Search Strategy
- Keywords used: [...]
- IPC/CPC classes searched: [...]
- Databases searched: Google Patents, Espacenet, Google Scholar, arXiv
- Date range: [year] to present

### Patent References Found

| # | Patent No. | Title | Date | Assignee | IPC/CPC | Key Teaching | Overlap Risk |
|---|-----------|-------|------|----------|---------|-------------|-------------|
| 1 | CN... / US... | [title] | [date] | [assignee] | [codes] | [2-3 sentences] | HIGH/MEDIUM/LOW |

### Non-Patent Literature Found

| # | Reference | Title | Authors/Venue | Year | Key Contribution | Relevance |
|---|-----------|-------|--------------|------|-----------------|-----------|
| 1 | [DOI/link] | [title] | [authors] | [year] | [1-2 sentences] | HIGH/MEDIUM/LOW |

### Prior Art Landscape
[Organized by technical approach or IPC class, not just chronological]

### Freedom-to-Operate Preliminary Assessment
[Which existing patents might block the invention? What is the risk level?]

### Recommendations
- Suggested claim scope adjustments based on prior art
- Areas where novelty appears strongest
- References to watch during prosecution

Key Rules

  • Never fabricate patent numbers or citations. Mark uncertain references with [VERIFY].
  • Search in English AND the target jurisdiction language (Chinese for CN).
  • Patent prior art includes everything published before the priority date, not just patents.
  • Academic papers are valid prior art for both novelty and inventive step.
  • Include expired patents -- they are public domain but still relevant for novelty.

Frequently asked questions

What does the Prior Art Search AI skill do?

Search patent databases and academic literature for prior art relevant to an invention. Use when user says "现有技术检索", "prior art search", "专利检索", "check patents", or wants to find relevant prior art.

Why use Prior Art Search on TypingMind?

Because you install it once and use it with any model. Prior Art Search 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 Prior Art Search 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/prior-art-search. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prior Art Search?

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 Prior Art Search?

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

Is the Prior Art Search 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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