SkillAgentSearch skills...

prior-art-search

Search patent databases and academic literature for prior art relevant to an invention

Install / Use

npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill prior-art-search

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

89/100

Supported Platforms

Universal

Our assessment of prior-art-search

prior-art-search scores 89/100 on our quality scale, 90th of 219 Data & Analytics skills we index (top 42%).

Its SKILL.md is 5.4 KB long, well organised into 20 sections with 2 code examples: a solid amount of guidance for an agent.

With 16,644 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
18/20
Description
12/15
Adoption
18/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 7 days ago, so prior-art-search is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

prior-art-search compared with similar skills

All 4 of these similar skills score higher than prior-art-search; compare them before choosing.

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Frequently asked questions

How do I install prior-art-search?
Run npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill prior-art-search. The install tabs above show the steps for each supported agent.
Which AI agents does prior-art-search work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is prior-art-search safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is prior-art-search still maintained?
The repository was last updated 7 days ago, so prior-art-search is actively maintained.

name: prior-art-search description: "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." argument-hint: "[invention-description-or-path]" allowed-tools: Bash(*), Read, Glob, Grep, WebSearch, WebFetch, Write

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:

## 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.

Related Skills

View on GitHub
GitHub Stars16.6k
CategoryData
Updated7d ago
Forks1.4k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions