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patent-novelty-check

Assess patent novelty and non-obviousness against prior art

Install / Use

npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill patent-novelty-check

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

82/100

Supported Platforms

OpenAI Codex

Our assessment of patent-novelty-check

patent-novelty-check scores 82/100 on our quality scale, 124th of 173 Education & Research skills we index.

Its SKILL.md is 5.6 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
8/15
Adoption
18/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 8 days ago, so patent-novelty-check 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.

patent-novelty-check compared with similar skills

All 4 of these similar skills score higher than patent-novelty-check; compare them before choosing.

SkillScoreStarsUpdatedFormat
patent-novelty-check (this skill)by wanshuiyin8216.6k8d agoSKILL.md
Agent-Reachby Panniantong10085.6k11d agoCLAUDE.md
headroomby headroomlabs-ai10073.9ktodayCLAUDE.md
rufloby ruvnet10073.3ktodayCLAUDE.md
last30days-skillby mvanhorn10062.9k3d agoCLAUDE.md

Frequently asked questions

How do I install patent-novelty-check?
Run npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill patent-novelty-check. The install tabs above show the steps for each supported agent.
Which AI agents does patent-novelty-check work with?
It is written for OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
Is patent-novelty-check 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 patent-novelty-check still maintained?
The repository was last updated 8 days ago, so patent-novelty-check is actively maintained.

name: patent-novelty-check description: "Assess patent novelty and non-obviousness against prior art. Use when user says "专利查新", "patent novelty", "可专利性评估", "patentability check", or wants to evaluate if an invention is patentable." argument-hint: "[invention-description-or-brief-path]" allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, mcp__codex__codex

Patent Novelty and Non-Obviousness Check

Assess patentability of: $ARGUMENTS

Adapted from /novelty-check for patent legal standards. Research novelty is NOT the same as patent novelty.

Constants

  • REVIEWER_MODEL = gpt-6-astra — Model used via Codex MCP for cross-model examiner verification
  • NOVELTY_STANDARD = patent — Always use legal patentability standard, not research contribution standard

Inputs

  1. Invention description from $ARGUMENTS
  2. patent/PRIOR_ART_REPORT.md (output of /prior-art-search)
  3. patent/INVENTION_BRIEF.md if exists

Shared References

Load ../shared-references/patent-writing-principles.md for novelty/non-obviousness standards. Load ../shared-references/patent-format-us.md for 102/103 analysis framework.

Workflow

Step 1: Define Claim Elements

From the invention description, extract the key claim elements that would define the invention's scope:

  1. List the technical features that make the invention novel
  2. Identify which features are known from prior art vs. inventive
  3. Draft preliminary claim language for 2-3 independent claims (method + system)

Step 2: Anticipation Analysis (Novelty)

For each preliminary claim, test against EACH prior art reference in PRIOR_ART_REPORT.md:

Single-reference test: Does any single reference disclose ALL claim elements?

| Claim Element | Ref 1 | Ref 2 | Ref 3 | ... | |--------------|-------|-------|-------|-----| | Feature A | Yes/No + evidence | | | | | Feature B | Yes/No + evidence | | | | | Feature C | Yes/No + evidence | | | | | Feature D | Yes/No + evidence | | | |

Verdict per reference:

  • ANTICIPATED: One reference discloses every element → claim is not novel
  • NOT ANTICIPATED: At least one element missing from every single reference → claim is novel

Step 3: Obviousness Analysis (Inventive Step)

If the invention is novel (passes Step 2), test for obviousness:

Two/three-reference combination test: Can 2-3 references be combined to render the claim obvious?

For each combination of the top references:

  1. Primary reference: Which reference is closest to the claimed invention?
  2. Secondary reference(s): Which reference(s) teach the missing element(s)?
  3. Motivation to combine: Would a POSITA have reason to combine these references?
    • Explicit suggestion in the references themselves?
    • Same field, same problem?
    • Common design incentive?
    • Known technique for improving similar devices?

Format as a matrix:

| Combination | Primary | Secondary | Missing Elements | Motivation to Combine | Obvious? | |-------------|---------|-----------|-----------------|----------------------|----------| | Ref1 + Ref2 | Ref1 | Ref2 | Feature D | Same field, similar problem | Yes/No |

Step 4: Cross-Model Examiner Verification

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 senior patent examiner at the [USPTO/CNIPA/EPO].
    Examine the following invention for patentability.

    INVENTION: [invention description + preliminary claims]

    PRIOR ART: [prior art references with key teachings]

    Please analyze:
    1. Anticipation (novelty): Does any single reference anticipate any claim?
    2. Obviousness: Can any combination of references render claims obvious?
    3. Claim scope: Are the claims broad enough to be valuable?
    4. Recommended amendments if any claim is rejected.
    Be rigorous and cite specific references.

Step 5: Jurisdiction-Specific Assessment

For each target jurisdiction, provide a patentability assessment:

Under 35 USC 102/103 (US):

  • Novelty: PASS / FAIL (cite specific reference if fail)
  • Non-obviousness: PASS / FAIL (cite combination if fail)

Under Article 22 CN Patent Law (CN):

  • 新颖性 (Novelty): 通过 / 未通过
  • 创造性 (Inventive Step): 通过 / 未通过

Under Article 54/56 EPC (EP):

  • Novelty: PASS / FAIL
  • Inventive step: PASS / FAIL (problem-solution approach)

Step 6: Output

Write patent/NOVELTY_ASSESSMENT.md:

## Patentability Assessment

### Invention Summary
[description]

### Overall Assessment
[PATENTABLE / PATENTABLE WITH AMENDMENTS / NOT PATENTABLE]

### Anticipation Analysis
[claim-by-claim matrix against each reference]

### Obviousness Analysis
[combination analysis with motivation to combine]

### Cross-Model Examiner Review
[summary of GPT-6-Astra examiner feedback]

### Recommended Claim Amendments
[If claims need modification to overcome prior art, suggest specific amendments]

### Risk Factors
[What could cause rejection during actual prosecution?]

Key Rules

  • Patent novelty is absolute: any public disclosure before the priority date counts as prior art, worldwide.
  • Research novelty ("has anyone published this?") is NOT the same as patent novelty ("does any single reference teach every claim element?").
  • Obviousness requires BOTH: (1) a combination of references AND (2) a motivation to combine them.
  • Never assume the invention is patentable just because no identical patent exists.
  • The assessment is advisory only -- actual prosecution may reveal different prior art.
  • If mcp__codex__codex is not available, skip cross-model examiner review and note it in the output.

Related Skills

View on GitHub
GitHub Stars16.6k
CategoryEducation
Updated8d 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