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Patent Reviewer

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RobThePCGuy
patent-reviewer

Expert system for reviewing utility patent applications against USPTO MPEP guidelines.

Overview

PublisherRobThePCGuy
RepositoryClaude-Patent-Creator
Skill namepatent-reviewer
Stars
203
Forks
33
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • 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 RobThePCGuy on GitHub. Read the source before you install it.

Installation

Install the Patent Reviewer 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/RobThePCGuy/Claude-Patent-Creator/tree/main/skills/patent-reviewer
  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/RobThePCGuy/Claude-Patent-Creator.git /tmp/Claude-Patent-Creator
mkdir -p .claude/skills
cp -r /tmp/Claude-Patent-Creator/skills/patent-reviewer .claude/skills/patent-reviewer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Patent Reviewer 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 Patent Reviewer 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 Patent Reviewer 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.

Patent Creator Skill

Comprehensive patent creation system with USPTO MPEP, prior art databases, and USPTO API for complete patent application analysis.

When to Use

Review patent applications for USPTO compliance, analyze claims/specifications/formalities, integrate prior art, get USPTO guidance, assist with patent drafting.

Quick Review Commands

bash
/full-review              # Complete parallel review
/review-claims            # 35 USC 112b compliance
/review-specification     # 35 USC 112a compliance
/review-formalities       # MPEP 608 compliance
/create-patent            # New patent application

Available MCP Tools

MPEP & Regulations

  • search_mpep - Search MPEP, 35 USC, 37 CFR
  • get_mpep_section - Get complete MPEP section by number

Patent Search

  • search_patents_google - Full-text search worldwide, claims included (recommended)
  • search_patents_bigquery - Fallback keyword search (~$2 a search)
  • get_patent_bigquery - Get patent details (claims by default; abstract/description opt-in)
  • get_patents_bigquery - Details for up to 50 patents in one query, same cost as one
  • search_patents_by_cpc_bigquery - Search by CPC classification

Patent Analysis

  • review_patent_claims - Analyze claims for 35 USC 112(b)
  • review_specification - Check specification support (112a)
  • check_formalities - Verify MPEP 608 compliance

Diagram Generation

  • render_diagram - Create diagrams from DOT code
  • create_flowchart - Generate patent-style flowcharts
  • create_block_diagram - Create system block diagrams
  • add_diagram_references - Add reference numbers

Review Workflows

Complete Patent Creation Review (/full-review)

Runs all analyzers in parallel for comprehensive analysis:

Output:

  • All compliance issues across components
  • Severity ratings (critical/important/minor)
  • Specific MPEP citations
  • Actionable fix recommendations
  • Prioritized remediation plan

Claims-Only Review (/review-claims)

35 USC 112(b) Compliance:

  • Antecedent basis
  • Definiteness
  • Claim structure
  • Subjective terms
  • Means-plus-function compliance

Specification Review (/review-specification)

35 USC 112(a) Requirements:

  • Written description
  • Enablement
  • Best mode
  • Claim support

Formalities Check (/review-formalities)

MPEP 608 Compliance:

  • Abstract (50-150 words)
  • Title (<=500 characters)
  • Drawing references
  • Required sections

Patent Creation Workflow (/create-patent)

Complete 6-phase patent drafting (55-80 minutes):

  1. Discovery (10-15 min) - Gather invention details
  2. Technology Analysis (5 min) - Assess patentability (101, 102, 103)
  3. Specification Drafting (15-20 min) - Background, summary, detailed description
  4. Claims Drafting (10-15 min) - Independent + dependent claims
  5. Diagrams & Abstract (10-15 min) - Block diagrams, flowcharts, abstract
  6. Automatic Validation (5-10 min) - Runs /full-review, provides fixes

Output: USPTO-ready filing package with diagrams

MPEP Research

python
# General search
search_mpep("claim definiteness 112(b)", top_k=5)

# Filtered by source
search_mpep("enablement", source_filter="35_USC")
search_mpep("abstract", source_filter="MPEP")

# Get specific section
get_mpep_section("2173")  # Claim definiteness

Common MPEP Sections

SectionTopic
608Formalities (abstract, title, drawings)
2100Patentability requirements
2163Guidelines for 35 USC 112(a)
2173Claim definiteness (35 USC 112(b))

Prior Art Integration

python
# Full-text search, worldwide, claims included
# (fallback: search_patents_bigquery with the same arguments)
search_patents_google(
    query="neural network training",
    country="US",
    start_year=2020,
    limit=20
)

# CPC classification search
search_patents_by_cpc_bigquery(cpc_code="G06N3", limit=50)

Integrate findings:

  1. Cite in Background section
  2. Emphasize distinctions in Summary
  3. Explain advantages in Detailed Description
  4. Draft claims to avoid/distinguish
  5. List in IDS

Best Practices

Before Review:

  • Prepare complete application
  • Run /full-review
  • Address critical issues first

During Review:

  • Focus on critical issues (antecedent basis, claim support, definiteness)
  • Use MPEP citations
  • Iterate until compliant

After Review:

  • Document compliance
  • Final /full-review validation
  • Prepare filing package

Common Review Findings

Critical (Must Fix):

  • Missing antecedent basis
  • Claim elements unsupported
  • Abstract exceeds 150 words
  • Indefinite language

Important (Should Fix):

  • Subjective terms without criteria
  • Weak enablement
  • Inconsistent terminology

Minor (Optional):

  • Add example embodiments
  • Strengthen best mode
  • Improve claim scope

Quick Reference

Key Compliance Checks

RequirementCitationTool
Antecedent basis35 USC 112(b)review_patent_claims
Written description35 USC 112(a)review_specification
Enablement35 USC 112(a)review_specification
Abstract lengthMPEP 608.01(b)check_formalities
Title formatMPEP 606check_formalities

Frequently asked questions

What does the Patent Reviewer AI skill do?

Expert system for reviewing utility patent applications against USPTO MPEP guidelines.

Why use Patent Reviewer on TypingMind?

Because you install it once and use it with any model. Patent Reviewer 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 Patent Reviewer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RobThePCGuy/Claude-Patent-Creator/tree/main/skills/patent-reviewer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Patent Reviewer?

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 Patent Reviewer?

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

Is the Patent Reviewer AI skill free?

Yes. It is published on GitHub by RobThePCGuy 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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