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Mpep Search

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RobThePCGuy
mpep-search

Expert system for searching USPTO MPEP, 35 USC statutes, 37 CFR regulations, and post-Jan 2024 updates.

Overview

PublisherRobThePCGuy
RepositoryClaude-Patent-Creator
Skill namempep-search
Stars
203
Forks
33
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Mpep 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.
    https://github.com/RobThePCGuy/Claude-Patent-Creator/tree/main/skills/mpep-search
  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/mpep-search .claude/skills/mpep-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mpep 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 Mpep 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 Mpep 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.

MPEP Search Skill

Search MPEP corpus through hybrid RAG (FAISS vector + BM25 keyword + HyDE + cross-encoder reranking).

Sources:

  • MPEP: Manual of Patent Examining Procedure
  • 35 USC: United States Code Title 35
  • 37 CFR: Code of Federal Regulations Title 37
  • Subsequent Publications: Federal Register updates (post-Jan 2024)

Core Operations

1. search_mpep

Inputs:

  • query (string, required): Search query (minimum 3 characters)
  • top_k (int, optional): Number of results (default: 5, max: 20)
  • retrieve_k (int | None, optional): Candidates before reranking (default: top_k * 4, max: 100)
  • source_filter (string | None, optional): Filter by source ("MPEP", "35_USC", "37_CFR", "SUBSEQUENT", or None)
  • is_statute (bool | None, optional): Filter for statute content
  • is_regulation (bool | None, optional): Filter for regulation content
  • is_update (bool | None, optional): Filter for recent updates

Outputs:

python
{
    "rank": int,
    "source": str,
    "section": str,
    "file": str,
    "page": int,
    "has_statute": bool,
    "has_mpep_ref": bool,
    "has_rule_ref": bool,
    "is_statute": bool,
    "is_regulation": bool,
    "is_update": bool,
    "relevance_score": float,
    "text": str,
    # Optional for SUBSEQUENT:
    "doc_type": str,
    "fr_citation": str,
    "effective_date": str
}

Examples:

python
# Basic search
search_mpep("enablement requirement 35 USC 112", top_k=5)

# Search only statutes
search_mpep("written description", top_k=10, is_statute=True)

# Search recent updates
search_mpep("AI inventorship", is_update=True)

# Filter by source
search_mpep("fee schedule", source_filter="37_CFR")

2. get_mpep_section

Retrieve all content from specific MPEP section.

Inputs:

  • section_number (string, required): MPEP section number (e.g., "2100", "608.01")
  • max_chunks (int, optional): Maximum chunks to return (default: 50)

Outputs:

python
{
    "section": str,
    "total_chunks": int,
    "chunks": [
        {
            "text": str,
            "metadata": {
                "source": str,
                "file": str,
                "page": int,
                "section": str,
                "has_statute": bool,
                "has_mpep_ref": bool,
                "has_rule_ref": bool,
                "is_statute": bool,
                "is_regulation": bool,
                "is_update": bool
            }
        }
    ]
}

Error Response:

python
{"error": "No content found for MPEP section {section_number}"}

Examples:

python
# Get MPEP 2100 (Patentability)
get_mpep_section("2100", max_chunks=50)

# Get subsection
get_mpep_section("608.01")

Input Validation

Query validation:

  • Minimum 3 characters
  • Case-insensitive
  • No empty/whitespace-only queries

Section number validation:

  • Numeric with optional decimal (e.g., "100", "2100", "608.01")

Limits:

  • top_k capped at 20
  • retrieve_k capped at 100

Implementation Notes

Index Location:

  • FAISS index: mcp_server/index/mpep_index.faiss
  • Metadata: mcp_server/index/mpep_metadata.json
  • BM25 index: mcp_server/index/mpep_bm25.json

Search Architecture:

  1. HyDE Query Expansion (hypothetical documents)
  2. Hybrid Retrieval (FAISS vector + BM25 keyword via RRF)
  3. Cross-Encoder Reranking (final relevance scores)
  4. Metadata Filtering (source/type filters)

Dependencies:

  • sentence-transformers (BGE-base-en-v1.5)
  • FAISS (vector search)
  • rank-bm25 (keyword search)
  • Cross-encoder (reranking)
  • HyDE (optional, graceful degradation)

Error Handling:

  • Clear error messages for missing index/invalid queries
  • Graceful degradation if HyDE fails
  • Input validation before processing

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mpep Search AI skill do?

Expert system for searching USPTO MPEP, 35 USC statutes, 37 CFR regulations, and post-Jan 2024 updates.

Why use Mpep Search on TypingMind?

Because you install it once and use it with any model. Mpep 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 Mpep Search in TypingMind?

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

Which AI models can use Mpep 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 Mpep Search?

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

Is the Mpep Search 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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