patent-search

patent-search is a skill for Claude Code from Raw1mage/patentmcp. It costs 37 tokens per session (577 once invoked), scanned A, a copy of patent-search, MIT.

A skill for searching more than 100 million patent records through BigQuery, Google’s service for querying large datasets. It supports keyword, classification, family, and full-record searches.

In plain words
What is it for?
Use it to search patent titles, abstracts, claims, CPC or IPC classifications, and patent families; retrieve full US patent records; and check BigQuery access and quota.
Why use it?
It gives patent research workflows a direct way to find earlier inventions and related filings across jurisdictions.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: positional $N argument.

Part of the claude-patent-creator-standalone plugin — 17 skills, 16 commands, 13 agents, 2 hooks shipped together

Good fit Use it to search patent titles, abstracts, claims, CPC or IPC classifications, and patent families; retrieve full US patent records; and check BigQuery access and quota.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/raw1mage/patentmcp/patent-search
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Raw1mage/patentmcp --skill patent-search
Clone the repo
git clone --depth 1 https://github.com/Raw1mage/patentmcp

Made for: Claude Code.

Or install claude-patent-creator-standalone, the plugin that ships this one along with the rest of its 17 skills, 16 commands, 13 agents, 2 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for patent-search

README.md
[![agentmods](https://agentmods.dev/badge/skills/raw1mage/patentmcp/patent-search/github.svg)](https://agentmods.dev/skills/raw1mage/patentmcp/patent-search)
Your own site
<a href="https://agentmods.dev/skills/raw1mage/patentmcp/patent-search"><img src="https://agentmods.dev/badge/skills/raw1mage/patentmcp/patent-search/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for patent-search

Your own site · 80×15
<a href="https://agentmods.dev/skills/raw1mage/patentmcp/patent-search"><img src="https://agentmods.dev/badge/skills/raw1mage/patentmcp/patent-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 577 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 97% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00037 $0.00577
Opus 5 $0.00018 $0.00289
Sonnet 5 $0.00007 $0.00115
Haiku 4.5 $0.00004 $0.00058

Measured 10d ago against content hash d9a96562a888, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

patent-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

97% identical to patent-search — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

refs/Claude-Patent-Creator/skills/patent-search/SKILL.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Patent Search Skill

This skill points Claude at the BigQuery patent-search tools registered by the patent-creator MCP server. Call the tools directly; do not shell out to Python.

When to use

  • Find prior art by keyword, classification, or family.
  • Pull full patent records (title, abstract, claims, description) for US patents.
  • Cross-reference an EP/WO patent into its US family member to get full text.

Available MCP tools

Tool What it does
search_patents_bigquery Keyword search across abstract / title / claims (US only for claims).
get_patent_bigquery Full patent details by publication number.
search_patents_by_cpc_bigquery Search by CPC classification prefix.
search_patents_by_ipc_bigquery Search by IPC classification prefix (good for older or non-US patents).
search_patent_family_bigquery All publications sharing a family ID across jurisdictions.
check_bigquery_status Verify auth and quota project before a long workflow.

Cost notes

BigQuery on-demand pricing is $6.25 / TiB (1 TiB free per month). The MCP server enforces a per-query bytes-billed ceiling, defaulting to 25 GiB. Override via PATENT_BIGQUERY_MAX_BYTES_BILLED if you need a larger scan window.

Choosing keywords

  • 2-3 keywords work better than long phrases (BigQuery LIKE matching is literal).
  • For non-US patents, claims is empty in the dataset; the MCP keyword tool already searches title/abstract for those jurisdictions. For full text on EP/WO, use the EPO OPS tools instead.
  • Use search_patent_family_bigquery to bridge from an EP/WO hit to its US family member when you need claims.

Common workflows

Prior art sweep:

  1. search_patents_bigquery(query=…, country="US") — broad scan.
  2. Pick the top hits' CPC codes from get_patent_bigquery.
  3. search_patents_by_cpc_bigquery(cpc_code=…) — pull adjacent technology.

Cross-jurisdiction lookup:

  1. search_patents_bigquery(query=…, country="EP").
  2. For any EP hit, get_patent_bigquery to get its family_id.
  3. search_patent_family_bigquery(family_id=…) to find the US member with full claims.

Read the full file on GitHub · 48 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 48 lines · 37 tokens per session scan A d9a96562a888

Subscribe to this mod's changes

patent-search is a skill published in the GitHub repository Raw1mage/patentmcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 577 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to patent-search, differing in 2 lines, and is treated as a copy.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens