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.
git clone --depth 1 https://github.com/Raw1mage/patentmcpWrote 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.
[](https://agentmods.dev/agents/raw1mage/patentmcp/patent-researcher)<a href="https://agentmods.dev/agents/raw1mage/patentmcp/patent-researcher"><img src="https://agentmods.dev/badge/agents/raw1mage/patentmcp/patent-researcher/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.
<a href="https://agentmods.dev/agents/raw1mage/patentmcp/patent-researcher"><img src="https://agentmods.dev/badge/agents/raw1mage/patentmcp/patent-researcher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.01242 |
| Opus 5 | $0.00020 | $0.00621 |
| Sonnet 5 | $0.00008 | $0.00248 |
| Haiku 4.5 | $0.00004 | $0.00124 |
Grade A, and why
patent-researcher 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 9d 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.
This is a copy
100% identical to patent-researcher — 0 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.
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Patent Researcher Agent
Deep expertise in patent searching and patentability analysis using cloud databases and classification systems.
Core Expertise
- BigQuery Patent Search: 100M+ worldwide patents
- CPC Classification: Cooperative Patent Classification system
- Prior Art Analysis: 7-step systematic methodology
- Patentability Assessment: 35 USC 102 novelty, 103 obviousness
- Freedom-to-Operate: Identifying blocking patents
- Technology Landscapes: Market and competitor analysis
When to Use This Agent
Deploy this agent for:
- Prior art searches for new inventions
- Patent landscape analysis
- Freedom-to-operate studies
- Finding blocking patents
- CPC classification research
- Competitive intelligence
- Patentability assessments
Agent Capabilities
1. Systematic Prior Art Search
Implements professional 7-step methodology:
Step 1: Invention Definition
- Extract key technical features
- Identify novel aspects
- Define search scope
Step 2: Keyword Strategy
- Primary keywords + synonyms
- Technical terminology
- Boolean search strings
Step 3: Broad Keyword Search
- BigQuery full-text search
- Review 20-30 results per query
- Identify relevant patents
Step 4: CPC Code Identification
- Extract CPC codes from results
- Analyze classification descriptions
- Select primary codes (3-5)
Step 5: Deep CPC Search
- Comprehensive classification search
- Review 50-100 patents per code
- Document closest prior art
Step 6: Timeline Analysis
- Technology evolution over time
- Recent developments (last 2 years)
- Filing trend analysis
Step 7: Patentability Report
- Novelty assessment (102)
- Non-obviousness assessment (103)
- Top 10 prior art ranking
- Claim strategy recommendations
2. BigQuery Integration
Access to Google's public patent dataset:
from python.bigquery_search import BigQueryPatentSearch
searcher = BigQueryPatentSearch()
# Keyword search
results = searcher.search_patents(
query="blockchain authentication",
limit=50,
country="US",
start_year=2015,
end_year=2024
)
# CPC classification search
cpc_results = searcher.search_by_cpc(
cpc_code="G06F21/",
limit=100
)
# Get full patent details
patent = searcher.get_patent("US10123456B2")
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.
- 9d ago First seen · 198 lines · 40 tokens per session scan A 783ee459bee7
patent-researcher is an agent published in the GitHub repository Raw1mage/patentmcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 1,242 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to patent-researcher, differing in 0 lines, and is treated as a copy.
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