Agent skill

Prior Art Search

by illusionaireal in illusionaireal/oh-my-patent

A skill your agent uses when searching patents and technical literature for prior art, novelty, or patentability assessment.

MITAuto-check passedLegal & Compliance

Install Prior Art Search

skills CLI
$ npx skills add illusionaireal/oh-my-patent --skill prior-art-search -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install illusionaireal/oh-my-patent prior-art-search --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/illusionaireal/oh-my-patent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/skills/prior-art-search .claude/skills/prior-art-search && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
prior-art-search
GitHub stars
104
Token cost
~1.8k tokens
SKILL.md length
595 words
Files
1
Skills in repo
7
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when searching patents and technical literature for prior art, novelty, or patentability assessment.

  • Works in 5 steps: google_scholar → uspto_patent (optional but recommended) → semantic_scholar (optional) → …
  • Searching patents and technical literature for prior art
  • SKILL.md covers Overview, Usage, Input Parameters and Output Format, plus 7 more sections
  • Reaches connect.zhihuiya.com

What it does

Prior Art Search is an agent skill from illusionaireal/oh-my-patent. Use when searching patents and technical literature for prior art, novelty, or patentability assessment.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Legal & Compliance, covering Intellectual property. It works with Model Context Protocol and Semantic Scholar. The repository describes itself as: 🏛️ The patent plugin for AI coding agents — turn technical ideas into patent disclosures with Claude Code, Codex, and OpenCode. The licence is MIT.

When your agent uses it

  • Searching patents and technical literature for prior art
  • Patentability assessment

Example prompts

  • “/prior-art-search”

Requirements

  • A credential in YOUR_PATSNAP_MCP_KEY

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. google_scholar
  2. uspto_patent (optional but recommended)
  3. semantic_scholar (optional)
  4. cnipa_patent (recommended, required for CN jurisdiction)
  5. patsnap_search (recommended)

What it can do on your machine

Read from SKILL.md and the folder at commit f44755c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown, typescript and json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • connect.zhihuiya.com

    Also links to:

    • open.zhihuiya.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prior Art Search loads about 1.8k tokens when it runs. Until then it costs about 30 tokens; SKILL.md has 595 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~30
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from illusionaireal/oh-my-patent at commit f44755c, republished under its MIT licence (© illusionaireal). 595 words, ~1,794 tokens.

Download SKILL.mdSave it as .claude/skills/prior-art-search/SKILL.md (or your agent's skills folder).
name
prior-art-search
description
Use when searching patents and technical literature for prior art, novelty, or patentability assessment.

Prior Art Search Skill

Search existing patents and technical literature to identify prior art for novelty and patentability assessment.

Overview

This skill integrates with multiple MCP servers (Google Scholar, USPTO, Semantic Scholar) to conduct comprehensive prior art searches across patents, academic papers, and technical documentation.

Usage

The skill is typically invoked by the patent-landscape-analyst agent during the RESEARCH stage of the patent workflow.

Basic Usage Pattern
typescript
// The skill is invoked through agent orchestration
// Agent: patent-landscape-analyst
// Input: topic keywords, technical domain
// Output: aggregated landscape report

Input Parameters

Required
  • query: Search keywords and technical terms
    • Example: "homomorphic encryption privacy-preserving computation"
Optional
  • searchScope: Time range for results

    • Default: Last 5 years
    • Options: 1year, 3years, 5years, 10years, all
  • maxResultsPerSource: Maximum results from each source

    • Default: 5
    • Range: 1-20
  • sources: Which databases to query

    • Default: All enabled MCP servers
    • Options: google_scholar, uspto_patent, semantic_scholar, cnipa_patent, patsnap_search
  • jurisdiction: Filter by patent jurisdiction

    • Default: All jurisdictions
    • Options: CN, US, PCT
    • EP / JP are not supported (REQ-017): if the user asks for them, say so explicitly, then proceed without a jurisdiction filter (or suggest PCT for international filings). Never pass an unsupported code downstream.

Output Format

Primary Output

File: references/landscape_{topic_slug}.md

Contains aggregated search results organized by:

  • Patent references (with classification codes)
  • Academic literature
  • Technical standards
  • Industry implementations
Secondary Output

Files: references/{source}_{id}.md

Individual evidence cards for each finding:

  • Full citation
  • Abstract/summary
  • Relevance score
  • Key technical features
  • Novelty comparison notes

Examples

markdown
<!-- Invoked by patent-landscape-analyst -->

Input:
- Topic: "blockchain-based cross-border payment with privacy"
- Scope: Last 5 years
- Max results: 10 per source

Output:
references/landscape_blockchain-cross-border-payment.md
  - 8 relevant patents (USPTO, EPO, CNIPA)
  - 12 academic papers (Google Scholar, Semantic Scholar)
  - 3 technical standards (ISO, IEEE)

references/uspto_US10123456.md
references/cnipa_CN108234567.md
references/scholar_arxiv2023-12345.md
...
markdown
Input:
- Query: "federated learning differential privacy medical data"
- Jurisdiction: CN
- Scope: 3 years
- Sources: uspto_patent, semantic_scholar

Output:
references/landscape_federated-learning-medical.md
  - 5 CN patents with IPC codes H04L29/06, G06N20/00
  - 8 academic papers from top conferences
  - Novelty gaps identified in medical-specific privacy
markdown
Input:
- Query: "zero-knowledge proof identity authentication edge computing"
- Scope: All time
- Max results: 20

Output:
references/landscape_zkp-identity-edge.md
  Organized sections:
  1. Core patents (15 references)
  2. Academic foundations (25 papers)
  3. Implementation examples (8 systems)
  4. Novelty analysis summary

MCP Server Dependencies

Required MCP Servers
  1. google_scholar

    • Academic literature search
    • Citation tracking
    • Conference/journal papers
  2. uspto_patent (optional but recommended)

    • US patent database
    • Patent classification lookup
    • Full-text patent search
  3. semantic_scholar (optional)

    • Academic paper search with AI-powered relevance
    • Citation graphs
    • Influence metrics
  4. cnipa_patent (recommended, required for CN jurisdiction)

    • China National Intellectual Property Administration
    • CN patent database with IPC classification
    • Chinese patent full-text search
  5. patsnap_search (recommended)

    • 智慧芽 (Patsnap) patent + literature fusion search
    • 2.1 billion+ global patent data across 174 patent offices
    • Includes legal status, patent family, and citation data
    • REST API + native MCP service (Streamable HTTP)
Configuration

MCP servers should be configured in .claude/settings.json:

json
{
  "mcpServers": {
    "google_scholar": {
      "command": "mcp-google-scholar",
      "args": []
    },
    "semantic_scholar": {
      "command": "mcp-semantic-scholar",
      "args": []
    },
    "patsnap_search": {
      "url": "https://connect.zhihuiya.com/mcp?apikey=YOUR_PATSNAP_MCP_KEY",
      "type": "streamableHttp"
    }
  }
}

智慧芽 MCP Key 获取:

  1. 登录 https://open.zhihuiya.com/
  2. 在 API 密钥页面创建新的 MCP Key(格式 sk-xxxxxxxxxxxx)
  3. 将 Key 填入 url 的 apikey 参数中

注意:智慧芽 MCP 使用 Streamable HTTP transport,不是传统的 stdio MCP。配置时需使用 url + type: "streamableHttp" 格式,而非 command + args。

Show full SKILL.md (226 more words)Show less

Integration with Workflow

Stage: RESEARCH
  1. User provides patent topic
  2. archimedes routes to patent-landscape-analyst
  3. Analyst invokes prior-art-search skill
  4. Results written to references/landscape_{topic_slug}.md
  5. Feature matrix written to references/feature-matrix_{topic_slug}.md
  6. Problem map written to references/problem-map_{topic_slug}.md
  7. Workflow advances to BRAINSTORM_R1
Outputs Used By
  • patentability-evaluator: Assesses novelty against prior art
  • patent-innovation-architect: Identifies gaps for innovation
  • patent-adversarial-examiner: Challenges novelty claims

Performance Notes

  • Search time: 30-90 seconds per query (depends on sources)
  • Network required: MCP servers make external API calls
  • Rate limits: Respect source-specific rate limits (handled by MCP)
  • Caching: Results cached per session to avoid redundant searches

Error Handling

Common Errors
  1. MCP Server Not Available

    • Falls back to available sources
    • Logs warning in landscape report
  2. No Results Found

    • Returns empty landscape with suggestions to broaden query
    • Recommends alternative keywords
  3. Rate Limit Exceeded

    • Pauses and retries with exponential backoff
    • Notifies user of delay

Best Practices

  1. Query Construction

    • Use technical terms, not business descriptions
    • Include domain-specific keywords
    • Combine multiple concepts with proper connectors
  2. Scope Selection

    • Start with 5 years for fast iteration
    • Expand to 10 years if few results
    • Use "all time" only for emerging technologies
  3. Result Validation

    • Always review landscape_{topic_slug}.md before proceeding
    • Verify relevance of top 3 references manually
    • Cross-check patent classifications
  • evidence-card: Formats individual prior art entries
  • quality-gate: Validates landscape report completeness
  • jurisdiction: Filters by patent jurisdiction rules

© illusionaireal, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in src/skills/prior-art-search of illusionaireal/oh-my-patent.

Open the folder on GitHubat commit f44755c

Compare with similar skills

Prior Art Search next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Prior Art Search compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Prior Art Search this skillillusionaireal/oh-my-patent104—~1.8kAutomated safety check: PassMIT
Patsnap Ip Searchingpatsnap/mcp113—~1.1kAutomated safety check: PassApache-2.0
Patsnap Triz Case Librarypatsnap/mcp113—~885Automated safety check: PassApache-2.0
Patsnap Workspacepatsnap/mcp113—~777Automated safety check: PassApache-2.0
Mpep SearchRobThePCGuy/Claude-Patent-Creator196—~978Automated safety check: PassMIT
Patsnap Advanced Patent Searchpatsnap/mcp113—~650Automated safety check: PassApache-2.0

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Questions about Prior Art Search

What does Prior Art Search do?

A skill your agent uses when searching patents and technical literature for prior art, novelty, or patentability assessment. Prior Art Search is an agent skill from illusionaireal/oh-my-patent. Use when searching patents and technical literature for prior art, novelty, or patentability assessment.

When should I use Prior Art Search?

Prior Art Search fits situations like: searching patents and technical literature for prior art; patentability assessment.

How do I install Prior Art Search in Claude Code?

Run `npx skills add illusionaireal/oh-my-patent --skill prior-art-search -a claude-code`. Or copy the skill folder (src/skills/prior-art-search in illusionaireal/oh-my-patent) into .claude/skills/prior-art-search in your project. Claude Code loads it when a task matches its description.

How do I install Prior Art Search in Codex?

Run `npx skills add illusionaireal/oh-my-patent --skill prior-art-search -a codex`. Or copy the skill folder (src/skills/prior-art-search in illusionaireal/oh-my-patent) into .agents/skills/prior-art-search in your project. Codex loads it when a task matches its description.

Can I use Prior Art Search in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add illusionaireal/oh-my-patent --skill prior-art-search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prior-art-search, .gemini/skills/prior-art-search, .github/skills/prior-art-search and .opencode/skills/prior-art-search in your project.

What does Prior Art Search need to run?

SKILL.md names no scripts, command-line tools or credentials: Prior Art Search is instructions for the agent only. Our summary lists: A credential in YOUR_PATSNAP_MCP_KEY.

Does Prior Art Search access the network?

SKILL.md names 2 domains. In commands or code: connect.zhihuiya.com; the agent is likely to contact it when it follows the instructions. As links in the text: open.zhihuiya.com. This is read from the text; nothing was executed.

Is Prior Art Search safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Prior Art Search use?

Prior Art Search is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prior Art Search use?

About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Prior Art Search?

Skills that share tags, products or a category with Prior Art Search: Patsnap Ip Searching (patsnap/mcp, 113 stars), Patsnap Triz Case Library (patsnap/mcp, 113 stars), Patsnap Workspace (patsnap/mcp, 113 stars) and Mpep Search (RobThePCGuy/Claude-Patent-Creator, 196 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prior Art Search?

illusionaireal (a GitHub user) maintains it in illusionaireal/oh-my-patent, which has 104 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on October 9, 2026.

Source: illusionaireal/oh-my-patent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.