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USPTO Database · FreedomIntelligence | DecimalAI

Legalby FreedomIntelligence

USPTO Database

Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.

SkillScore

SkillScore41/100

Test results 41

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%41%of test cases passed +27 pts

Without skill3 of 22

With skill9 of 22

6 fixed3 passed either way13 still failing0 broke

Measured on gemini-3.6-flash7/30/2026v119 comparable

Track record

Agent runs: 0; All time

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add uspto-database
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install uspto-database

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/uspto-database/SKILL.md https://app.decimal.ai/s/uspto-database/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/uspto-database/SKILL.md

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add uspto-database

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add uspto-database

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add uspto-database

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add uspto-database

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add uspto-database

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add uspto-database

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add uspto-database

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add uspto-database

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add uspto-database

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add uspto-database

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add uspto-database

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add uspto-database

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("uspto-database")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/uspto-database/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add uspto-database

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/uspto-database/SKILL.md https://app.decimal.ai/s/uspto-database/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/uspto-database/SKILL.md

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/uspto-database/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/uspto-database.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/30/2026
Source: FreedomIntelligence/OpenClaw-Medical-Skills ↗
License: Not recorded

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

USPTO Database

Overview

USPTO provides specialized APIs for patent and trademark data. Search patents by keywords/inventors/assignees, retrieve examination history via PEDS, track assignments, analyze citations and office actions, access TSDR for trademarks, for IP analysis and prior art searches.

When to Use This Skill

This skill should be used when:

Patent Search: Finding patents by keywords, inventors, assignees, classifications, or dates
Patent Details: Retrieving full patent data including claims, abstracts, citations
Trademark Search: Looking up trademarks by serial or registration number
Trademark Status: Checking trademark status, ownership, and prosecution history
Examination History: Accessing patent prosecution data from PEDS (Patent Examination Data System)
Office Actions: Retrieving office action text, citations, and rejections
Assignments: Tracking patent/trademark ownership transfers
Citations: Analyzing patent citations (forward and backward)
Litigation: Accessing patent litigation records
Portfolio Analysis: Analyzing patent/trademark portfolios for companies or inventors

USPTO API Ecosystem

The USPTO provides multiple specialized APIs for different data needs:

Core APIs

PatentSearch API - Modern ElasticSearch-based patent search (replaced legacy PatentsView in May 2025)
- Search patents by keywords, inventors, assignees, classifications, dates
- Access to patent data through June 30, 2025
- 45 requests/minute rate limit
- Base URL: https://search.patentsview.org/api/v1/
PEDS (Patent Examination Data System) - Patent examination history
- Application status and transaction history from 1981-present
- Office action dates and examination events
- Use uspto-opendata-python Python library
- Replaced: PAIR Bulk Data (PBD) - decommissioned
TSDR (Trademark Status & Document Retrieval) - Trademark data
- Trademark status, ownership, prosecution history
- Search by serial or registration number
- Base URL: https://tsdrapi.uspto.gov/ts/cd/

Additional APIs

Patent Assignment Search - Ownership records and transfers
Trademark Assignment Search - Trademark ownership changes
Enriched Citation API - Patent citation analysis
Office Action Text Retrieval - Full text of office actions
Office Action Citations - Citations from office actions
Office Action Rejection - Rejection reasons and types
PTAB API - Patent Trial and Appeal Board proceedings
Patent Litigation Cases - Federal district court litigation data
Cancer Moonshot Data Set - Cancer-related patents

Quick Start

API Key Registration

All USPTO APIs require an API key. Register at: https://account.uspto.gov/api-manager/

Set the API key as an environment variable:

export USPTO_API_KEY="your_api_key_here"

Helper Scripts

This skill includes Python scripts for common operations:

scripts/patent_search.py - PatentSearch API client for searching patents
scripts/peds_client.py - PEDS client for examination history
scripts/trademark_client.py - TSDR client for trademark data

Task 1: Searching Patents

Using the PatentSearch API

The PatentSearch API uses a JSON query language with various operators for flexible searching.

Basic Patent Search Examples

Search by keywords in abstract:

from scripts.patent_search import PatentSearchClient

client = PatentSearchClient()

# Search for machine learning patents
results = client.search_patents({
    "patent_abstract": {"_text_all": ["machine", "learning"]}
})

for patent in results['patents']:
    print(f"{patent['patent_number']}: {patent['patent_title']}")

Search by inventor:

results = client.search_by_inventor("John Smith")

Search by assignee/company:

results = client.search_by_assignee("Google")

Search by date range:

results = client.search_by_date_range("2024-01-01", "2024-12-31")

Search by CPC classification:

results = client.search_by_classification("H04N")  # Video/image tech

Advanced Patent Search

Combine multiple criteria with logical operators:

results = client.advanced_search(
    keywords=["artificial", "intelligence"],
    assignee="Microsoft",
    start_date="2023-01-01",
    end_date="2024-12-31",
    cpc_codes=["G06N", "G06F"]  # AI and computing classifications
)

Direct API Usage

For complex queries, use the API directly:

import requests

url = "https://search.patentsview.org/api/v1/patent"
headers = {
    "X-Api-Key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}

query = {
    "q": {
        "_and": [
            {"patent_date": {"_gte": "2024-01-01"}},
            {"assignee_organization": {"_text_any": ["Google", "Alphabet"]}},
            {"cpc_subclass_id": ["G06N", "H04N"]}
        ]
    },
    "f": ["patent_number", "patent_title", "patent_date", "inventor_name"],
    "s": [{"patent_date": "desc"}],
    "o": {"per_page": 100, "page": 1}
}

response = requests.post(url, headers=headers, json=query)
results = response.json()

Query Operators

Equality: {"field": "value"} or {"field": {"_eq": "value"}}
Comparison: _gt, _gte, _lt, _lte, _neq
Text search: _text_all, _text_any, _text_phrase
String matching: _begins, _contains
Logical: _and, _or, _not

Best Practice: Use _text_* operators for text fields (more performant than _contains or _begins)

Available Patent Endpoints

/patent - Granted patents
/publication - Pregrant publications
/inventor - Inventor information
/assignee - Assignee information
/cpc_subclass, /cpc_at_issue - CPC classifications
/uspc - US Patent Classification
/ipc - International Patent Classification
/claims, /brief_summary_text, /detail_description_text - Text data (beta)

Reference Documentation

See references/patentsearch_api.md for complete PatentSearch API documentation including:

All available endpoints
Complete field reference
Query syntax and examples
Response formats
Rate limits and best practices

Task 2: Retrieving Patent Examination Data

Using PEDS (Patent Examination Data System)

PEDS provides comprehensive prosecution history including transaction events, status changes, and examination timeline.

Installation

uv pip install uspto-opendata-python

Basic PEDS Usage

Get application data:

from scripts.peds_client import PEDSHelper

helper = PEDSHelper()

# By application number
app_data = helper.get_application("16123456")
print(f"Title: {app_data['title']}")
print(f"Status: {app_data['app_status']}")

# By patent number
patent_data = helper.get_patent("11234567")

Get transaction history:

transactions = helper.get_transaction_history("16123456")

for trans in transactions:
    print(f"{trans['date']}: {trans['code']} - {trans['description']}")

Get office actions:

office_actions = helper.get_office_actions("16123456")

for oa in office_actions:
    if oa['code'] == 'CTNF':
        print(f"Non-final rejection: {oa['date']}")
    elif oa['code'] == 'CTFR':
        print(f"Final rejection: {oa['date']}")
    elif oa['code'] == 'NOA':
        print(f"Notice of allowance: {oa['date']}")

Get status summary:

summary = helper.get_status_summary("16123456")

print(f"Current status: {summary['current_status']}")
print(f"Filing date: {summary['filing_date']}")
print(f"Pendency: {summary['pendency_days']} days")

if summary['is_patented']:
    print(f"Patent number: {summary['patent_number']}")
    print(f"Issue date: {summary['issue_date']}")

Prosecution Analysis

Analyze prosecution patterns:

analysis = helper.analyze_prosecution("16123456")

print(f"Total office actions: {analysis['total_office_actions']}")
print(f"Non-final rejections: {analysis['non_final_rejections']}")
print(f"Final rejections: {analysis['final_rejections']}")
print(f"Allowed: {analysis['allowance']}")
print(f"Responses filed: {analysis['responses']}")

Common Transaction Codes

CTNF - Non-final rejection mailed
CTFR - Final rejection mailed
NOA - Notice of allowance mailed
WRIT - Response filed
ISS.FEE - Issue fee payment
ABND - Application abandoned
AOPF - Office action mailed

Reference Documentation

See references/peds_api.md for complete PEDS documentation including:

All available data fields
Transaction code reference
Python library usage
Portfolio analysis examples

Task 3: Searching and Monitoring Trademarks

Using TSDR (Trademark Status & Document Retrieval)

Access trademark status, ownership, and prosecution history.

Basic Trademark Usage

Get trademark by serial number:

from scripts.trademark_client import TrademarkClient

client = TrademarkClient()

# By serial number
tm_data = client.get_trademark_by_serial("87654321")

# By registration number
tm_data = client.get_trademark_by_registration("5678901")

Get trademark status:

status = client.get_trademark_status("87654321")

print(f"Mark: {status['mark_text']}")
print(f"Status: {status['status']}")
print(f"Filing date: {status['filing_date']}")

if status['is_registered']:
    print(f"Registration #: {status['registration_number']}")
    print(f"Registration date: {status['registration_date']}")

Check trademark health:

health = client.check_trademark_health("87654321")

print(f"Mark: {health['mark']}")
print(f"Status: {health['status']}")

for alert in health['alerts']:
    print(alert)

if health['needs_attention']:
    print("⚠️  This mark needs attention!")

Trademark Portfolio Monitoring

Monitor multiple trademarks:

def monitor_portfolio(serial_numbers, api_key):
    """Monitor trademark portfolio health."""
    client = TrademarkClient(api_key)

    results = {
        'active': [],
        'pending': [],
        'problems': []
    }

    for sn in serial_numbers:
        health = client.check_trademark_health(sn)

        if 'REGISTERED' in health['status']:
            results['active'].append(health)
        elif 'PENDING' in health['status'] or 'PUBLISHED' in health['status']:
            results['pending'].append(health)
        elif health['needs_attention']:
            results['problems'].append(health)

    return results

Common Trademark Statuses

REGISTERED - Active registered mark
PENDING - Under examination
PUBLISHED FOR OPPOSITION - In opposition period
ABANDONED - Application abandoned
CANCELLED - Registration cancelled
SUSPENDED - Examination suspended
REGISTERED AND RENEWED - Registration renewed

Reference Documentation

See references/trademark_api.md for complete trademark API documentation including:

TSDR API reference
Trademark Assignment Search API
All status codes
Prosecution history access
Ownership tracking

Task 4: Tracking Assignments and Ownership

Patent and Trademark Assignments

Both patents and trademarks have Assignment Search APIs for tracking ownership changes.

Patent Assignment API

Base URL: https://assignment-api.uspto.gov/patent/v1.4/

Search by patent number:

import requests
import xml.etree.ElementTree as ET

def get_patent_assignments(patent_number, api_key):
    url = f"https://assignment-api.uspto.gov/patent/v1.4/assignment/patent/{patent_number}"
    headers = {"X-Api-Key": api_key}

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.text  # Returns XML

assignments_xml = get_patent_assignments("11234567", api_key)
root = ET.fromstring(assignments_xml)

for assignment in root.findall('.//assignment'):
    recorded_date = assignment.find('recordedDate').text
    assignor = assignment.find('.//assignor/name').text
    assignee = assignment.find('.//assignee/name').text
    conveyance = assignment.find('conveyanceText').text

    print(f"{recorded_date}: {assignor} → {assignee}")
    print(f"  Type: {conveyance}\n")

Search by company name:

def find_company_patents(company_name, api_key):
    url = "https://assignment-api.uspto.gov/patent/v1.4/assignment/search"
    headers = {"X-Api-Key": api_key}
    data = {"criteria": {"assigneeName": company_name}}

    response = requests.post(url, headers=headers, json=data)
    return response.text

Common Assignment Types

ASSIGNMENT OF ASSIGNORS INTEREST - Ownership transfer
SECURITY AGREEMENT - Collateral/security interest
MERGER - Corporate merger
CHANGE OF NAME - Name change
ASSIGNMENT OF PARTIAL INTEREST - Partial ownership

Task 5: Accessing Additional USPTO Data

Office Actions, Citations, and Litigation

Multiple specialized APIs provide additional patent data.

Office Action Text Retrieval

Retrieve full text of office actions using application number. Integrate with PEDS to identify which office actions exist, then retrieve full text.

Enriched Citation API

Analyze patent citations:

Forward citations (patents citing this patent)
Backward citations (prior art cited)
Examiner vs. applicant citations
Citation context

Patent Litigation Cases API

Access federal district court patent litigation records:

74,623+ litigation records
Patents asserted
Parties and venues
Case outcomes

PTAB API

Patent Trial and Appeal Board proceedings:

Inter partes review (IPR)
Post-grant review (PGR)
Appeal decisions

Reference Documentation

See references/additional_apis.md for comprehensive documentation on:

Enriched Citation API
Office Action APIs (Text, Citations, Rejections)
Patent Litigation Cases API
PTAB API
Cancer Moonshot Data Set
OCE Status/Event Codes

Complete Analysis Example

Comprehensive Patent Analysis

Combine multiple APIs for complete patent intelligence:

def comprehensive_patent_analysis(patent_number, api_key):
    """
    Full patent analysis using multiple USPTO APIs.
    """
    from scripts.patent_search import PatentSearchClient
    from scripts.peds_client import PEDSHelper

    results = {}

    # 1. Get patent details
    patent_client = PatentSearchClient(api_key)
    patent_data = patent_client.get_patent(patent_number)
    results['patent'] = patent_data

    # 2. Get examination history
    peds = PEDSHelper()
    results['prosecution'] = peds.analyze_prosecution(patent_number)
    results['status'] = peds.get_status_summary(patent_number)

    # 3. Get assignment history
    import requests
    assign_url = f"https://assignment-api.uspto.gov/patent/v1.4/assignment/patent/{patent_number}"
    assign_resp = requests.get(assign_url, headers={"X-Api-Key": api_key})
    results['assignments'] = assign_resp.text if assign_resp.status_code == 200 else None

    # 4. Analyze results
    print(f"\n=== Patent {patent_number} Analysis ===\n")
    print(f"Title: {patent_data['patent_title']}")
    print(f"Assignee: {', '.join(patent_data.get('assignee_organization', []))}")
    print(f"Issue Date: {patent_data['patent_date']}")

    print(f"\nProsecution:")
    print(f"  Office Actions: {results['prosecution']['total_office_actions']}")
    print(f"  Rejections: {results['prosecution']['non_final_rejections']} non-final, {results['prosecution']['final_rejections']} final")
    print(f"  Pendency: {results['prosecution']['pendency_days']} days")

    # Analyze citations
    if 'cited_patent_number' in patent_data:
        print(f"\nCitations:")
        print(f"  Cites: {len(patent_data['cited_patent_number'])} patents")
    if 'citedby_patent_number' in patent_data:
        print(f"  Cited by: {len(patent_data['citedby_patent_number'])} patents")

    return results

Best Practices

API Key Management
- Store API key in environment variables
- Never commit keys to version control
- Use same key across all USPTO APIs
Rate Limiting
- PatentSearch: 45 requests/minute
- Implement exponential backoff for rate limit errors
- Cache responses when possible
Query Optimization
- Use _text_* operators for text fields (more performant)
- Request only needed fields to reduce response size
- Use date ranges to narrow searches
Data Handling
- Not all fields populated for all patents/trademarks
- Handle missing data gracefully
- Parse dates consistently
Combining APIs
- Use PatentSearch for discovery
- Use PEDS for prosecution details
- Use Assignment APIs for ownership tracking
- Combine data for comprehensive analysis

Important Notes

Legacy API Sunset: PatentsView legacy API discontinued May 1, 2025 - use PatentSearch API
PAIR Bulk Data Decommissioned: Use PEDS instead
Data Coverage: PatentSearch has data through June 30, 2025; PEDS from 1981-present
Text Endpoints: Claims and description endpoints are in beta with ongoing backfilling
Rate Limits: Respect rate limits to avoid service disruptions

Resources

API Documentation

PatentSearch API: https://search.patentsview.org/docs/
USPTO Developer Portal: https://developer.uspto.gov/
USPTO Open Data Portal: https://data.uspto.gov/
API Key Registration: https://account.uspto.gov/api-manager/

Python Libraries

uspto-opendata-python: https://pypi.org/project/uspto-opendata-python/
USPTO Docs: https://docs.ip-tools.org/uspto-opendata-python/

Reference Files

references/patentsearch_api.md - Complete PatentSearch API reference
references/peds_api.md - PEDS API and library documentation
references/trademark_api.md - Trademark APIs (TSDR and Assignment)
references/additional_apis.md - Citations, Office Actions, Litigation, PTAB

Scripts

scripts/patent_search.py - PatentSearch API client
scripts/peds_client.py - PEDS examination data client
scripts/trademark_client.py - Trademark search client

About this skill

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skills/uspto-database/SKILL.md

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Imported 8/1/2026Version v1Bundled 7 files · scripts

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USPTO Database

Overview

When to Use This Skill

USPTO API Ecosystem

Core APIs

Additional APIs

Quick Start

API Key Registration

Helper Scripts

Task 1: Searching Patents

Using the PatentSearch API

Basic Patent Search Examples

Advanced Patent Search

Direct API Usage

Query Operators

Available Patent Endpoints

Reference Documentation

Task 2: Retrieving Patent Examination Data

Using PEDS (Patent Examination Data System)

Installation

Basic PEDS Usage

Prosecution Analysis

Common Transaction Codes

Reference Documentation

Task 3: Searching and Monitoring Trademarks

Using TSDR (Trademark Status & Document Retrieval)

Basic Trademark Usage

Trademark Portfolio Monitoring

Common Trademark Statuses

Reference Documentation

Task 4: Tracking Assignments and Ownership

Patent and Trademark Assignments

Patent Assignment API

Common Assignment Types

Task 5: Accessing Additional USPTO Data

Office Actions, Citations, and Litigation

Office Action Text Retrieval

Enriched Citation API

Patent Litigation Cases API

PTAB API

Reference Documentation

Complete Analysis Example

Comprehensive Patent Analysis

Best Practices

Important Notes

Resources

API Documentation

Python Libraries

Reference Files

Scripts

SkillSafety

Source

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