scamp-analyzer 2025.12.21135930

A new package that analyzes user-provided text descriptions of mischievous or cleverly unconventional behavior (a 'scamp' scenario) and returns a structured assessment. It evaluates the creativity, hu

scamp-analyzer

scamp-analyzer is a lightweight Python package that evaluates user‑provided text descriptions of mischievous or cleverly unconventional behavior (a “scamp” scenario). It returns a structured XML‑like assessment of the act’s creativity, humor, and potential social impact, categorizing it as harmless fun, borderline, or potentially problematic.

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Installation

pip install scamp_analyzer

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Quick Start

from scamp_analyzer import scamp_analyzer

# Simple usage with the default LLM (ChatLLM7)
result = scamp_analyzer(
    user_input="I swapped the sugar with salt in the office kitchen."
)

print(result)   # → List of extracted XML‑like tags

Parameters

Name	Type	Description
user_input	str	The text description of the scamp scenario to be analysed.
llm	Optional[BaseChatModel]	A LangChain chat model. If omitted, the built‑in ChatLLM7 is used.
api_key	Optional[str]	API key for LLM7. If omitted, the function reads LLM7_API_KEY from the environment (or uses a placeholder).

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Using a Custom LLM

You can provide any LangChain chat model that implements BaseChatModel. Below are a few examples.

OpenAI

from langchain_openai import ChatOpenAI
from scamp_analyzer import scamp_analyzer

llm = ChatOpenAI()
response = scamp_analyzer(
    user_input="I printed the boss's email signature on a birthday cake.",
    llm=llm
)

Anthropic

from langchain_anthropic import ChatAnthropic
from scamp_analyzer import scamp_analyzer

llm = ChatAnthropic()
response = scamp_analyzer(
    user_input="I replaced the office chairs with beanbags for a surprise.",
    llm=llm
)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from scamp_analyzer import scamp_analyzer

llm = ChatGoogleGenerativeAI()
response = scamp_analyzer(
    user_input="I swapped the ‘Out of Office’ replies with a funny poem.",
    llm=llm
)

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Default LLM (ChatLLM7)

If you don’t supply a custom llm, scamp_analyzer automatically creates a ChatLLM7 instance:

from scamp_analyzer import scamp_analyzer

response = scamp_analyzer(
    user_input="I anonymously left motivational sticky notes around the office."
)

• ChatLLM7 is provided by the langchain_llm7 package: https://pypi.org/project/langchain-llm7/
• The free tier’s rate limits are sufficient for typical usage.

Providing Your Own LLM7 API Key

You can either set the environment variable:

export LLM7_API_KEY="your_llm7_api_key"

or pass it directly:

response = scamp_analyzer(
    user_input="I organized a surprise flash mob at lunch.",
    api_key="your_llm7_api_key"
)

Obtain a free API key by registering at https://token.llm7.io/.

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Contributing & Issues

If you encounter any problems or have feature requests, please open an issue:

👉 https://github....

We welcome contributions, documentation improvements, and bug fixes.

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Author

Eugene Evstafev
📧 [e-mail]
🐙 GitHub: chigwell

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License

This project is licensed under the MIT License – see the LICENSE file for details.