Structured Response Python Packages

llm-securescan

A new package is designed to facilitate secure and structured user interactions with language models by analyzing and extracting specific information from user-provided text. It uses pattern matching

memoryinvestguide

A new package designed to help users navigate the volatile memory market by providing structured, long-term investment strategies. Users input their current financial situation, risk tolerance, and in

csrf-sentry

Helps developers implement tailored CSRF protection strategies without tokens, ensuring robust security and easy integration.

cpufreqizer

cpufreqizer recommends optimal CPU scaling governors and kernel params based on workload, balancing power and performance.

forgejo-bot-guard

This package helps administrators of Forgejo (or Git hosting platforms) generate clear, actionable anti-crawling measures by analyzing user-provided text inputs. When given a description of suspicious

websumm-agent

Efficient web information retrieval and summation without excessive token usage.

inboxpattern

A new package that helps users and organizations analyze and categorize email account usage patterns. The package takes user-submitted text input describing their email management habits and returns a

sqlite-column-sentry

A new package that helps developers ensure column safety in SQLite queries by analyzing and validating their SQL statements. The package takes a user's SQL query as text input and returns a structured

toon-troubleshooter

Toon-troubleshooter identifies and solves Cartoon Network viewing issues via user input and structured responses.

autourgos-google-modelkit

Google Gemini text and vision model wrappers for the Autourgos framework.