Can I manage token limits with this skill?

Yes, it provides a ContextWindowManager that supports sliding window logic and smart truncation to ensure conversations stay within token limits while preserving the most relevant history.

What is Chain-of-Thought prompting in this context?

It provides a standardized pattern to force the LLM to output its reasoning steps before providing a final answer, which is proven to increase accuracy for complex logic tasks.

Does it support structured data validation?

Absolutely. It utilizes Pydantic to ensure that prompt templates are correctly filled and validated before being sent to the model.

How does this skill help with prompt injection?

It includes a dedicated sanitizer that detects dangerous instruction-override patterns and wraps user input in XML-style tags to maintain clear boundaries between system instructions and user data.

Prompting Patterns

Name: Prompting Patterns
Author: ricardoroche

byricardoroche

Ciencia de Datos y ML

Implements standardized prompt engineering patterns to ensure reliable, secure, and high-performance LLM interactions.

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Prompting Patterns is a specialized skill designed for developers and AI engineers building LLM-powered applications. It provides a library of implementation patterns for structured templates, few-shot learning, and Chain-of-Thought reasoning to significantly improve model output quality. The skill also addresses critical production concerns by offering robust prompt injection prevention mechanisms and sophisticated context window management strategies, ensuring your AI agents remain secure and context-aware even during complex, long-running interactions.

Características Principales

0 GitHub stars
Structured prompt templates with Pydantic validation and type safety
Automated few-shot learning builders for consistent example formatting
Pre-configured Chain-of-Thought patterns to improve reasoning capabilities
Advanced prompt injection sanitization using pattern matching and XML-style wrapping
Context window management including sliding windows and smart truncation

Casos de Uso

Building robust RAG applications with complex document context requirements
Developing secure AI agents that resist user-level instruction overrides
Standardizing prompt design and versioning across large engineering teams

Acerca de

Características Principales

0 GitHub stars
Structured prompt templates with Pydantic validation and type safety
Automated few-shot learning builders for consistent example formatting
Pre-configured Chain-of-Thought patterns to improve reasoning capabilities
Advanced prompt injection sanitization using pattern matching and XML-style wrapping
Context window management including sliding windows and smart truncation

Casos de Uso

Building robust RAG applications with complex document context requirements
Developing secure AI agents that resist user-level instruction overrides
Standardizing prompt design and versioning across large engineering teams