lcb-hard

Inject structured reasoning scaffolds into AI problem-solving workflows for complex programming tasks.

4|Updated Mar 22, 2026

Search Tags:#reasoning #programming #structured-prompt #prompt-injection #CognitiveRouting #AICalibration

Ready to run

npx skills add https://github.com/ejentum/benchmarks --skill lcb-hard

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lcb-hard
Source: https://github.com/ejentum/benchmarks/tree/main/lcb-hard
Command: npx skills add https://github.com/ejentum/benchmarks --skill lcb-hard

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enhances AI performance in solving complex programming tasks by injecting structured reasoning patterns that prevent premature convergence and reasoning spirals.

Core Features & Use Cases

Structured Reasoning Injection: Incorporates detailed, domain-specific reasoning prompts to improve problem-solving accuracy.
Cognitive Routing: Selects and routes queries based on problem type, difficulty, and context, optimizing AI responses.
Use Case: When tackling a difficult coding problem, the Skill prompts the AI to analyze potential failure points, decide the best reasoning mode, and incorporate reasoning scaffolds before code generation, leading to higher correctness rates.

Quick Start

Use the lcb-hard skill to guide the AI in solving complex programming problems with injected reasoning scaffolds and structured prompts.

Frequently Asked Questions about lcb-hard

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I prevent AI from premature convergence when solving complex programming problems?▼

To prevent premature convergence in complex programming problems, inject structured reasoning patterns that guide the AI through failure analysis and context absorption before code generation.

What is cognitive routing for AI problem-solving workflows?▼

Cognitive routing in AI problem-solving selects and routes queries based on problem type, difficulty, and context, optimizing AI responses by applying the correct reasoning mode.

How do I use structured prompts to improve AI coding accuracy on difficult tasks?▼

Use structured prompts to prompt the AI to analyze potential failure points and incorporate reasoning scaffolds before code generation, leading to higher correctness rates on difficult coding tasks.

Does AI calibration work for competitive programming and benchmark-driven reasoning?▼

AI calibration works for competitive programming by injecting domain-specific reasoning structures and benchmark-driven scaffolds that prevent reasoning spirals and improve problem-solving accuracy.

What are the limitations of structured prompt injection for AI code generation?▼

Structured prompt injection for AI code generation is limited if the underlying model lacks sufficient context absorption, requiring systematic reasoning scaffolds to avoid reasoning spirals.