When should I use the Beam Search strategy?

Use Beam Search for general problems where you want the AI to maintain and compare several (usually 3-10) of the most promising solution paths simultaneously.

How does Reasoner differ from Sequential Thinking?

While Sequential Thinking handles simple linear steps, Reasoner uses scoring, ranking, and non-linear search strategies like MCTS to find the best possible path among many alternatives.

What are the specialized MCTS variants for?

The mcts_002_alpha variant provides higher exploration bonuses for creative solutions, while mcts_002alt_alpha rewards longer reasoning paths for deeper, more detailed analysis.

Can I branch off an existing thought process?

Yes, by utilizing the parent_id parameter, you can instruct the reasoner to branch from a specific previous node to explore an alternative direction.

Advanced AI Reasoner

Name: Advanced AI Reasoner
Author: cyrup-ai

bycyrup-ai

Data Science & ML

Enhances Claude's problem-solving capabilities using advanced search strategies like Beam Search and Monte Carlo Tree Search.

About

The Reasoner skill empowers Claude to tackle complex, multi-faceted problems that require more than simple linear thinking. By utilizing sophisticated search algorithms—including Beam Search for maintaining multiple high-quality paths and MCTS for exploring deep decision trees—it identifies optimal solutions through scoring and ranking. This tool is essential for architectural decision-making, creative exploration, and high-stakes optimization where finding the single best reasoning path is critical to the final output.

Key Features

Exploration-exploitation balancing with UCB1/PUCT algorithms
Branching capabilities to explore alternative solutions from specific nodes
0 GitHub stars
Path scoring and ranking to identify optimal reasoning chains
Semantic analysis and path validation for high-quality logic steps
Multi-strategy search including Beam Search and Monte Carlo Tree Search (MCTS)

Use Cases

Evaluating complex architectural trade-offs like microservices vs. monoliths
Creative problem-solving where non-linear exploration of multiple paths is required
Optimizing distributed systems and consensus algorithms

About

Key Features

Exploration-exploitation balancing with UCB1/PUCT algorithms
Branching capabilities to explore alternative solutions from specific nodes
0 GitHub stars
Path scoring and ranking to identify optimal reasoning chains
Semantic analysis and path validation for high-quality logic steps
Multi-strategy search including Beam Search and Monte Carlo Tree Search (MCTS)

Use Cases

Evaluating complex architectural trade-offs like microservices vs. monoliths
Creative problem-solving where non-linear exploration of multiple paths is required
Optimizing distributed systems and consensus algorithms