The Reasoner skill empowers Claude with advanced non-linear reasoning capabilities using sophisticated search algorithms like Beam Search and Monte Carlo Tree Search (MCTS). It is specifically engineered for complex problem-solving, optimization, and architectural decision-making where exploring multiple solution paths and ranking them by quality is essential for finding the best outcome. By moving beyond simple sequential thinking, it provides a structured framework for high-stakes logic and deep analysis.

When should I use reasoner?

reasoner is useful in the following scenarios: • System Architecture Design: Evaluating and scoring different technical stacks or architectural patterns (e.g., Microservices vs. Monolith) to find the optimal balance between scalability and complexity. • Complex Algorithmic Optimization: Utilizing MCTS to explore various optimization paths for resource allocation, logistics, or performance tuning in software development. • Strategic Decision Trees: Mapping out and ranking potential outcomes for business or technical strategies, allowing for data-driven exploration of 'what-if' scenarios with scored reasoning paths. • Advanced Technical Troubleshooting: Systematically branching through multiple hypotheses to diagnose root causes in complex, multi-layered distributed systems where linear debugging fails.

name	reasoner
description	>
WHEN	Complex problem-solving requiring exploration of multiple solution paths, optimization problems, decision trees, when you need scored/ranked reasoning paths.
WHEN NOT	Simple linear reasoning (use sequential_thinking), trivial problems, when branching isn't needed.
version	0.1.0

Reasoner - Advanced Multi-Strategy Reasoning

Core Concept

mcp__plugin_kg_kodegen__reasoner provides sophisticated reasoning with multiple search strategies. Unlike sequential_thinking (simple linear tracking), reasoner uses algorithms like Beam Search and Monte Carlo Tree Search (MCTS) to explore and score multiple solution paths, finding optimal reasoning chains.

Strategies

Strategy	Best For	Description
`beam_search`	General problems	Maintains top N paths simultaneously
`mcts`	Decision trees	UCB1/PUCT exploration-exploitation
`mcts_002_alpha`	Creative solutions	10% higher exploration bonus
`mcts_002alt_alpha`	Detailed analysis	Rewards longer reasoning paths

Key Parameters

Required:

Parameter	Type	Description
`thought`	string	Current reasoning step
`thought_number`	number	Current step (1-based)
`total_thoughts`	number	Estimated total needed
`next_thought_needed`	boolean	Whether more steps needed

Optional:

Parameter	Type	Description
`strategy_type`	string	beam_search (default), mcts, mcts_002_alpha, mcts_002alt_alpha
`beam_width`	number	Paths to maintain (1-10, default: 3)
`num_simulations`	number	MCTS rollouts (1-150, default: 50)
`parent_id`	string	Parent node for branching

Usage Examples

Beam Search (Default)

{
  "thought": "Analyzing possible caching strategies for the API",
  "thought_number": 1,
  "total_thoughts": 4,
  "next_thought_needed": true,
  "strategy_type": "beam_search",
  "beam_width": 3
}

MCTS for Decision Making

{
  "thought": "Evaluating database migration approaches",
  "thought_number": 1,
  "total_thoughts": 3,
  "next_thought_needed": true,
  "strategy_type": "mcts",
  "num_simulations": 100
}

Creative Problem Solving

{
  "thought": "Exploring novel approaches to distributed consensus",
  "thought_number": 1,
  "total_thoughts": 5,
  "next_thought_needed": true,
  "strategy_type": "mcts_002_alpha",
  "num_simulations": 75
}

Detailed Analysis

{
  "thought": "Deep comparison of microservices vs monolithic architecture",
  "thought_number": 1,
  "total_thoughts": 6,
  "next_thought_needed": true,
  "strategy_type": "mcts_002alt_alpha",
  "num_simulations": 50
}

Branching from Parent

{
  "thought": "Alternative approach using event sourcing",
  "thought_number": 3,
  "total_thoughts": 5,
  "next_thought_needed": true,
  "parent_id": "previous-node-uuid"
}

Output Format

{
  "session_id": "uuid-v4",
  "thought": "echoed input",
  "score": 0.85,
  "depth": 2,
  "is_complete": false,
  "next_thought_needed": true,
  "branches": 3,
  "best_path_score": 0.92,
  "strategy": "beam_search",
  "history_length": 5
}

When to Use What

Problem Type	Tool	Why
Simple step-by-step	sequential_thinking	No scoring needed
Optimization	reasoner (mcts)	Finds optimal path
Multiple alternatives	reasoner (beam_search)	Tracks top N paths
Creative exploration	reasoner (mcts_002_alpha)	Higher exploration
Detailed analysis	reasoner (mcts_002alt_alpha)	Rewards depth

Reasoner vs Sequential Thinking

Feature	Sequential Thinking	Reasoner
Path scoring	No	Yes (0.0-1.0)
Strategy selection	No	beam_search, MCTS variants
Semantic analysis	No	Yes (Stella 400M embeddings)
Best path tracking	No	Yes (best_path_score)
Complexity	Lower	Higher
Use case	Linear reasoning	Optimization/exploration

Remember

Choose strategy wisely - beam_search for general, MCTS for optimization
Adjust beam_width - higher = more paths but slower
num_simulations - more = better MCTS results but slower
Check scores - output includes quality scores (0.0-1.0)
Use for complex problems - overkill for simple reasoning
Prefer sequential_thinking for straightforward step-by-step

reasoner

When & Why to Use This Skill

Use Cases