ReddRadar
Agent skill
reasoner
Advanced reasoning with search strategies (beam search, MCTS). 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.
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Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/reasoner
SKILL.md
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)
json
{
"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
json
{
"thought": "Evaluating database migration approaches",
"thought_number": 1,
"total_thoughts": 3,
"next_thought_needed": true,
"strategy_type": "mcts",
"num_simulations": 100
}
Creative Problem Solving
json
{
"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
json
{
"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
json
{
"thought": "Alternative approach using event sourcing",
"thought_number": 3,
"total_thoughts": 5,
"next_thought_needed": true,
"parent_id": "previous-node-uuid"
}
Output Format
json
{
"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
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