ensemble-solving: Design & UI for AI Agents | OpenAgentSkill

Overview

Generate multiple diverse solutions in parallel and select the best. Use for architecture decisions, code generation with multiple valid approaches, or creative tasks where exploring alternatives improves quality.

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Ensemble Problem Solving

Generate multiple solutions in parallel by spawning 3 subagents with different approaches, then evaluate and select the best result.

When to Use

Activation phrases:

"Give me options for..."
"What's the best way to..."
"Explore different approaches..."
"I want to see alternatives..."
"Compare approaches for..."
"Which approach should I use..."

Good candidates:

Architecture decisions with trade-offs
Code generation with multiple valid implementations
API design with different philosophies
Naming, branding, documentation style
Refactoring strategies
Algorithm selection

Skip ensemble for:

Simple lookups or syntax questions
Single-cause bug fixes
File operations, git commands
Deterministic configuration changes
Tasks with one obvious solution

What It Does

Analyzes the task to determine if ensemble approach is valuable
Generates 3 distinct prompts using appropriate diversification strategy
Spawns 3 parallel subagents to develop solutions independently
Evaluates all solutions using weighted criteria
Returns the best solution with explanation and alternatives summary

Approach

Step 1: Classify Task Type

Determine which category fits:

Code Generation: Functions, classes, APIs, algorithms
Architecture/Design: System design, data models, patterns
Creative: Writing, naming, documentation

Step 2: Invoke Ensemble Orchestrator

Task tool with:
- subagent_type: 'ensemble-orchestrator'
- description: 'Generate and evaluate 3 parallel solutions'
- prompt: [User's original task with full context]

The orchestrator handles:

Prompt diversification
Parallel execution
Solution evaluation
Winner selection

Step 3: Present Result

The orchestrator returns:

The winning solution (in full)
Evaluation scores for all 3 approaches
Why the winner was selected
When alternatives might be preferred

Diversification Strategies

For Code (Constraint Variation):

Approach	Focus
Simplicity	Minimal code, maximum readability
Performance	Efficient, optimized
Extensibility	Clean abstractions, easy to extend

For Architecture (Approach Variation):

Approach	Focus
Top-down	Requirements → Interfaces → Implementation
Bottom-up	Primitives → Composition → Structure
Lateral	Analogies from other domains

For Creative (Persona Variation):

Approach	Focus
Expert	Technical precision, authoritative
Pragmatic	Ship-focused, practical
Innovative	Creative, unconventional

Evaluation Rubric

Criterion	Base Weight	Description
Correctness	30%	Solves the problem correctly
Completeness	20%	Addresses all requirements
Quality	20%	How well-crafted
Clarity	15%	How understandable
Elegance	15%	How simple/beautiful

Weights adjust based on task type.

Example

User: "What's the best way to implement a rate limiter?"

Skill:

Classifies as Code Generation
Invokes ensemble-orchestrator
Three approaches generated:
- Simple: Token bucket with in-memory counter
- Performance: Sliding window with atomic operations
- Extensible: Strategy pattern with pluggable backends
Evaluation selects extensible approach (score 8.4)
Returns full implementation with explanation

Output:

## Selected Solution

[Full rate limiter implementation with strategy pattern]

## Why This Solution Won

The extensible approach scored highest (8.4) because it provides
a clean abstraction that works for both simple use cases and
complex distributed scenarios. The strategy pattern allows
swapping Redis/Memcached backends without code changes.

## Alternatives

- **Simple approach**: Best if you just need basic in-memory
  limiting and will never scale beyond one process.

- **Performance approach**: Best for high-throughput scenarios
  where every microsecond matters.

Success Criteria

3 genuinely different solutions generated
Clear evaluation rationale provided
Winner selected with confidence
Alternatives summarized with use cases
User understands trade-offs

Token Cost

~4x overhead vs single attempt. Worth it for:

High-stakes architecture decisions
Creative work where first attempt rarely optimal
Learning scenarios where seeing alternatives is valuable
Code that will be maintained long-term

Integration

feature-planning: Can ensemble architecture decisions
code-auditor: Can ensemble analysis perspectives
plan-implementer: Executes the winning approach

View source ↗

File metadata

name: ensemble-solving
description: Generate multiple diverse solutions in parallel and select the best. Use for architecture decisions, code generation with multiple valid approaches, or creative tasks where exploring alternatives improves quality.

View original text

---
name: ensemble-solving
description: Generate multiple diverse solutions in parallel and select the best. Use for architecture decisions, code generation with multiple valid approaches, or creative tasks where exploring alternatives improves quality.
---

# Ensemble Problem Solving

Generate multiple solutions in parallel by spawning 3 subagents with different approaches, then evaluate and select the best result.

## When to Use

**Activation phrases:**
- "Give me options for..."
- "What's the best way to..."
- "Explore different approaches..."
- "I want to see alternatives..."
- "Compare approaches for..."
- "Which approach should I use..."

**Good candidates:**
- Architecture decisions with trade-offs
- Code generation with multiple valid implementations
- API design with different philosophies
- Naming, branding, documentation style
- Refactoring strategies
- Algorithm selection

**Skip ensemble for:**
- Simple lookups or syntax questions
- Single-cause bug fixes
- File operations, git commands
- Deterministic configuration changes
- Tasks with one obvious solution

## What It Does

1. **Analyzes the task** to determine if ensemble approach is valuable
2. **Generates 3 distinct prompts** using appropriate diversification strategy
3. **Spawns 3 parallel subagents** to develop solutions independently
4. **Evaluates all solutions** using weighted criteria
5. **Returns the best solution** with explanation and alternatives summary

## Approach

### Step 1: Classify Task Type

Determine which category fits:
- **Code Generation**: Functions, classes, APIs, algorithms
- **Architecture/Design**: System design, data models, patterns
- **Creative**: Writing, naming, documentation

### Step 2: Invoke Ensemble Orchestrator

```
Task tool with:
- subagent_type: 'ensemble-orchestrator'
- description: 'Generate and evaluate 3 parallel solutions'
- prompt: [User's original task with full context]
```

The orchestrator handles:
- Prompt diversification
- Parallel execution
- Solution evaluation
- Winner selection

### Step 3: Present Result

The orchestrator returns:
- The winning solution (in full)
- Evaluation scores for all 3 approaches
- Why the winner was selected
- When alternatives might be preferred

## Diversification Strategies

**For Code (Constraint Variation):**
| Approach | Focus |
|----------|-------|
| Simplicity | Minimal code, maximum readability |
| Performance | Efficient, optimized |
| Extensibility | Clean abstractions, easy to extend |

**For Architecture (Approach Variation):**
| Approach | Focus |
|----------|-------|
| Top-down | Requirements → Interfaces → Implementation |
| Bottom-up | Primitives → Composition → Structure |
| Lateral | Analogies from other domains |

**For Creative (Persona Variation):**
| Approach | Focus |
|----------|-------|
| Expert | Technical precision, authoritative |
| Pragmatic | Ship-focused, practical |
| Innovative | Creative, unconventional |

## Evaluation Rubric

| Criterion | Base Weight | Description |
|-----------|-------------|-------------|
| Correctness | 30% | Solves the problem correctly |
| Completeness | 20% | Addresses all requirements |
| Quality | 20% | How well-crafted |
| Clarity | 15% | How understandable |
| Elegance | 15% | How simple/beautiful |

Weights adjust based on task type.

## Example

**User:** "What's the best way to implement a rate limiter?"

**Skill:**
1. Classifies as Code Generation
2. Invokes ensemble-orchestrator
3. Three approaches generated:
   - Simple: Token bucket with in-memory counter
   - Performance: Sliding window with atomic operations
   - Extensible: Strategy pattern with pluggable backends
4. Evaluation selects extensible approach (score 8.4)
5. Returns full implementation with explanation

**Output:**
```
## Selected Solution

[Full rate limiter implementation with strategy pattern]

## Why This Solution Won

The extensible approach scored highest (8.4) because it provides
a clean abstraction that works for both simple use cases and
complex distributed scenarios. The strategy pattern allows
swapping Redis/Memcached backends without code changes.

## Alternatives

- **Simple approach**: Best if you just need basic in-memory
  limiting and will never scale beyond one process.

- **Performance approach**: Best for high-throughput scenarios
  where every microsecond matters.
```

## Success Criteria

- 3 genuinely different solutions generated
- Clear evaluation rationale provided
- Winner selected with confidence
- Alternatives summarized with use cases
- User understands trade-offs

## Token Cost

~4x overhead vs single attempt. Worth it for:
- High-stakes architecture decisions
- Creative work where first attempt rarely optimal
- Learning scenarios where seeing alternatives is valuable
- Code that will be maintained long-term

## Integration

- **feature-planning**: Can ensemble architecture decisions
- **code-auditor**: Can ensemble analysis perspectives
- **plan-implementer**: Executes the winning approach

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: Apache-2.0

Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: secrets or environment access, filesystem or document access
Permission surface: secrets or environment access, filesystem or document access

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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: mhattingpete/claude-skills-marketplace
License: Apache-2.0
Version: 1.0.0
Last GitHub push: Jul 25, 2026
Registry updated: Sep 3, 2026
Instruction path: engineering-workflow-plugin/skills/ensemble-solving/SKILL.md @ b5b34bcf4c92

Version reported in registry metadata; check source releases before relying on it.

Quality

69/100

Promising

Trust

69/100

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This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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}

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Source: mhattingpete/claude-skills-marketplace
Indexed by: OpenAgentSkill community index

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