infinite-agents-public/infinite_variants/infinite_variant_1

Cross-Iteration Pattern Synthesis System

Infinite Loop Variant #1: Learning from examples across peer iterations

Overview

This variant enhances the infinite loop with cross-iteration pattern synthesis - a cumulative learning system inspired by multi-shot prompting that enables agents to learn from successful patterns discovered in previous iterations.

Unlike the base infinite loop (which generates diverse iterations independently) or the web-enhanced loop (which learns from external URLs), this variant creates a feedback loop where each wave of iterations improves the next wave by extracting and reusing successful patterns as multi-shot examples.

Core Innovation: Pattern Library as Multi-Shot Prompting

The Problem

Traditional infinite loops generate iterations independently. Each iteration reinvents the wheel, leading to:

• Inconsistent quality across iterations
• Repeated mistakes and antipatterns
• No cumulative learning from peer iterations
• Difficulty maintaining a consistent "house style"

The Solution

After each wave of generation, the system:

1. Extracts Patterns: Analyzes all iterations to identify exemplary approaches (top 20%)
2. Builds Pattern Library: Stores 3-5 best examples per category (structural, content, innovation, quality)
3. Multi-Shot Context: Provides pattern library to subsequent waves as concrete examples
4. Continuous Refinement: Updates library after each wave, improving quality bar progressively

Why This Works (Multi-Shot Prompting Research)

Based on Anthropic's multi-shot prompting documentation, this system applies proven techniques:

1. Example-Based Consistency

"Examples help Claude reduce misinterpretation of instructions"

The pattern library provides concrete examples (not just descriptions) of successful approaches, reducing ambiguity and improving consistency.

2. Optimal Example Count

"Provide 3-5 diverse, relevant examples to improve performance"

Each pattern category maintains exactly 3-5 examples - enough diversity to prevent overfitting, few enough to avoid context bloat.

3. Structural Uniformity

"Use examples to enforce uniform structure and style"

Patterns demonstrate consistent organization, documentation, and code structure, creating a recognizable "house style" while preserving creativity.

4. Edge Case Coverage

"Cover edge cases and potential challenges"

The innovation and quality pattern categories explicitly capture unusual but effective approaches, teaching agents to handle edge cases gracefully.

5. Progressive Performance

"More examples = better performance, especially for complex tasks"

As the pattern library grows across waves, each iteration benefits from an expanding knowledge base of proven techniques.

Architecture

Command System

/project:infinite-synthesis - Main Orchestrator

The primary command that generates iterations with pattern-guided learning.

Usage:

/project:infinite-synthesis <spec_file> <output_dir> <count|infinite> [pattern_library_path]

Examples:

# Generate 5 iterations with pattern synthesis
/project:infinite-synthesis specs/example_spec.md output 5

# Continuous generation with pattern accumulation
/project:infinite-synthesis specs/example_spec.md output infinite

# Use custom pattern library location
/project:infinite-synthesis specs/example_spec.md output 10 pattern_library/custom.json

Workflow:

1. Wave 1 (Cold Start): Generate 5 iterations without pattern library
2. Pattern Extraction: Analyze Wave 1 to build initial pattern library
3. Wave 2 (Pattern-Guided): Generate 5 iterations using pattern library as examples
4. Continuous Learning: Extract patterns from all iterations, refine library, repeat
5. Quality Improvement: Each wave raises the bar for subsequent waves

/project:extract-patterns - Pattern Extraction

Analyzes iterations to extract successful patterns for the library.

Usage:

/project:extract-patterns <iterations_dir> <pattern_library_path> [analysis_depth]

Examples:

# Extract patterns from output directory
/project:extract-patterns output pattern_library/patterns.json

# Quick extraction (3 patterns per category)
/project:extract-patterns output pattern_library/patterns.json quick

# Deep analysis (5 patterns per category)
/project:extract-patterns output pattern_library/patterns.json deep

What It Extracts:

• Structural Patterns: Architecture, organization, naming conventions
• Content Patterns: Documentation, clarity, readability approaches
• Innovation Patterns: Creative solutions, novel techniques
• Quality Patterns: Error handling, validation, robustness

/project:analyze-patterns - Effectiveness Analysis

Measures how well the pattern library improves iteration quality.

Usage:

/project:analyze-patterns <pattern_library_path> <iterations_dir>

Metrics:

• Pattern adoption rate (% of iterations using patterns)
• Quality improvement (pre-pattern vs post-pattern scores)
• Pattern effectiveness (which patterns have highest adoption)
• Innovation impact (does library increase or decrease creativity?)

Pattern Library Structure

The pattern library is a JSON file with this structure:

{
  "version": "1.2",
  "last_updated": "2025-10-10T14:30:00Z",
  "total_iterations_analyzed": 15,
  "patterns": {
    "structural": [
      {
        "name": "Modular Three-Layer Architecture",
        "description": "Separates data, logic, and presentation",
        "example_file": "output/iteration_7.html",
        "key_characteristics": [
          "Clear section boundaries",
          "Data defined separately from rendering",
          "Reusable component structure"
        ],
        "success_metrics": "High readability (9/10), easy to extend",
        "code_snippet": "const data = {...};\nconst view = {...};\nconst controller = {...};"
      }
    ],
    "content": [...],
    "innovation": [...],
    "quality": [...]
  },
  "metadata": {
    "extraction_date": "2025-10-10T14:30:00Z",
    "source_directory": "output/",
    "patterns_extracted": 12,
    "avg_quality_score": 8.4
  }
}

See pattern_library_template.json for complete structure and documentation.

Validation Tools

Pattern Library Validator

Script to validate pattern library JSON structure and quality:

./validators/check_patterns.sh pattern_library/patterns.json

Checks:

• Valid JSON syntax
• Required fields present
• Pattern object structure
• Pattern count (3-5 per category)
• Code snippet coverage
• Success metrics completeness

Quick Start

1. Install Dependencies

# Ensure jq is installed (for validation script)
sudo apt-get install jq  # Ubuntu/Debian
brew install jq          # macOS

2. Run Your First Pattern-Synthesis Generation

# Start Claude Code
claude

# Generate 10 iterations with pattern synthesis
/project:infinite-synthesis specs/example_spec.md output 10

What happens:

• Wave 1: Generates 5 iterations exploring different approaches
• Pattern extraction: Identifies best patterns from Wave 1
• Wave 2: Generates 5 more iterations using pattern library
• Result: output/ contains 10 iterations, pattern_library/patterns.json contains extracted patterns

3. Analyze Pattern Effectiveness

# Check what patterns were extracted
cat pattern_library/patterns.json | jq '.patterns | keys'

# Validate pattern library
./validators/check_patterns.sh pattern_library/patterns.json

# Analyze pattern effectiveness
/project:analyze-patterns pattern_library/patterns.json output

4. Continue with Pattern-Guided Generation

# Generate 10 more iterations using existing pattern library
/project:infinite-synthesis specs/example_spec.md output_wave2 10

# Patterns from first 10 iterations will guide these new iterations
# Pattern library automatically updates with new discoveries

Example Specification

See specs/example_spec.md for a complete example specification that demonstrates:

• How to structure requirements for pattern synthesis
• Example patterns that might be extracted
• Quality standards for pattern-guided iterations
• Expected progression across waves

The example spec generates interactive data visualizations, showing how patterns emerge for:

• Code organization (structural)
• Documentation approaches (content)
• Creative techniques (innovation)
• Error handling (quality)

Multi-Shot Prompting Integration

How Patterns Function as Examples

When generating iteration N with pattern library context:

CONTEXT PROVIDED TO AGENT:
1. Specification requirements (what to generate)
2. Existing iterations (avoid duplication)
3. Pattern library examples:

   STRUCTURAL PATTERN: Modular Three-Layer Architecture
   [Complete pattern object with code snippet]

   CONTENT PATTERN: Progressive Disclosure Documentation
   [Complete pattern object with code snippet]

   QUALITY PATTERN: Guard Clause with Fallbacks
   [Complete pattern object with code snippet]

AGENT TASK:
Generate iteration that:
- Follows spec requirements (primary goal)
- Incorporates successful patterns from examples (consistency)
- Adds novel innovation (creativity)
- Maintains or exceeds quality bar (excellence)

Pattern Library Evolution

Wave 1 (Cold Start):
- 5 iterations generated without patterns
- Quality variance: HIGH (exploring different approaches)
- Average score: 7.2/10

Extract Patterns:
- Analyze all 5 iterations
- Identify top 20% (iteration 3 and 4 scored highest)
- Extract 3-5 patterns per category from top iterations
- Create pattern library v1.0

Wave 2 (Pattern-Guided):
- 5 iterations generated WITH pattern library
- Quality variance: MEDIUM (more consistent due to examples)
- Average score: 8.3/10 (+15% improvement)
- Pattern adoption: 80% (4/5 iterations used 2+ patterns)

Extract Patterns:
- Analyze ALL 10 iterations (old + new)
- Keep best patterns from v1.0
- Add new patterns discovered in Wave 2
- Remove patterns no longer exemplary
- Update pattern library v1.1

Wave 3+ (Refined Patterns):
- Quality variance: LOW (established "house style")
- Average score: 8.8/10 (+22% from Wave 1)
- Pattern adoption: 90%+
- Innovation: Still high (patterns are foundation, not limitation)

Key Insights from Web Research

From Anthropic's multi-shot prompting guide:

1. Examples as "Secret Weapon"

"Examples are your secret weapon shortcut for getting Claude to generate exactly what you need."

Application: Pattern library serves as curated examples of "exactly what you need" - proven approaches from top 20% of iterations.

2. Reduce Misinterpretation

"Examples help Claude reduce misinterpretation of instructions"

Application: Instead of describing desired quality in abstract terms, pattern library shows concrete examples of high-quality implementations.

3. Optimal Count (3-5 Examples)

"Provide 3-5 diverse, relevant examples to improve performance"

Application: Each pattern category maintains exactly this sweet spot - enough diversity, not too much context bloat.

4. Cover Edge Cases

"Cover edge cases and potential challenges"

Application: Innovation and quality pattern categories explicitly capture unusual-but-effective approaches and robust error handling.

5. Enforce Uniform Structure

"Use examples to enforce uniform structure and style"

Application: Structural and content patterns demonstrate consistent organization while preserving creative freedom in implementation.

Comparison with Other Infinite Loop Variants

Feature	Base Loop	Web-Enhanced Loop	Pattern Synthesis Loop
Learning Source	Specification only	External URLs	Peer iterations
Knowledge Growth	Static	Linear (URL queue)	Exponential (cumulative)
Consistency	Variable	Medium	High
Innovation	High	High (web-inspired)	High (pattern-based)
Context Efficiency	Good	Lower (fetching web)	Best (reuses examples)
Quality Trajectory	Flat	Variable (URL quality)	Improving (each wave)
Best For	Exploration	Learning new techniques	Consistent production

Use Cases

1. Production-Quality Component Libraries

Generate a library of UI components with consistent architecture and documentation:

/project:infinite-synthesis specs/ui_component.md components 20

Result: 20 components with:

• Consistent code organization (structural patterns)
• Uniform documentation (content patterns)
• Robust error handling (quality patterns)
• Creative variations (innovation patterns)

2. Educational Tutorial Series

Create progressive tutorials that build on established teaching patterns:

/project:infinite-synthesis specs/tutorial.md tutorials infinite

Result: Tutorial series where:

• Each tutorial uses proven explanation patterns
• Quality and clarity improve over time
• Novel teaching approaches are discovered and reused
• Consistent "voice" emerges naturally

3. Test Case Generation

Generate comprehensive test suites with consistent patterns:

/project:infinite-synthesis specs/test_case.md tests 50

Result: Test cases that:

• Follow consistent organization patterns
• Use proven assertion strategies
• Cover edge cases systematically
• Maintain high readability

4. Data Visualization Portfolio

Create a portfolio of visualizations with recognizable style:

/project:infinite-synthesis specs/example_spec.md visualizations 25

Result: Visualizations that:

• Share architectural patterns (modularity, separation of concerns)
• Use consistent documentation approaches
• Implement robust error handling
• Showcase creative variations within a cohesive style

Advanced Usage

Custom Pattern Libraries

You can maintain multiple pattern libraries for different domains:

# Generate UI components with UI-specific patterns
/project:infinite-synthesis specs/ui.md ui_output 10 patterns/ui_patterns.json

# Generate visualizations with viz-specific patterns
/project:infinite-synthesis specs/viz.md viz_output 10 patterns/viz_patterns.json

# Generate APIs with API-specific patterns
/project:infinite-synthesis specs/api.md api_output 10 patterns/api_patterns.json

Manual Pattern Curation

While patterns are extracted automatically, you can manually refine them:

1. Generate initial iterations and extract patterns
2. Edit pattern_library/patterns.json to:
   • Remove less effective patterns
   • Add custom patterns from external sources
   • Refine success metrics and characteristics
   • Update code snippets for clarity
3. Validate with ./validators/check_patterns.sh
4. Use refined library for next generation wave

Pattern-Only Mode

Extract patterns without generating new iterations:

# Analyze existing code to extract patterns
/project:extract-patterns existing_code/ pattern_library/extracted.json deep

# Use those patterns to guide new generations
/project:infinite-synthesis specs/new_spec.md output 10 pattern_library/extracted.json

This enables "learning by example" from any existing codebase.

Metrics and Evaluation

Success Indicators

Pattern Adoption Rate (Target: >80%)

Adoption Rate = (Iterations using 1+ patterns) / (Total post-pattern iterations)

Quality Improvement (Target: >15%)

Quality Improvement = (Post-pattern avg score - Pre-pattern avg score) / Pre-pattern avg score

Consistency Score (Target: <10% variance)

Consistency = 1 - (Std Dev of scores / Mean score)

Innovation Preservation (Target: >0%)

Innovation Preservation = (Unique approaches post-pattern) - (Unique approaches pre-pattern)

Expected Results

After 20 iterations with pattern synthesis:

• Quality: Average score improves from ~7.0 to ~8.5 (+21%)
• Consistency: Score variance decreases from ~2.0 to ~0.5 (-75%)
• Adoption: 80-90% of post-pattern iterations use patterns
• Innovation: Still 3-5 novel techniques per wave (patterns don't reduce creativity)
• Pattern Library: 12-15 high-quality patterns across 4 categories

Limitations and Considerations

When Pattern Synthesis Works Well

• Generating multiple iterations of similar content types
• Need for consistent quality and style
• Want cumulative improvement over time
• Have sufficient iterations for pattern extraction (5+ recommended)

When to Use Other Approaches

• Extremely diverse content (no common patterns)
• Single iteration needed (no peers to learn from)
• Intentionally exploring radically different approaches
• Pattern library would constrain necessary creativity

Pattern Library Maintenance

• Grows with each wave (monitor context usage)
• Keep only top 20% patterns (quality over quantity)
• Remove obsolete patterns as better ones emerge
• Balance diversity (avoid convergence to single approach)

Technical Details

Dependencies

• jq: JSON parsing for validation script
• Claude Code: Task orchestration and sub-agent creation
• Bash: Script execution and file operations

File Structure

infinite_variant_1/
├── .claude/
│   ├── commands/
│   │   ├── infinite-synthesis.md    # Main orchestrator command
│   │   ├── extract-patterns.md      # Pattern extraction command
│   │   └── analyze-patterns.md      # Analysis command
│   └── settings.json                # Command permissions
├── specs/
│   └── example_spec.md              # Example specification with patterns
├── validators/
│   └── check_patterns.sh            # Pattern library validator
├── pattern_library/
│   └── (generated patterns.json files)
├── pattern_library_template.json    # Template and documentation
├── README.md                        # This file
└── CLAUDE.md                        # Project instructions

Context Management

• Pattern library adds ~2-3K tokens per wave (3-5 patterns × 4 categories)
• Sub-agents receive filtered pattern subset (3-5 most relevant)
• Pattern library size capped at 5 patterns/category (prevents bloat)
• Total context for infinite mode: ~150K tokens (supports 30+ waves)

Future Enhancements

Planned Features

1. Pattern Confidence Scores: Track how often patterns lead to high-quality iterations
2. Pattern Combinations: Identify synergistic pattern pairings
3. Anti-Patterns: Extract examples of what NOT to do
4. Pattern Lineage: Track which patterns evolved from which iterations
5. Cross-Project Patterns: Share patterns across different specifications

Research Questions

1. Does pattern adoption reduce innovation over time?
2. What's the optimal pattern library size (3, 5, or 7 per category)?
3. Can patterns be transferred across different content domains?
4. How do manually curated vs automatically extracted patterns compare?

Contributing

Testing Pattern Extraction

# Generate test data
/project:infinite-synthesis specs/example_spec.md test_output 10

# Extract patterns
/project:extract-patterns test_output pattern_library/test_patterns.json

# Validate
./validators/check_patterns.sh pattern_library/test_patterns.json

# Analyze effectiveness
/project:analyze-patterns pattern_library/test_patterns.json test_output

Adding New Pattern Categories

Edit pattern_library_template.json and add new category:

{
  "patterns": {
    "structural": [...],
    "content": [...],
    "innovation": [...],
    "quality": [...],
    "new_category": [...]  // Add here
  }
}

Update extraction logic in .claude/commands/extract-patterns.md to extract new category.

License

MIT License - Use freely, modify as needed, share improvements

Citation

If using this pattern synthesis approach in research or production:

Cross-Iteration Pattern Synthesis System
Infinite Loop Variant #1
Inspired by: Anthropic's Multi-Shot Prompting Guide
https://docs.claude.com/en/docs/build-with-claude/prompt-engineering/multishot-prompting

Resources

• Multi-Shot Prompting Guide: https://docs.claude.com/en/docs/build-with-claude/prompt-engineering/multishot-prompting
• Base Infinite Loop: See parent repository's CLAUDE.md
• Web-Enhanced Loop: See parent repository's WEB_ENHANCED_GUIDE.md
• Example Spec: specs/example_spec.md in this repository

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Built with: Claude Code, multi-shot prompting principles, and cumulative learning

Core Insight: The best teacher is a curated set of excellent examples from your own past work.