p235

This repository contains collections of design patterns and organizational metaphors with formal specifications, including a virtual hardware implementation (NPU-253) for accelerated pattern operations, an APL language implementation for array-based pattern analysis, a Skill Framework for generalized pattern-based workflows, and an Optimal Grip Implementation for cognitive-aware pattern exploration.

🎯 NEW: Optimal Grip Implementation

Cognitive "optimal grip" on the gestalt salience landscape - A complete multi-layer implementation featuring:

• 🔍 Datalog Query Layer: Declarative pattern discovery and relationship inference
• 🧠 ML Salience Engine: Context-aware relevance scoring and gestalt detection
• 🎨 Interactive D3.js Visualization: Force-directed graph explorer with real-time filtering
• 🌐 REST API: FastAPI-based endpoints for programmatic access
• 📊 Emergence Tracking: Detect synergistic pattern combinations

Quick Start:

# Install dependencies
pip install pyDatalog numpy fastapi uvicorn

# Start the API
python3 pattern_api.py

# Open visualization (in browser)
# file:///path/to/pattern_explorer.html

Documentation:

• Quick Start Guide - Get started in 5 minutes
• Implementation Analysis - Complete design rationale
• API Documentation - Swagger/OpenAPI (after starting API)

Features:

• 🎯 Context-aware pattern salience scoring
• 🔗 Gestalt pattern detection (emergent groupings)
• 📈 Emergence tracking in pattern sequences
• 🔍 Declarative Datalog queries with inference
• 🎨 Interactive force-directed graph visualization
• 🌐 RESTful API with 7 endpoints
• 📊 Multi-scale perception (Towns → Buildings → Construction)

Try it now:

# Demo the salience engine
python3 pattern_salience_engine.py

# Demo Datalog queries
python3 demo_datalog_queries.py

# Start API and open pattern_explorer.html in browser
python3 pattern_api.py

🤖 Agent Invocation System: Collaborative Pattern Intelligence

NEW: All 4,836 agents in the .github/agents/apl0/ hierarchy are now configured for cross-invocation, enabling sophisticated multi-agent collaboration!

User → @apl0/dim2 (Physical dimension agent)
  ↓ delegates to
@apl0/dim2/cat1 (Towns category agent)
  ↓ delegates to
@apl0/dim2/cat1/seq01 (Regional planning sequence)
  ↓ delegates to
@apl0/dim2/cat1/seq01/apl001 (INDEPENDENT REGIONS pattern)

Features:

• 🤖 4,836 Specialized Agents across 6 dimensions, 18 categories, 216 sequences
• 🔗 Cross-Invocation Protocol: Any agent can invoke any other agent
• 📋 Standardized Context Passing: Consistent format for task delegation
• 🎯 Hierarchical Delegation: Agents delegate to appropriate specialists
• 🌐 Multi-Dimensional Views: Same pattern from physical/social/conceptual/psychic perspectives
• 🧭 Navigation Support: Context agents help navigate pattern hierarchy

Agent Types:

• Dimension Agents (6) - Coordinate entire dimensions
• Category Agents (18) - Coordinate scale-specific work
• Sequence Agents (216) - Manage emergent phenomena
• Pattern Agents (1,555+) - Provide specific pattern guidance
• Context Agents (3,041+) - Navigate broader/narrower relationships

Documentation:

• Agent Invocation Guide - Complete reference
• Agent Invocation Examples - Detailed scenarios
• Quick Reference - Quick lookup
• .github/agents/apl0/README.md - Agent directory overview

Standard Context Format:

I am working on [task description].
So far I have [summary of work done].
I need help with [specific question] because [reason].
Constraints: [any limitations or requirements]
Related patterns in use: [list of pattern IDs]

Example Invocations:

# Get help with town design
@apl0/dim2/cat1 → Category agent for town-scale patterns

# Understand a specific pattern
@apl0/dim2/cat1/seq01/apl001 → INDEPENDENT REGIONS pattern

# Find what comes next
@apl0/dim2/cat1/seq01/apl001/narrower → Next patterns in hierarchy

# Multi-dimensional view
@apl0/dim2/.../apl028 (Physical)
@apl0/dim3/.../apl028 (Social)
@apl0/dim4/.../apl028 (Conceptual)

Validation:

python3 validate_agent_invocation.py
# ✓ All 4,836 agent files validated

🚀 Skill Framework: Pattern-Based Workflow System

NEW: Generalized framework for implementing sequences of skills as ordered routines defined by algorithmic workflows!

from skill_framework import Skill, SequenceBuilder, SkillWorkflow, WorkflowEngine

# Create skills from patterns
skill1 = Skill("apl1", "Independent Regions", "Design autonomous regions")
skill2 = Skill("apl2", "Distribution of Towns", "Balance urban settlements")

# Build sequence
sequence = (SequenceBuilder("regional", "Regional Planning")
           .add_skill(skill1)
           .add_skill(skill2)
           .build())

# Execute workflow
context = SkillContext(domain="physical")
results = sequence.execute(context)

Features:

• 🎯 Domain-agnostic skill execution (physical/social/conceptual/psychic)
• 🔄 Sequential and conditional workflow modes
• ✅ Preconditions and postconditions validation
• 📊 State management with scoped variables
• 🌐 Domain transformation support
• 🔗 Integration with pattern sequences and NPU-253
• ✅ 17 passing tests

Documentation:

• Skill Framework README - Complete documentation
• Quick Reference - Quick start guide
• Demo | Tests | Integration Examples

# Run demo
python3 demo_skill_framework.py

# Run tests
python3 test_skill_framework.py

# Run integration examples
python3 integration_examples.py

🎯 Meta-Recursive Achievement

NEW: This repository now applies Pattern Language principles to itself, creating a living example of patterns in action!

• PATTERN_MAP.md - Repository organized as 8 independent regions (Pattern 1)
• NAVIGATION_HUB.md - Multiple entry points for exploration (Pattern 28)
• SEQUENCE_NAVIGATION.md - Navigate 36 pattern sequences (Pattern 52)
• markdown/sequences/ - NEW: Comprehensive sequence documentation with aggregated problem-solution pairs
• PATTERN_INDEX.md - Comprehensive pattern access (Pattern 30)
• PATTERN_CROSS_REFERENCE.md - Links between representations (Pattern 8)
• META_RECURSIVE_IMPLEMENTATION.md - How patterns apply to themselves

The repository structure embodies the patterns it documents, achieving optimal cognitive grip on the gestalt through self-application.

🚀 NPU-253: Neural Processing Unit / Natural Patterning Unit

NEW: Virtual hardware device implementing the 253-pattern language as a memory-mapped coprocessor!

from npu253 import PatternCoprocessorDriver, NPUConfig

npu = PatternCoprocessorDriver(NPUConfig())
npu.load()

# Query patterns
pattern = npu.query_by_id(1)
print(f"{pattern.name}: {pattern.solution}")

# Domain transformation
social = npu.transform_pattern("12610010", "social")

Features:

• 🔧 Hardware-style MMIO register interface
• 🎯 253 APL patterns + 253 archetypal patterns
• 🔄 Domain transformation (physical/social/conceptual/psychic)
• ⚡ LRU caching for performance
• 📊 Telemetry and diagnostics
• ✅ 34 passing tests

Documentation:

• NPU-253 Blueprint - Architecture and design
• NPU-253 API - Complete API reference
• npu253/README.md - Package documentation
• Demo | Tests

# Run demo
python3 demo_npu253.py

# Run tests
python3 test_npu253.py

🔢 APL Language Implementation

NEW: Array-based implementation in APL (A Programming Language)!

APL is a powerful array-oriented language perfect for pattern analysis and transformations. This implementation leverages APL's concise array operations for efficient pattern queries.

⍝ Query patterns by category
towns ← GetTownPatterns

⍝ Transform archetypal pattern to domain
social ← TransformToSocial pattern

⍝ Find pattern relationships
connected ← GetAllConnectedPatterns 1

Features:

• ✨ 253 patterns as array-based data structures
• 🔍 Fast array-based queries and filters
• 🔄 Domain transformations (physical/social/conceptual/psychic)
• 🔗 Relationship navigation and path finding
• 📊 Pattern sequences and statistics
• 🎯 ~1,900 lines of APL code

Documentation:

• apl_language/README.md - Module overview
• apl_language/INSTALLATION.md - Complete installation and usage guide
• Demo | Tests

# Run tests
python3 test_apl_implementation.py

# Generate data
python3 generate_apl_data.py

APL Files:

• patterns.apl - Core pattern data structures
• queries.apl - Search and query operations
• transformations.apl - Domain transformations
• relationships.apl - Pattern relationship navigation
• demo.apl - Interactive demonstrations
• data_loader.apl - Pattern data initialization

🔄 Meta-Recursive Organization

Patterns Applied to Repository Structure:

This repository uses Pattern Language principles to organize itself:

Navigation & Access

• PATTERN_MAP.md - 8 independent regions (Pattern 1: Independent Regions)
• NAVIGATION_HUB.md - Multiple entry points (Pattern 28: Eccentric Nucleus)
• SEQUENCE_NAVIGATION.md - Navigate 36 sequences (Pattern 52: Network of Paths)
• PATTERN_INDEX.md - Complete access (Pattern 30: Activity Nodes)

Integration & Understanding

• PATTERN_CROSS_REFERENCE.md - Links between APL/UIA/Archetypal (Pattern 8: Mosaic of Subcultures)
• META_RECURSIVE_IMPLEMENTATION.md - Complete analysis of self-application
• .github/copilot-instructions.md - GitHub Copilot guidance using Pattern Language principles

Cognitive Achievement

• ✅ Multi-scale perception: Navigate repository → region → sequence → pattern
• ✅ Relationship richness: Clear connections between all elements
• ✅ Contextual relevance: Find patterns by domain/context/need
• ✅ Gestalt perception: See the whole as living system
• ✅ Optimal grip: Structure supports understanding

The repository is both documentation of patterns and example of patterns in use.

📖 Documentation

For Contributors & AI Assistants

• .github/copilot-instructions.md - GitHub Copilot instructions applying Pattern Language principles
• CLAUDE.md - Developer quick reference guide
• QUICK_REFERENCE.md - Essential commands and workflows

Formal Specifications

• Formal Specification Summary - Complete overview of architecture and Z++ specifications
• docs/ - Comprehensive technical documentation (2,328 lines)
  • Architecture overview with Mermaid diagrams
  • Z++ formal specifications (data model, system state, operations, integrations)
  • Usage guide for specifications

Pattern Collections

• APL: Christopher Alexander's "A Pattern Language" (253 patterns) - architectural and urban design patterns
• UIA: Union of International Associations "Patterns & Metaphors" (253 patterns) - organizational and conceptual patterns

Markdown Versions

The markdown/ directory contains converted versions of all APL and UIA pages in markdown format for improved readability and accessibility:

• markdown/apl/: 253 A Pattern Language patterns (apl001.md - apl253.md)
• markdown/uia/: 253 UIA Patterns & Metaphors (12610010.md - 12612530.md)
• markdown/arc/: 102 Archetypal Patterns extracted from UIA templates with domain-specific placeholders

See markdown/README.md for detailed information about the conversion process and markdown structure.

Pattern Language Schema

The repository includes formalized JSON schemas for both the Pattern Language and Archetypal Patterns:

APL Pattern Language Schema

• Pattern Language Schema (pattern_language_generated.json) - Complete meta-pattern, categories, and sequences
• Pattern Sequences (pattern_sequences.json) - All 36 pattern sequences with emergent phenomena
• Categories (category_*.json) - Towns, Buildings, and Construction categories

See PATTERN_SCHEMA_README.md for detailed information about the APL schema structure.

Archetypal Pattern Schema

• Archetypal Pattern Schema (archetypal_pattern_schema.json) - JSON schema for archetypal patterns
• Archetypal Patterns (archetypal_patterns.json) - All 102 archetypal patterns with domain mappings
• Placeholder Reference (archetypal_placeholders.json) - Complete placeholder documentation

Archetypal patterns use the format: "generic {{domain-specific}} generic" and can be transformed across:

• Physical - Spatial, material, architectural domains
• Social - Organizational, community, institutional domains
• Conceptual - Knowledge, theoretical, paradigmatic domains
• Psychic - Awareness, consciousness, mental domains

See ARCHETYPAL_SCHEMA_README.md for detailed information about the archetypal pattern schema.

Generating Schemas

# Generate APL Pattern Language schema
python3 generate_pattern_schema.py

# Generate Archetypal Pattern schema
python3 generate_archetypal_schema.py

Testing Schemas

# Test APL schema (if tests exist)
python3 validate_schema.py

# Test Archetypal schema
python3 test_archetypal_schema.py

Demo Schemas

# Demo APL schema
python3 demo_pattern_schema.py

# Demo Archetypal schema
python3 demo_archetypal_schema.py

OpenCog Atomese Representation

The Pattern Language has been converted to OpenCog's Atomese format for knowledge representation and pattern matching:

• opencog_atomese/ - Complete Atomese hypergraph representation in Scheme format
  • pattern_language.scm - Complete representation
  • meta_pattern.scm - Meta-pattern
  • categories.scm - Categories with InheritanceLinks
  • sequences.scm - Sequences with MemberLinks
  • Enhanced Features:
    • pattern_language_enhanced.scm - Enhanced with diagrams, details, and connections
    • relationship_types.scm - Pattern relationship types (complement, conflict, alternative)
    • patterns/ - Individual .scm files for modular loading

The Atomese format enables:

• Pattern matching and reasoning in OpenCog
• Hypergraph queries for pattern relationships
• Knowledge graph navigation and inference
• Integration with AI/AGI systems

See opencog_atomese/README.md for usage examples and opencog_atomese/ENHANCEMENTS.md for enhanced features documentation.

Paradigm & Language Analysis

Comprehensive analysis of optimal implementation approaches for achieving cognitive "optimal grip" on the gestalt salience landscape:

• PARADIGM_LANGUAGE_ANALYSIS.md - Complete paradigm and language evaluation

  • Analysis of 6 programming paradigms (hypergraph, functional, logic, OOP, constraint, agent-based)
  • Evaluation of 6+ languages (Scheme, Datalog, Python, Haskell, JavaScript, Prolog)
  • Cognitive affordances mapping
  • Recommended multi-layer architecture

• IMPLEMENTATION_GUIDE.md - Practical implementation guide

  • Concrete code examples for each layer
  • Datalog query system integration
  • Haskell domain transformation engine
  • Python salience computation
  • D3.js visualization framework

• PARADIGM_COMPARISON_MATRIX.md - Detailed comparison matrices

  • Cognitive requirements vs paradigms matrix
  • Implementation tasks vs languages matrix
  • Paradigm synergy analysis
  • Use-case specific recommendations

Key Finding: Multi-paradigm approach required - Hypergraph (Scheme/OpenCog) foundation + Datalog queries + Functional transformations (Haskell) + Python integration + JavaScript visualization provides optimal cognitive affordances.

Generating Atomese Files

# Generate base Atomese files
python3 generate_opencog_atomese.py

# Generate enhanced features
python3 generate_enhanced_atomese.py

Testing Atomese Files

# Test base files
python3 test_opencog_atomese.py

# Test enhanced features
python3 test_enhanced_atomese.py

Demo Enhanced Features

python3 demo_enhanced_atomese.py

Demo Query System

# Demo Datalog query integration (requires pyDatalog)
pip install pyDatalog
python3 demo_datalog_queries.py