#!/usr/bin/env python3 """ ╔══════════════════════════════════════════════════════════════════════════════╗ ║ AIVA QUEEN ELEVATION MEGA SPRINT ║ ║ ║ ║ $20 Budget | 50 Deep Agents | 5 Hierarchical Swarms | Unstoppable ║ ║ ║ ║ SWARM HIERARCHY: ║ ║ ┌─────────────────────────────────────────────────────────────────────┐ ║ ║ │ QUEEN AIVA (Orchestrator) │ ║ ║ ├─────────────────────────────────────────────────────────────────────┤ ║ ║ │ ALPHA SWARM │ BETA SWARM │ GAMMA SWARM │ DELTA SWARM │ ║ ║ │ Knowledge (10) │ Memory (8) │ Skills (15) │ Evolution (7) │ ║ ║ ├─────────────────────────────────────────────────────────────────────┤ ║ ║ │ OMEGA SWARM - Integration (10) │ ║ ║ └─────────────────────────────────────────────────────────────────────┘ ║ ║ ║ ║ PRE-MORTEM HARDENING ENABLED | VERIFICATION-FIRST | INFINITE LOOPS ║ ╚══════════════════════════════════════════════════════════════════════════════╝ """ import os import sys import json import asyncio import time import hashlib import threading import traceback from datetime import datetime from pathlib import Path from typing import List, Dict, Any, Optional, Callable from dataclasses import dataclass, field from enum import Enum import urllib.request import urllib.error import re from concurrent.futures import ThreadPoolExecutor, as_completed # ═══════════════════════════════════════════════════════════════════════════════ # CONFIGURATION # ═══════════════════════════════════════════════════════════════════════════════ GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyCT_rx0NusUJWoqtT7uxHAKEfHo129SJb8") GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent" # Pricing (per 1M tokens) INPUT_COST = 0.10 OUTPUT_COST = 0.40 # Budget Control TOTAL_BUDGET = 20.00 EMERGENCY_STOP = 19.50 CHECKPOINT_INTERVALS = [5.00, 10.00, 15.00, 19.00] # Alert at these spend levels # Paths BASE_DIR = Path("/mnt/e/genesis-system/AIVA") OUTPUT_DIR = BASE_DIR / "queen_outputs" CHECKPOINT_DIR = OUTPUT_DIR / "checkpoints" PATENT_DIR = Path("/mnt/e/genesis-system/docs/GENESIS PATENTS") KNOWLEDGE_DIR = Path("/mnt/e/genesis-system/KNOWLEDGE_GRAPH") # Create directories OUTPUT_DIR.mkdir(exist_ok=True) CHECKPOINT_DIR.mkdir(exist_ok=True) (OUTPUT_DIR / "alpha").mkdir(exist_ok=True) (OUTPUT_DIR / "beta").mkdir(exist_ok=True) (OUTPUT_DIR / "gamma").mkdir(exist_ok=True) (OUTPUT_DIR / "delta").mkdir(exist_ok=True) (OUTPUT_DIR / "omega").mkdir(exist_ok=True) # ═══════════════════════════════════════════════════════════════════════════════ # TRACKING & MONITORING # ═══════════════════════════════════════════════════════════════════════════════ class SwarmPhase(Enum): ALPHA = "alpha" BETA = "beta" GAMMA = "gamma" DELTA = "delta" OMEGA = "omega" @dataclass class AgentResult: agent_id: str swarm: SwarmPhase success: bool output_size: int tokens_in: int tokens_out: int elapsed: float artifact_path: Optional[str] = None error: Optional[str] = None @dataclass class SwarmResult: swarm: SwarmPhase agents_run: int agents_success: int total_output: int total_cost: float elapsed: float artifacts: List[str] = field(default_factory=list) class BudgetTracker: """Thread-safe budget tracking with alerts and emergency stop.""" def __init__(self, total_budget: float, emergency_stop: float): self.total_budget = total_budget self.emergency_stop = emergency_stop self.input_tokens = 0 self.output_tokens = 0 self.start_time = datetime.now() self._lock = threading.Lock() self.checkpoint_alerts = set() self.swarm_costs = {phase: 0.0 for phase in SwarmPhase} self.agent_results: List[AgentResult] = [] def add_tokens(self, inp: int, out: int, swarm: SwarmPhase): with self._lock: self.input_tokens += inp self.output_tokens += out cost = (inp / 1e6) * INPUT_COST + (out / 1e6) * OUTPUT_COST self.swarm_costs[swarm] += cost # Check for checkpoint alerts for threshold in CHECKPOINT_INTERVALS: if self.cost >= threshold and threshold not in self.checkpoint_alerts: self.checkpoint_alerts.add(threshold) print(f"\n⚠️ BUDGET ALERT: ${self.cost:.2f} spent (${threshold:.2f} threshold)") def add_result(self, result: AgentResult): with self._lock: self.agent_results.append(result) @property def cost(self) -> float: return (self.input_tokens / 1e6) * INPUT_COST + (self.output_tokens / 1e6) * OUTPUT_COST @property def remaining(self) -> float: return self.total_budget - self.cost @property def should_stop(self) -> bool: return self.cost >= self.emergency_stop def elapsed_minutes(self) -> float: return (datetime.now() - self.start_time).total_seconds() / 60 def status(self) -> str: return (f"[{self.elapsed_minutes():.1f}min] " f"${self.cost:.4f}/${self.total_budget} | " f"{self.input_tokens + self.output_tokens:,} tokens | " f"Remaining: ${self.remaining:.2f}") def swarm_status(self) -> str: lines = ["Swarm Costs:"] for swarm, cost in self.swarm_costs.items(): lines.append(f" {swarm.value.upper()}: ${cost:.4f}") return "\n".join(lines) def save_checkpoint(self, swarm: SwarmPhase): """Save checkpoint after swarm completion.""" checkpoint = { "timestamp": datetime.now().isoformat(), "swarm_completed": swarm.value, "total_cost": self.cost, "swarm_costs": {k.value: v for k, v in self.swarm_costs.items()}, "tokens": {"input": self.input_tokens, "output": self.output_tokens}, "agents_run": len(self.agent_results), "agents_success": sum(1 for r in self.agent_results if r.success) } path = CHECKPOINT_DIR / f"checkpoint_{swarm.value}_{datetime.now().strftime('%H%M%S')}.json" path.write_text(json.dumps(checkpoint, indent=2)) print(f"💾 Checkpoint saved: {path.name}") # ═══════════════════════════════════════════════════════════════════════════════ # API INTERFACE # ═══════════════════════════════════════════════════════════════════════════════ async def gemini_deep_call( prompt: str, tracker: BudgetTracker, swarm: SwarmPhase, max_tokens: int = 65536, temperature: float = 0.7 ) -> tuple[str, int, int]: """Execute deep Gemini call with maximum output and tracking.""" if tracker.should_stop: return "[BUDGET EXHAUSTED - EMERGENCY STOP]", 0, 0 payload = { "contents": [{"parts": [{"text": prompt}]}], "generationConfig": { "maxOutputTokens": max_tokens, "temperature": temperature } } try: req = urllib.request.Request( f"{GEMINI_API_URL}?key={GEMINI_API_KEY}", data=json.dumps(payload).encode(), headers={'Content-Type': 'application/json'}, method='POST' ) with urllib.request.urlopen(req, timeout=300) as resp: data = json.loads(resp.read().decode()) text = "" if "candidates" in data and data["candidates"]: parts = data["candidates"][0].get("content", {}).get("parts", []) text = "".join(p.get("text", "") for p in parts) usage = data.get("usageMetadata", {}) tokens_in = usage.get("promptTokenCount", len(prompt) // 4) tokens_out = usage.get("candidatesTokenCount", len(text) // 4) tracker.add_tokens(tokens_in, tokens_out, swarm) return text, tokens_in, tokens_out except Exception as e: return f"[ERROR: {str(e)}]", 0, 0 def save_artifact(content: str, filename: str, swarm: SwarmPhase) -> str: """Save artifact to swarm directory.""" path = OUTPUT_DIR / swarm.value / filename path.write_text(content) return str(path) def extract_code(response: str, extension: str = "py") -> str: """Extract code block from response.""" pattern = rf'```{extension}\n(.*?)```' match = re.search(pattern, response, re.DOTALL) if match: return match.group(1) # Try generic code block match = re.search(r'```\n(.*?)```', response, re.DOTALL) if match: return match.group(1) return response def load_existing_code(*paths: str) -> str: """Load existing code files as context.""" context = [] for path in paths: full_path = Path(path) if full_path.exists(): content = full_path.read_text() context.append(f"# FILE: {path}\n{content}\n") return "\n".join(context) # ═══════════════════════════════════════════════════════════════════════════════ # AGENT DEFINITIONS # ═══════════════════════════════════════════════════════════════════════════════ # ───────────────────────────────────────────────────────────────────────────── # ALPHA SWARM: Knowledge Foundation (10 Agents) # ───────────────────────────────────────────────────────────────────────────── ALPHA_AGENTS = [ { "id": "ALPHA_01_PDF_EXTRACTOR", "name": "Patent PDF Text Extractor", "prompt": """You are a document processing expert. Create a comprehensive PDF text extraction system for patent documents. REQUIREMENTS: 1. Extract text from PDF files using PyPDF2 and pdfplumber 2. Handle multi-page documents with proper ordering 3. Extract tables, figures captions, and structured data 4. Clean extracted text (remove headers/footers, page numbers) 5. Preserve section structure (Abstract, Claims, Description) 6. Generate structured JSON output per patent The system must process these 9 Genesis patents: - Patent 1: Cryptographic Validation - Patent 2: Currency Validation - Patent 3: Risk Assessment - Patent 4: Audit Trail - Patent 5: Multi-Model Consensus - Patent 6: Confidence Scoring - Patent 7: Hallucination Detection - Patent 8: Privacy Validation - Patent 9: Self-Improving System Output a COMPLETE Python module with: - PDFExtractor class with async batch processing - PatentDocument dataclass for structured output - CLI interface for processing patent directory - Error handling and logging - Unit tests ```python # patent_pdf_extractor.py """ }, { "id": "ALPHA_02_CHUNKER", "name": "Intelligent Text Chunker", "prompt": """You are a text processing expert. Create an intelligent chunking system for RAG. REQUIREMENTS: 1. Semantic chunking (not just character/token splitting) 2. Preserve sentence boundaries 3. Configurable chunk sizes (256, 512, 1024 tokens) 4. Overlap handling (10-20% overlap between chunks) 5. Metadata preservation (source file, page, section) 6. Special handling for patent claims (keep claims together) 7. Hierarchical chunking (document -> section -> paragraph -> chunk) Output formats: - Chunk text - Chunk metadata (source, page, section, position) - Chunk embeddings placeholder - Chunk relationships (next, previous, parent) ```python # semantic_chunker.py """ }, { "id": "ALPHA_03_EMBEDDER", "name": "Vector Embedding Generator", "prompt": """You are an ML engineer. Create a vector embedding system using multiple providers. REQUIREMENTS: 1. Support multiple embedding providers: - Gemini embedding API (text-embedding-004) - OpenAI embeddings (text-embedding-3-small) - Local sentence-transformers fallback 2. Batch processing for efficiency 3. Caching layer (avoid re-embedding same text) 4. Normalization of embeddings 5. Dimension reduction option (PCA to 256/512) 6. Async processing with rate limiting Interface: - embed_text(text) -> vector - embed_batch(texts) -> vectors - embed_chunks(chunks) -> chunks_with_embeddings ```python # embedding_generator.py """ }, { "id": "ALPHA_04_VECTOR_STORE", "name": "Vector Database Manager", "prompt": """You are a database architect. Create a vector storage system with multiple backends. REQUIREMENTS: 1. Support multiple vector databases: - ChromaDB (local, default) - Qdrant (production) - FAISS (in-memory, fast) 2. Collection management (create, delete, list) 3. CRUD operations for vectors 4. Metadata filtering 5. Hybrid search (vector + keyword) 6. Batch operations 7. Persistence and backup Collections needed: - patents: Patent document chunks - knowledge: Knowledge graph entities - axioms: Generated axioms - skills: Skill definitions - conversations: Episodic memory ```python # vector_store_manager.py """ }, { "id": "ALPHA_05_RAG_ENGINE", "name": "RAG Query Engine", "prompt": """You are a RAG systems expert. Create a production-grade RAG query engine. REQUIREMENTS: 1. Query processing: - Query expansion (synonyms, related terms) - Query embedding - Hybrid retrieval (semantic + BM25) 2. Retrieval: - Top-K retrieval with configurable K - MMR (Maximum Marginal Relevance) for diversity - Reranking with cross-encoder 3. Context assembly: - Smart context window packing - Source deduplication - Citation tracking 4. Response generation: - Context-grounded responses - Citation injection - Confidence scoring Integration with AIVA: - aiva_query(question) -> answer_with_citations ```python # rag_query_engine.py """ }, { "id": "ALPHA_06_PATENT_INGESTER", "name": "Patent Ingestion Pipeline", "prompt": """You are a data pipeline engineer. Create the master patent ingestion pipeline. REQUIREMENTS: 1. Orchestrate the full ingestion flow: - PDF extraction - Text cleaning - Chunking - Embedding - Vector storage 2. Progress tracking and resumability 3. Validation at each stage 4. Duplicate detection 5. Incremental updates (only process new/changed docs) 6. Batch processing for efficiency Pipeline stages: 1. EXTRACT: PDF -> Raw Text 2. CLEAN: Raw Text -> Cleaned Text 3. CHUNK: Cleaned Text -> Chunks 4. EMBED: Chunks -> Embedded Chunks 5. STORE: Embedded Chunks -> Vector DB 6. INDEX: Update knowledge graph ```python # patent_ingestion_pipeline.py """ }, { "id": "ALPHA_07_KNOWLEDGE_GRAPH", "name": "Knowledge Graph Builder", "prompt": """You are a knowledge engineer. Create a knowledge graph system for patents. REQUIREMENTS: 1. Entity types: - Patent (id, title, claims, abstract) - Concept (technical concepts from patents) - Algorithm (methods described in patents) - Relationship (between patents) 2. Relationship types: - DEPENDS_ON (patent dependencies) - IMPLEMENTS (concept -> algorithm) - VALIDATES (patent -> validation type) - ENHANCES (patent improvements) 3. Graph storage (NetworkX for now, Neo4j ready) 4. Query interface: - Find related patents - Trace concept lineage - Find all validations for a type 5. Visualization export (D3.js format) ```python # knowledge_graph_builder.py """ }, { "id": "ALPHA_08_ENTITY_EXTRACTOR", "name": "Patent Entity Extractor", "prompt": """You are an NLP expert. Create an entity extraction system for patents. REQUIREMENTS: 1. Extract entities from patent text: - Technical terms - Algorithm names - System components - Validation methods - Metrics and thresholds 2. Use pattern matching + LLM extraction 3. Entity normalization (canonical forms) 4. Confidence scoring 5. Relationship extraction 6. Output as JSONL for knowledge graph Named entity types for Genesis patents: - VALIDATION_METHOD - CRYPTOGRAPHIC_PRIMITIVE - CONFIDENCE_METRIC - RISK_DIMENSION - PRIVACY_TECHNIQUE - CONSENSUS_ALGORITHM ```python # patent_entity_extractor.py """ }, { "id": "ALPHA_09_AXIOM_GENERATOR", "name": "Patent Axiom Generator", "prompt": """You are a knowledge distillation expert. Create an axiom generation system. REQUIREMENTS: 1. Extract core truths from patents: - Fundamental principles - Invariant relationships - Proven assertions 2. Axiom structure: - Statement (concise truth) - Confidence (0-1) - Evidence (source references) - Dependencies (other axioms) 3. Axiom validation: - Logical consistency check - Contradiction detection - Redundancy elimination 4. Axiom hierarchy: - Foundation axioms (core truths) - Derived axioms (from combinations) - Operational axioms (for decision making) ```python # axiom_generator.py """ }, { "id": "ALPHA_10_KNOWLEDGE_VALIDATOR", "name": "Knowledge Validation System", "prompt": """You are a quality assurance expert. Create a knowledge validation system. REQUIREMENTS: 1. Validate ingested knowledge: - Completeness check (all patents processed) - Consistency check (no contradictions) - Coverage check (all claims indexed) 2. RAG quality testing: - Test queries with known answers - Retrieval precision/recall - Response accuracy 3. Knowledge graph validation: - Orphan detection - Cycle detection - Relationship consistency 4. Generate validation report 5. Auto-fix common issues ```python # knowledge_validator.py """ } ] # ───────────────────────────────────────────────────────────────────────────── # BETA SWARM: Memory Architecture (8 Agents) # ───────────────────────────────────────────────────────────────────────────── BETA_AGENTS = [ { "id": "BETA_01_WORKING_MEMORY", "name": "Working Memory Manager", "prompt": """You are a cognitive systems architect. Create AIVA's working memory system. REQUIREMENTS: 1. Redis-based working memory: - Current conversation context - Active task state - Recent retrievals cache - Real-time metrics 2. Memory operations: - Store with TTL - Retrieve with fallback - Update atomically - Clear selectively 3. Capacity management: - Max items limit - LRU eviction - Priority preservation 4. Context window management: - Track token usage - Compress when needed - Summarize old context ```python # working_memory_manager.py """ }, { "id": "BETA_02_EPISODIC_MEMORY", "name": "Episodic Memory Store", "prompt": """You are a database architect. Create AIVA's episodic memory system. REQUIREMENTS: 1. PostgreSQL-based episodic store: - Conversation history - Decision records - Learning events - Error logs 2. Schema design: - episodes table (id, timestamp, type, content, metadata) - decisions table (id, episode_id, input, output, confidence, outcome) - learnings table (id, source_episode, axiom_generated, confidence) 3. Query patterns: - Recent episodes by type - Decisions with outcomes - Learning patterns 4. Retention policies: - Archive old episodes - Summarize for semantic memory - Maintain audit trail ```python # episodic_memory_store.py """ }, { "id": "BETA_03_SEMANTIC_MEMORY", "name": "Semantic Memory System", "prompt": """You are a knowledge systems expert. Create AIVA's semantic memory. REQUIREMENTS: 1. Long-term knowledge storage: - Facts and concepts - Skills and capabilities - User preferences - Domain expertise 2. Integration with vector store: - Embedded knowledge retrieval - Similarity-based recall - Contextual relevance 3. Knowledge organization: - Hierarchical categories - Cross-references - Confidence decay 4. Memory promotion: - Working -> Episodic (on save) - Episodic -> Semantic (on consolidation) ```python # semantic_memory_system.py """ }, { "id": "BETA_04_MEMORY_CONSOLIDATION", "name": "Memory Consolidation Engine", "prompt": """You are a cognitive scientist. Create memory consolidation system. REQUIREMENTS: 1. Consolidation triggers: - Time-based (hourly, daily) - Event-based (session end, milestone) - Capacity-based (memory full) 2. Consolidation operations: - Summarize episodic memories - Extract patterns and axioms - Update semantic memory - Prune working memory 3. Sleep-inspired processing: - Replay important events - Strengthen connections - Forget irrelevant details 4. Metrics: - Consolidation efficiency - Memory utilization - Knowledge growth rate ```python # memory_consolidation_engine.py """ }, { "id": "BETA_05_MEMORY_RETRIEVAL", "name": "Unified Memory Retrieval", "prompt": """You are a search systems expert. Create unified memory retrieval. REQUIREMENTS: 1. Multi-memory search: - Search working memory first (fast) - Fall back to episodic (recent) - Finally semantic (comprehensive) 2. Query routing: - Factual queries -> Semantic - Recent events -> Episodic - Current context -> Working 3. Result fusion: - Merge results from all stores - Deduplicate and rank - Format for context 4. Caching and optimization: - Cache frequent queries - Preload likely retrievals - Batch similar queries ```python # unified_memory_retrieval.py """ }, { "id": "BETA_06_SURPRISE_DETECTOR", "name": "Surprise Detection System", "prompt": """You are a prediction systems expert. Create surprise detection for learning. REQUIREMENTS: 1. Prediction tracking: - Record AIVA's predictions - Compare to actual outcomes - Calculate prediction error 2. Surprise metrics: - Magnitude of error - Frequency of surprise - Domain-specific surprise 3. Learning triggers: - High surprise -> Investigate - Consistent surprise -> Update model - Surprise pattern -> New axiom 4. Integration with memory: - Log surprises to episodic - Update semantic on resolution - Adjust confidence scores ```python # surprise_detection_system.py """ }, { "id": "BETA_07_CONTEXT_MANAGER", "name": "Context Window Manager", "prompt": """You are an LLM systems expert. Create context window management. REQUIREMENTS: 1. Token budget tracking: - Count tokens accurately - Reserve space for response - Track per-section usage 2. Context assembly: - System prompt (fixed) - Retrieved knowledge (dynamic) - Conversation history (sliding) - Current query (required) 3. Compression strategies: - Summarize old messages - Truncate verbose retrievals - Priority-based pruning 4. Context optimization: - Most relevant first - Recency weighting - Redundancy removal ```python # context_window_manager.py """ }, { "id": "BETA_08_MEMORY_DASHBOARD", "name": "Memory Status Dashboard", "prompt": """You are a monitoring expert. Create memory system dashboard. REQUIREMENTS: 1. Real-time metrics: - Memory utilization (working, episodic, semantic) - Query latencies - Cache hit rates - Consolidation status 2. Visualization data: - Time series data - Distribution charts - Relationship graphs 3. Alerts: - Memory pressure warnings - Consolidation failures - Query timeout alerts 4. API endpoints: - GET /memory/status - GET /memory/metrics - POST /memory/consolidate ```python # memory_dashboard.py """ } ] # ───────────────────────────────────────────────────────────────────────────── # GAMMA SWARM: Skill System (15 Agents) # ───────────────────────────────────────────────────────────────────────────── GAMMA_AGENTS = [ { "id": "GAMMA_01_SKILL_REGISTRY", "name": "Skill Registry System", "prompt": """You are a plugin systems architect. Create AIVA's skill registry. REQUIREMENTS: 1. Skill definition: - Skill ID and name - Description and usage - Input/output schema - Dependencies - Confidence threshold 2. Registry operations: - Register skill - Discover skills - Invoke skill - Validate skill 3. Skill categories: - Patent skills (validation) - Memory skills (store/recall) - Meta skills (self-improvement) - Utility skills (formatting, parsing) 4. Skill versioning: - Track versions - Rollback capability - A/B testing support ```python # skill_registry.py """ }, { "id": "GAMMA_02_SKILL_EXECUTOR", "name": "Skill Execution Engine", "prompt": """You are a runtime systems expert. Create skill execution engine. REQUIREMENTS: 1. Execution pipeline: - Parse skill invocation - Validate inputs - Execute skill - Validate outputs - Log execution 2. Error handling: - Retry with backoff - Fallback skills - Graceful degradation 3. Performance: - Execution timeout - Resource limits - Parallel execution 4. Observability: - Execution traces - Performance metrics - Error tracking ```python # skill_executor.py """ }, { "id": "GAMMA_03_PATENT_SKILL_P1", "name": "Cryptographic Validation Skill", "prompt": """Create Patent 1 skill: Cryptographic Validation. This skill enables AIVA to: 1. Generate HMAC-SHA256 signatures for AI outputs 2. Create cryptographic proof chains 3. Verify output integrity 4. Detect tampering Skill interface: - Input: AI output text, optional key - Output: Signed output with proof chain - Confidence: Based on cryptographic strength Integration: - Auto-apply to high-stakes outputs - Store proofs in audit trail - Verify on retrieval ```python # skill_patent_cryptographic.py """ }, { "id": "GAMMA_04_PATENT_SKILL_P5", "name": "Multi-Model Consensus Skill", "prompt": """Create Patent 5 skill: Multi-Model Consensus Validation. This skill enables AIVA to: 1. Query multiple AI models for same question 2. Aggregate responses with voting 3. Detect disagreements 4. Calculate consensus confidence Skill interface: - Input: Query to validate - Output: Consensus response with confidence - Models: Can specify which models to use Integration: - Use for critical decisions - Log disagreements for learning - Escalate low consensus ```python # skill_patent_consensus.py """ }, { "id": "GAMMA_05_PATENT_SKILL_P7", "name": "Hallucination Detection Skill", "prompt": """Create Patent 7 skill: Real-Time Hallucination Detection. This skill enables AIVA to: 1. Analyze outputs for potential hallucinations 2. Cross-reference with knowledge base 3. Flag unverifiable claims 4. Calculate hallucination probability Skill interface: - Input: AI output text - Output: Hallucination report with flagged segments - Confidence: Per-claim confidence scores Detection methods: - Knowledge base verification - Self-consistency check - Source attribution ```python # skill_patent_hallucination.py """ }, { "id": "GAMMA_06_PATENT_SKILL_P3", "name": "Risk Assessment Skill", "prompt": """Create Patent 3 skill: Multi-Dimensional Risk Assessment. This skill enables AIVA to: 1. Score advice across risk dimensions 2. Aggregate risk with configurable weights 3. Generate risk reports 4. Recommend risk mitigations Risk dimensions: - Financial risk - Legal risk - Operational risk - Reputational risk - Technical risk ```python # skill_patent_risk.py """ }, { "id": "GAMMA_07_PATENT_SKILL_P4", "name": "Audit Trail Skill", "prompt": """Create Patent 4 skill: Immutable Audit Trail. This skill enables AIVA to: 1. Log all decisions with hash chains 2. Verify audit trail integrity 3. Query decision history 4. Generate compliance reports Audit events: - Queries processed - Decisions made - Skills invoked - Errors encountered ```python # skill_patent_audit.py """ }, { "id": "GAMMA_08_PATENT_SKILL_P6", "name": "Confidence Scoring Skill", "prompt": """Create Patent 6 skill: Dynamic Confidence Scoring. This skill enables AIVA to: 1. Calculate confidence for any output 2. Adjust based on context 3. Apply confidence decay 4. Calibrate from feedback Confidence factors: - Source reliability - Knowledge coverage - Query specificity - Historical accuracy ```python # skill_patent_confidence.py """ }, { "id": "GAMMA_09_PATENT_SKILL_P8", "name": "Privacy Validation Skill", "prompt": """Create Patent 8 skill: Privacy-Preserving Validation. This skill enables AIVA to: 1. Validate without exposing sensitive data 2. Apply data masking 3. Check for PII leakage 4. Generate privacy-safe reports Privacy techniques: - Tokenization - Masking - Aggregation - Differential privacy concepts ```python # skill_patent_privacy.py """ }, { "id": "GAMMA_10_PATENT_SKILL_P9", "name": "Self-Improving Skill", "prompt": """Create Patent 9 skill: Self-Improving Validation Thresholds. This skill enables AIVA to: 1. Track validation performance 2. Auto-adjust thresholds 3. Learn from outcomes 4. Optimize validation settings Improvement loop: - Collect validation outcomes - Analyze error patterns - Adjust thresholds - Measure improvement ```python # skill_patent_self_improve.py """ }, { "id": "GAMMA_11_SLASH_COMMANDS", "name": "Slash Command Handler", "prompt": """Create AIVA's slash command system. COMMANDS TO IMPLEMENT: /aiva-status - Full system status /aiva-validate - Validate AI output with patents /aiva-remember - Store fact to memory /aiva-recall - Retrieve from memory /aiva-patent - Query patent knowledge /aiva-learn - Trigger learning loop /aiva-evolve - Trigger evolution loop /aiva-skills - List available skills /aiva-revenue - Revenue dashboard /aiva-help - Help for all commands Each command should: 1. Parse arguments 2. Execute appropriate skill 3. Format output 4. Log to audit trail ```python # slash_command_handler.py """ }, { "id": "GAMMA_12_SKILL_CREATOR", "name": "Meta Skill Creator", "prompt": """Create a meta-skill that creates new skills. This skill enables AIVA to: 1. Analyze a capability request 2. Generate skill code 3. Validate skill syntax 4. Register new skill 5. Test skill execution Skill creation flow: 1. User describes needed capability 2. AIVA generates skill definition 3. Code is validated and tested 4. Skill is registered 5. Confirmation with usage examples ```python # skill_creator.py """ }, { "id": "GAMMA_13_SKILL_COMPOSER", "name": "Skill Composition Engine", "prompt": """Create skill composition system for complex workflows. REQUIREMENTS: 1. Chain multiple skills: - Sequential execution - Parallel execution - Conditional branching 2. Data flow: - Pass outputs to inputs - Transform between skills - Aggregate results 3. Templates: - Common workflow patterns - Reusable compositions - Parameterized workflows 4. Validation: - Type checking - Dependency resolution - Cycle detection ```python # skill_composer.py """ }, { "id": "GAMMA_14_PATENT_SKILL_P2", "name": "Currency Validation Skill", "prompt": """Create Patent 2 skill: Currency/Financial Validation. This skill enables AIVA to: 1. Validate financial data accuracy 2. Check exchange rates 3. Verify calculations 4. Detect anomalies Financial checks: - Rate freshness - Calculation accuracy - Source verification - Anomaly detection ```python # skill_patent_currency.py """ }, { "id": "GAMMA_15_UNIFIED_VALIDATOR", "name": "Unified Triple Gate Validator", "prompt": """Create the master validation skill that orchestrates all patents. TRIPLE GATE PATTERN: - ALPHA GATE: P1 (Crypto) + P2 (Currency) + P4 (Audit) - BETA GATE: P3 (Risk) + P6 (Confidence) + P8 (Privacy) - GAMMA GATE: P5 (Consensus) + P7 (Hallucination) + P9 (Self-Improve) This skill: 1. Routes to appropriate gates based on content 2. Aggregates validation results 3. Makes final validation decision 4. Generates comprehensive report 5. Updates learning from outcomes ```python # unified_triple_gate_validator.py """ } ] # ───────────────────────────────────────────────────────────────────────────── # DELTA SWARM: Evolution Engine (7 Agents) # ───────────────────────────────────────────────────────────────────────────── DELTA_AGENTS = [ { "id": "DELTA_01_LEARNING_LOOP", "name": "Continuous Learning Loop", "prompt": """Create AIVA's continuous learning system. LEARNING LOOPS: 1. Micro Loop (per query): - Record prediction - Observe outcome - Update confidence 2. Meso Loop (per session): - Analyze patterns - Generate insights - Update axioms 3. Macro Loop (daily): - Comprehensive review - Model updates - Threshold adjustments Learning sources: - User feedback - Validation outcomes - Surprise events - Performance metrics ```python # continuous_learning_loop.py """ }, { "id": "DELTA_02_REFLEXION", "name": "Reflexion Self-Critique", "prompt": """Create reflexion-based self-critique system. REFLEXION PATTERN: 1. Generate response 2. Critique response 3. Identify improvements 4. Regenerate if needed 5. Log learnings Critique dimensions: - Accuracy - Completeness - Clarity - Relevance - Safety Integration: - Apply to high-stakes outputs - Cache good critiques - Learn from critique patterns ```python # reflexion_engine.py """ }, { "id": "DELTA_03_EVOLUTION_PLANNER", "name": "Evolution Planning System", "prompt": """Create evolution planning for continuous improvement. EVOLUTION DOMAINS: 1. Knowledge evolution: - Identify gaps - Plan acquisitions - Validate additions 2. Skill evolution: - Analyze usage - Identify needs - Create/improve skills 3. Memory evolution: - Optimize storage - Improve retrieval - Enhance consolidation 4. Performance evolution: - Profile bottlenecks - Optimize hot paths - Scale resources ```python # evolution_planner.py """ }, { "id": "DELTA_04_THRESHOLD_OPTIMIZER", "name": "Threshold Optimization Engine", "prompt": """Create threshold optimization system. THRESHOLDS TO OPTIMIZE: 1. Confidence thresholds: - When to answer vs. abstain - When to escalate - When to validate 2. Risk thresholds: - Accept/reject levels per dimension - Aggregation weights 3. Performance thresholds: - Latency limits - Memory limits - Cost limits Optimization methods: - Bayesian optimization - Reinforcement learning - A/B testing - Grid search ```python # threshold_optimizer.py """ }, { "id": "DELTA_05_PERFORMANCE_PROFILER", "name": "Performance Profiling System", "prompt": """Create performance profiling and optimization. METRICS TO TRACK: 1. Latency: - Query processing time - Retrieval time - Generation time 2. Throughput: - Queries per second - Concurrent capacity 3. Accuracy: - Response quality - Validation pass rate 4. Cost: - Token usage - API costs - Storage costs Optimization recommendations based on metrics. ```python # performance_profiler.py """ }, { "id": "DELTA_06_EXPERIMENT_RUNNER", "name": "A/B Experiment System", "prompt": """Create A/B testing system for improvements. EXPERIMENT TYPES: 1. Skill variants 2. Threshold settings 3. Prompt templates 4. Retrieval strategies System features: - Experiment definition - Traffic splitting - Metric collection - Statistical analysis - Winner selection - Rollout automation ```python # experiment_runner.py """ }, { "id": "DELTA_07_DARWIN_MODE", "name": "Competitive Reasoning (DARWIN)", "prompt": """Create DARWIN mode for competitive reasoning paths. DARWIN PATTERN: 1. Generate N reasoning paths 2. Evaluate each path 3. Select winner 4. Optionally combine best elements 5. Learn from competition Competition criteria: - Accuracy - Completeness - Efficiency - Novelty Use cases: - Complex reasoning - Creative tasks - Optimization problems ```python # darwin_mode.py """ } ] # ───────────────────────────────────────────────────────────────────────────── # OMEGA SWARM: Integration Layer (10 Agents) # ───────────────────────────────────────────────────────────────────────────── OMEGA_AGENTS = [ { "id": "OMEGA_01_QUEEN_ORCHESTRATOR", "name": "Queen AIVA Orchestrator", "prompt": """Create the Queen AIVA orchestrator - the master brain. QUEEN RESPONSIBILITIES: 1. Query routing: - Classify query intent - Select appropriate skills - Manage context 2. Response generation: - Assemble context - Generate response - Apply validations - Format output 3. Learning integration: - Log all interactions - Trigger learning loops - Update confidence 4. Self-monitoring: - Health checks - Performance tracking - Error recovery QUEEN PERSONALITY: - Confident but humble - Direct but helpful - Knowledgeable about all 9 patents - Aware of her capabilities and limitations ```python # queen_aiva_orchestrator.py """ }, { "id": "OMEGA_02_API_SERVER", "name": "AIVA API Server", "prompt": """Create FastAPI server for AIVA. ENDPOINTS: 1. Chat: - POST /chat - Main chat endpoint - POST /chat/stream - Streaming response - GET /chat/history - Get history 2. Validation: - POST /validate - Validate AI output - POST /validate/batch - Batch validation 3. Memory: - POST /memory/store - Store to memory - GET /memory/recall - Recall from memory - POST /memory/consolidate - Trigger consolidation 4. Skills: - GET /skills - List skills - POST /skills/{id}/invoke - Invoke skill 5. Admin: - GET /health - Health check - GET /metrics - Prometheus metrics - POST /admin/evolve - Trigger evolution Features: - JWT authentication - Rate limiting - Request validation - CORS configuration - WebSocket support ```python # aiva_api_server.py """ }, { "id": "OMEGA_03_SYSTEM_PROMPT", "name": "AIVA System Prompt Generator", "prompt": """Create AIVA's dynamic system prompt generator. SYSTEM PROMPT COMPONENTS: 1. Identity: - Who AIVA is - Her capabilities - Her limitations 2. Knowledge: - Patent summaries - Current axioms - Skill list 3. Context: - Current user - Session history - Active tasks 4. Instructions: - Response format - Validation requirements - Safety guidelines Dynamic elements: - Load fresh from memory - Inject relevant knowledge - Adapt to query type ```python # system_prompt_generator.py """ }, { "id": "OMEGA_04_OLLAMA_BRIDGE", "name": "Ollama Integration Bridge", "prompt": """Create bridge between AIVA systems and Ollama/QwenLong. BRIDGE FUNCTIONS: 1. Request formatting: - Build system prompt - Add context - Format for Ollama API 2. Response processing: - Parse response - Extract thinking blocks - Format for user 3. Error handling: - Timeout handling - Retry logic - Fallback responses 4. Performance: - Connection pooling - Request batching - Caching Ollama endpoint: http://152.53.201.152:23405/api/generate Model: qwen-long ```python # ollama_bridge.py """ }, { "id": "OMEGA_05_REVENUE_TRACKER", "name": "Patent Revenue Tracker", "prompt": """Create revenue tracking for patent usage. TRACKING: 1. Usage counts: - Per patent - Per skill - Per user 2. Revenue calculation: - Per-validation pricing - Subscription tracking - Enterprise contracts 3. Reporting: - Daily/weekly/monthly - By patent - By customer 4. Analytics: - Usage trends - Revenue projections - ROI by patent ```python # revenue_tracker.py """ }, { "id": "OMEGA_06_TEST_SUITE", "name": "Comprehensive Test Suite", "prompt": """Create comprehensive test suite for AIVA. TEST CATEGORIES: 1. Unit tests: - Each skill - Each memory operation - Each API endpoint 2. Integration tests: - Skill chains - Memory flow - API to skill 3. E2E tests: - Full query flow - Validation pipeline - Learning loop 4. Performance tests: - Latency benchmarks - Load testing - Memory pressure Use pytest with fixtures. ```python # test_aiva_comprehensive.py """ }, { "id": "OMEGA_07_DEPLOYMENT_CONFIG", "name": "Deployment Configuration", "prompt": """Create deployment configuration for AIVA. CONFIGURATIONS: 1. Docker: - Dockerfile for AIVA - docker-compose.yml 2. Kubernetes: - Deployment - Service - ConfigMap - Secrets 3. Environment: - Development - Staging - Production 4. Monitoring: - Prometheus config - Grafana dashboards - Alert rules ```yaml # deployment/ """ }, { "id": "OMEGA_08_CLI_INTERFACE", "name": "AIVA CLI Interface", "prompt": """Create CLI interface for AIVA. COMMANDS: aiva chat - Interactive chat aiva ask "..." - Single question aiva validate - Validate input aiva status - System status aiva memory - Memory operations aiva skills - Skill management aiva evolve - Trigger evolution aiva config - Configuration Features: - Rich terminal output - Command history - Tab completion - Configuration file ```python # aiva_cli.py """ }, { "id": "OMEGA_09_DOCUMENTATION", "name": "AIVA Documentation", "prompt": """Create comprehensive documentation for AIVA. DOCUMENTATION: 1. User Guide: - Getting started - Chat interface - Slash commands - Skills usage 2. Developer Guide: - Architecture overview - Adding skills - Memory integration - API reference 3. Operations Guide: - Deployment - Monitoring - Troubleshooting - Scaling 4. Patent Portfolio: - Patent summaries - Integration map - Revenue guide ```markdown # AIVA_DOCUMENTATION.md """ }, { "id": "OMEGA_10_STARTUP_SCRIPT", "name": "AIVA Startup & Health", "prompt": """Create startup script and health monitoring. STARTUP: 1. Check dependencies: - Redis connection - PostgreSQL connection - Ollama availability - Vector store ready 2. Initialize systems: - Load memory - Register skills - Start loops 3. Health checks: - Memory health - Skill health - API health 4. Graceful shutdown: - Save state - Close connections - Log shutdown ```python # aiva_startup.py """ } ] # ═══════════════════════════════════════════════════════════════════════════════ # EXECUTION ENGINE # ═══════════════════════════════════════════════════════════════════════════════ async def execute_agent( agent: Dict, tracker: BudgetTracker, swarm: SwarmPhase, context: str = "" ) -> AgentResult: """Execute a single agent with full tracking.""" agent_id = agent["id"] if tracker.should_stop: return AgentResult( agent_id=agent_id, swarm=swarm, success=False, output_size=0, tokens_in=0, tokens_out=0, elapsed=0, error="Budget exhausted" ) print(f"\n{'─' * 60}") print(f"🤖 Agent: {agent['name']}") print(f" ID: {agent_id}") print(f" {tracker.status()}") print(f"{'─' * 60}") # Build prompt with context prompt = agent["prompt"] if context: prompt = f"EXISTING CODE CONTEXT:\n{context}\n\n{prompt}" start = time.time() response, tokens_in, tokens_out = await gemini_deep_call( prompt, tracker, swarm, max_tokens=65536 ) elapsed = time.time() - start # Check for errors if "[ERROR" in response or "[BUDGET" in response: print(f" ❌ Error: {response[:100]}") return AgentResult( agent_id=agent_id, swarm=swarm, success=False, output_size=len(response), tokens_in=tokens_in, tokens_out=tokens_out, elapsed=elapsed, error=response[:200] ) # Extract and save code code = extract_code(response) filename = f"{agent_id.lower()}.py" artifact_path = save_artifact(code, filename, swarm) print(f" ✅ Generated: {len(response):,} chars in {elapsed:.1f}s") print(f" 📄 Saved: {filename}") result = AgentResult( agent_id=agent_id, swarm=swarm, success=True, output_size=len(response), tokens_in=tokens_in, tokens_out=tokens_out, elapsed=elapsed, artifact_path=artifact_path ) tracker.add_result(result) return result async def execute_swarm( swarm: SwarmPhase, agents: List[Dict], tracker: BudgetTracker, context_loader: Optional[Callable] = None ) -> SwarmResult: """Execute an entire swarm of agents.""" print(f"\n{'═' * 70}") print(f"{'═' * 70}") print(f" 🐝 SWARM: {swarm.value.upper()}") print(f" 📊 Agents: {len(agents)}") print(f" 💰 Budget Status: {tracker.status()}") print(f"{'═' * 70}") print(f"{'═' * 70}") start = time.time() results = [] artifacts = [] for i, agent in enumerate(agents): if tracker.should_stop: print(f"\n⚠️ EMERGENCY STOP - Budget exhausted at ${tracker.cost:.4f}") break print(f"\n[{i+1}/{len(agents)}] Executing {agent['id']}...") # Load context if available context = "" if context_loader: context = context_loader(swarm, i) result = await execute_agent(agent, tracker, swarm, context) results.append(result) if result.artifact_path: artifacts.append(result.artifact_path) elapsed = time.time() - start # Calculate swarm stats swarm_result = SwarmResult( swarm=swarm, agents_run=len(results), agents_success=sum(1 for r in results if r.success), total_output=sum(r.output_size for r in results), total_cost=tracker.swarm_costs[swarm], elapsed=elapsed, artifacts=artifacts ) # Save checkpoint tracker.save_checkpoint(swarm) print(f"\n{'─' * 70}") print(f" ✅ SWARM {swarm.value.upper()} COMPLETE") print(f" 📊 Success: {swarm_result.agents_success}/{swarm_result.agents_run}") print(f" 📄 Output: {swarm_result.total_output:,} chars") print(f" 💰 Cost: ${swarm_result.total_cost:.4f}") print(f" ⏱️ Time: {swarm_result.elapsed:.1f}s") print(f"{'─' * 70}") return swarm_result # ═══════════════════════════════════════════════════════════════════════════════ # MAIN EXECUTION # ═══════════════════════════════════════════════════════════════════════════════ async def main(): print(""" ╔══════════════════════════════════════════════════════════════════════════════╗ ║ ║ ║ █████╗ ██╗██╗ ██╗ █████╗ ██████╗ ██╗ ██╗███████╗███████╗███╗ ██║ ║ ██╔══██╗██║██║ ██║██╔══██╗ ██╔═══██╗██║ ██║██╔════╝██╔════╝████╗ ██║ ║ ███████║██║██║ ██║███████║ ██║ ██║██║ ██║█████╗ █████╗ ██╔██╗ ██║ ║ ██╔══██║██║╚██╗ ██╔╝██╔══██║ ██║▄▄ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║ ║ ██║ ██║██║ ╚████╔╝ ██║ ██║ ╚██████╔╝╚██████╔╝███████╗███████╗██║ ╚████║ ║ ╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═╝ ╚═╝ ╚══▀▀═╝ ╚═════╝ ╚══════╝╚══════╝╚═╝ ╚═══╝ ║ ║ ║ QUEEN ELEVATION MEGA SPRINT ║ ║ ║ ║ $20 BUDGET | 50 DEEP AGENTS | 5 HIERARCHICAL SWARMS ║ ║ ║ ║ ALPHA: Knowledge Foundation (10 agents) ║ ║ BETA: Memory Architecture (8 agents) ║ ║ GAMMA: Skill System (15 agents) ║ ║ DELTA: Evolution Engine (7 agents) ║ ║ OMEGA: Integration Layer (10 agents) ║ ║ ║ ║ PRE-MORTEM HARDENING | VERIFICATION-FIRST | UNSTOPPABLE ║ ║ ║ ╚══════════════════════════════════════════════════════════════════════════════╝ """) # Initialize tracker tracker = BudgetTracker(TOTAL_BUDGET, EMERGENCY_STOP) swarm_results = [] # Pre-mortem check print("\n🔍 PRE-MORTEM CHECKS:") print(f" ✅ Budget: ${TOTAL_BUDGET}") print(f" ✅ Emergency Stop: ${EMERGENCY_STOP}") print(f" ✅ Total Agents: {len(ALPHA_AGENTS) + len(BETA_AGENTS) + len(GAMMA_AGENTS) + len(DELTA_AGENTS) + len(OMEGA_AGENTS)}") print(f" ✅ Output Directory: {OUTPUT_DIR}") print(f" ✅ API Key: {'Set' if GEMINI_API_KEY else 'MISSING!'}") # ALPHA SWARM - Knowledge Foundation if not tracker.should_stop: result = await execute_swarm(SwarmPhase.ALPHA, ALPHA_AGENTS, tracker) swarm_results.append(result) # BETA SWARM - Memory Architecture if not tracker.should_stop: result = await execute_swarm(SwarmPhase.BETA, BETA_AGENTS, tracker) swarm_results.append(result) # GAMMA SWARM - Skill System if not tracker.should_stop: result = await execute_swarm(SwarmPhase.GAMMA, GAMMA_AGENTS, tracker) swarm_results.append(result) # DELTA SWARM - Evolution Engine if not tracker.should_stop: result = await execute_swarm(SwarmPhase.DELTA, DELTA_AGENTS, tracker) swarm_results.append(result) # OMEGA SWARM - Integration Layer if not tracker.should_stop: result = await execute_swarm(SwarmPhase.OMEGA, OMEGA_AGENTS, tracker) swarm_results.append(result) # Final Summary print(f""" ╔══════════════════════════════════════════════════════════════════════════════╗ ║ QUEEN ELEVATION MEGA SPRINT COMPLETE ║ ╠══════════════════════════════════════════════════════════════════════════════╣ ║ ║ ║ SWARM RESULTS: ║""") total_agents = 0 total_success = 0 total_output = 0 for sr in swarm_results: total_agents += sr.agents_run total_success += sr.agents_success total_output += sr.total_output print(f"║ {sr.swarm.value.upper():6} | {sr.agents_success:2}/{sr.agents_run:2} success | ${sr.total_cost:.4f} | {sr.total_output:,} chars") print(f"""║ ║ ╠══════════════════════════════════════════════════════════════════════════════╣ ║ TOTALS: ║ ║ Agents Run: {total_agents:3} ║ ║ Successful: {total_success:3} ║ ║ Total Output: {total_output:,} chars ║ ║ Total Cost: ${tracker.cost:.4f} ║ ║ Total Tokens: {tracker.input_tokens + tracker.output_tokens:,} ║ ║ Runtime: {tracker.elapsed_minutes():.1f} minutes ║ ║ ║ ╠══════════════════════════════════════════════════════════════════════════════╣ ║ ARTIFACTS: ║ ║ {OUTPUT_DIR} ╚══════════════════════════════════════════════════════════════════════════════╝ """) # List all artifacts print("\n📄 Generated Artifacts:") for swarm_dir in ["alpha", "beta", "gamma", "delta", "omega"]: swarm_path = OUTPUT_DIR / swarm_dir if swarm_path.exists(): files = list(swarm_path.glob("*.py")) if files: print(f"\n {swarm_dir.upper()}:") for f in sorted(files): print(f" - {f.name} ({f.stat().st_size:,} bytes)") print(f"\n\n👑 AIVA QUEEN ELEVATION COMPLETE! 👑") print(f"Total investment: ${tracker.cost:.4f}") print(tracker.swarm_status()) if __name__ == "__main__": asyncio.run(main())