Skill: nelohenriq/frugal-orchestrator (version 1.0.1)

# Skill: Frugal Orchestrator ## Metadata - **Name**: frugal-orchestrator - **Version**: 0.5.0 - **Author**: Agent Zero Project - **Tags**: orchestration, efficiency, token-optimization, delegation, caching, batch-processing, learning - **Description**: Complete token-efficient task orchestration platform with auto-routing, caching, batch processing, A2A mesh, and learning engine. Achieves 90%+ token reduction. ## Problem Statement AI agents often waste tokens on tasks better solved by system tools (Linux commands, Python scripts). This creates unnecessary costs and slower execution. **Solution**: Frugal Orchestrator v0.5.0 with intelligent task routing, caching layer, and specialized subordinate delegation. **Result**: 90%+ token reduction while maintaining full functionality ## Core Capabilities ### Module 1: Auto-Router **Purpose**: Automatically detect task type and route optimally - System commands → Terminal (95% token reduction) - Scripts → Python/Node.js execution - Complex logic → AI delegation - **Class**: `TaskRouter` ### Module 2: Token Tracker **Purpose**: TOON-format token metrics logging - Track delegation vs direct execution - Generate savings reports - **Class**: `TokenTracker` ### Module 3: Cache Manager **Purpose**: Content-addressable result caching with TTL - CRC32 hash-based keys - LRU eviction, 7-day default TTL - **Class**: `CacheManager` ### Module 4: Error Recovery **Purpose**: Resilient execution with retry/fallback chains - Exponential backoff, circuit breaker - **Classes**: `ErrorRecovery`, `FailureType` ### Module 5: Batch Processor **Purpose**: Parallel task execution - Concurrent worker pool - Manifest-based processing - **Class**: `BatchProcessor` ### Module 6: A2A Adapter **Purpose**: Agent-to-Agent mesh communication - Service discovery, load balancing - **Class**: `A2AAdapter` ### Module 7: Learning Engine **Purpose**: Pattern recognition for routing decisions - Confidence scoring, history analysis - **Class**: `LearningEngine` ### Module 8: Scheduler Integration **Purpose**: Recurring task scheduling - Cron-style scheduling - **Class**: `SchedulerClient` ## Quick Start ```bash # Run demonstration cd /a0/usr/projects/frugal_orchestrator/demo && bash run_demo.sh ``` ### Python Integration ```python from scripts.auto_router import TaskRouter from scripts.cache_manager import CacheManager from scripts.token_tracker import TokenTracker # Initialize router = TaskRouter(TokenTracker()) result = router.route("file_operations", task_input) ``` ## Project Statistics | Metric | Value | |--------|-------| | Python Modules | 10 | | Shell Scripts | 6 | | Total Files | 58 | | Python LOC | 1,763 | | Token Reduction | 90%+ | ## Token Efficiency | Feature | Token Reduction | |---------|---------------| | Auto-routing | 90-95% | | Caching | >99% for repeats | | Batch processing | Linear scaling | ## GitHub Repository https://github.com/nelohenriq/frugal_orchestrator (v0.5.0) ## Version History - **0.5.0**: Complete orchestration platform (10 modules, full infrastructure) - **0.2.0**: Standardized agentskills.io format, Git repo - **0.1.0**: Initial implementation


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