Frugal Orchestrator

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

# Run demonstration
cd /a0/usr/projects/frugal_orchestrator/demo && bash run_demo.sh

Python Integration

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

# Run demonstration
cd /a0/usr/projects/frugal_orchestrator/demo && bash run_demo.sh

Python Integration

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)