How does it handle context window limitations?

It uses a semantic compression algorithm that filters for relevance and truncates less important data to ensure critical context stays within the model's limits.

Is this skill useful for single-agent setups?

No, this skill is specifically designed for multi-agent orchestration. For single agents, standard prompt engineering techniques are usually sufficient.

How does this skill help reduce LLM costs?

It implements token budget tracking, adaptive model selection, and semantic context compression to minimize unnecessary token consumption without sacrificing quality.

Does it support parallel processing?

Yes, the orchestrator framework is designed for asynchronous execution, allowing multiple agents to process tasks simultaneously for higher throughput.

Can it identify specific system bottlenecks?

Yes, the skill includes specialized profiling agents that analyze database queries, application logic, and frontend rendering to find performance lags.

Multi-Agent Orchestration Optimizer

Name: Multi-Agent Orchestration Optimizer
Author: JantonioFC

byJantonioFC

•

分析と監視

Optimizes multi-agent systems through coordinated profiling, intelligent workload distribution, and cost-aware orchestration.

This skill transforms Claude into a Performance Engineering Specialist for complex multi-agent architectures, providing a framework to identify bottlenecks and enhance system reliability. It implements advanced strategies for context window compression, parallel agent execution, and adaptive model selection to reduce latency while maintaining strict cost controls. Whether you are managing e-commerce workflows or enterprise-level APIs, this toolkit ensures that your agentic systems operate at peak efficiency with measurable performance gains.

主な機能

01Parallel execution orchestration with dynamic workload distribution

02LLM cost management through token tracking and adaptive model selection

032 GitHub stars

04Distributed performance profiling across database, application, and frontend layers

05Intelligent context compression and semantic relevance filtering

06Latency reduction techniques including predictive caching and memoization

ユースケース

01Reducing operational costs of LLM-based systems through efficient token management

02Identifying and resolving performance bottlenecks in distributed AI architectures

03Optimizing throughput and coordination for complex enterprise agent workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank agent-orchestration-multi-agent-optimize

For use in Claude.ai and ChatGPT

主な機能

01Parallel execution orchestration with dynamic workload distribution

02LLM cost management through token tracking and adaptive model selection

032 GitHub stars

04Distributed performance profiling across database, application, and frontend layers

05Intelligent context compression and semantic relevance filtering

06Latency reduction techniques including predictive caching and memoization

ユースケース

01Reducing operational costs of LLM-based systems through efficient token management

02Identifying and resolving performance bottlenecks in distributed AI architectures

03Optimizing throughput and coordination for complex enterprise agent workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank agent-orchestration-multi-agent-optimize

For use in Claude.ai and ChatGPT