agent-orchestration

Coordinate BioETL multi-agent workflows across py-* profiles using an orchestration map.

1|Updated Dec 1, 2025

Search Tags:#automation #workflow #multi-agent #orchestration #profiles #coordinate #bioetl

Ready to run

npx skills add https://github.com/SatoryKono/BioactivityDataAcquisition --skill agent-orchestration-satorykono

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-orchestration
Source: https://github.com/SatoryKono/BioactivityDataAcquisition/tree/main/docs/skills/local/agent-orchestration
Command: npx skills add https://github.com/SatoryKono/BioactivityDataAcquisition --skill agent-orchestration-satorykono

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill coordinates complex BioETL multi-agent workflows across py-* profiles using a Codex-local orchestration map to reduce handoff overhead and improve traceability.

Core Features & Use Cases

Orchestrates coordinated tasks across multiple agent profiles to enable end-to-end data acquisition, normalization, and validation.
Loads and references an orchestration map and agent profiles to drive deterministic routing through defined phases.
Supports scenario-based task routing (full, quick, config, doc) with artifact capture and verification for auditability.

Quick Start

Load the orchestration map and the py-* agent profiles, then trigger routing to the appropriate profile skills to execute a coordinated task.

Frequently Asked Questions about agent-orchestration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I coordinate multi-agent workflows for structured task handoffs?▼

Multi-agent workflow coordination routes work through designated paths using an orchestration map and agent profiles. This enables deterministic phase routing, structured handoffs, and traceable artifact verification across configured profiles.

What is phase routing in multi-agent orchestration and when is it needed?▼

Phase routing in multi-agent orchestration directs tasks through predefined stages based on scenario requirements. It is needed when workflows require structured handoffs, scenario-based task routing (full, quick, config, doc), and artifact verification for auditability.

How to execute end-to-end data acquisition and normalization across multiple agent profiles?▼

Executing end-to-end data acquisition and normalization requires loading an orchestration map and py-* agent profiles, then triggering routing to appropriate profile skills to run coordinated tasks with traceable outputs.

Can I use this orchestration approach for scenario-based task routing across different configurations?▼

Yes, scenario-based task routing supports full, quick, config, and doc scenarios across py-* profiles. It drives deterministic routing through defined phases and maintains artifact capture and verification for auditability across orchestration configurations.

What is the best way to maintain traceable outputs and verifications across coordinated agent tasks?▼

To maintain traceable outputs and verifications, load the orchestration map and agent profiles to route work through designated paths. This structured handoff approach ensures artifact capture and deterministic phase routing across profiles.

Why does multi-agent orchestration reduce handoff overhead in complex workflows?▼

Multi-agent orchestration reduces handoff overhead by using a local orchestration map to drive deterministic routing through defined phases. This replaces manual coordination with structured phase routing and artifact verification across agent profiles.