dynamic-full-auto

Automate multi-wave workflows with discovery, evidence insertion, and gated handoffs.

Updated Apr 8, 2026
One-click install
npx skills add https://github.com/yiwei79/azoth --skill dynamic-full-auto
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dynamic-full-auto
Source: https://github.com/yiwei79/azoth/tree/main/.opencode/skills/dynamic-full-auto
Command: npx skills add https://github.com/yiwei79/azoth --skill dynamic-full-auto

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Complex, multi-step workflows require sustained human prompts and manual orchestration. This skill automates end-to-end execution within a declared autonomy budget, reducing prompt fatigue and handoff latency.

Core Features & Use Cases

  • End-to-end autonomy: run discovery, evidence gathering, reclassification, and delivery within one session.
  • Budgeted governance: maintain gates and checkpoints for safe autonomous operation.
  • Use Case: orchestrate a multi-wave research-to-delivery pipeline with dynamic re-planning.

Quick Start

Authorize an autonomy budget and initiate a dynamic-full-auto session with your goal.

Frequently Asked Questions about dynamic-full-auto

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

FAQPage Schema
How do I automate long-running workflows without constant manual prompts?▼

Automating long-running workflows without manual prompts requires an orchestrator-driven model that applies an explicit autonomy budget. This approach handles multi-wave execution, evidence insertion, and gated handoffs within a single session to reduce prompt fatigue.

What is an autonomy budget in pipeline orchestration?▼

An autonomy budget in pipeline orchestration is a declared limit that allows an orchestrator-driven model to execute tasks safely. It maintains governance gates and checkpoints for scope alignment during complex, multi-wave autonomous workflows.

How do I set up a multi-wave research-to-delivery pipeline with dynamic re-planning?▼

Setting up a multi-wave research-to-delivery pipeline with dynamic re-planning involves authorizing an autonomy budget and initiating an autonomous session. The orchestrator automates discovery, evidence gathering, reclassification, and delivery handoffs.

Does autonomous workflow execution support evidence insertion and reclassification?▼

Autonomous workflow execution supports evidence insertion and reclassification through its orchestrator-driven multi-agent model. It automates these processes within a declared autonomy budget to ensure safe gated handoffs in complex pipelines.

What are the limitations of fully autonomous pipeline orchestration?▼

The limitations of fully autonomous pipeline orchestration relate to its reliance on a declared autonomy budget. While it reduces manual orchestration, it requires explicit scope gates and roadmap alignment to ensure safe operation during complex multi-wave workflows.