agentic-engineering

Coordinate multi-agent software development workflows with risk classification and quality gates.

12|1|Updated Apr 7, 2026

Search Tags:#workflow #multi-agent #orchestration #risk-classification #agentic-engineering #skeptic-review

Ready to run

npx skills add https://github.com/Space-Dinosaurs/DinoStack --skill agentic-engineering-space-dinosaurs

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentic-engineering
Source: https://github.com/Space-Dinosaurs/DinoStack/tree/main/content
Command: npx skills add https://github.com/Space-Dinosaurs/DinoStack --skill agentic-engineering-space-dinosaurs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

The Agentic Engineering skill orchestrates a robust, multi-agent workflow for planning, implementing, and reviewing software tasks, providing a disciplined structure that reduces coordination overhead and increases outcome quality.

Core Features & Use Cases

Multi-agent orchestration: spawns named agents (architect, engineer, skeptic, qa-engineer) to plan, implement, and verify changes.
Risk-aware governance: performs risk classification, adversarial Skeptic reviews, and quality gates to ensure safe shipping.
Cross-session traceability: supports briefs and plans with artifacts, loop resumes, and telemetry to enable auditability across sessions.

Quick Start

Start a session by running the Activation preflight, then trigger the architect to draft a plan and spawn an engineer to implement the change.

Frequently Asked Questions about agentic-engineering

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

FAQPage Schema

How does multi-agent orchestration work for software development workflows?▼

Multi-agent orchestration works by spawning specialized named agents—architect, engineer, skeptic, and qa-engineer—to plan, implement, review, and verify software changes through a structured sequence. This reduces coordination overhead while increasing outcome quality across development sessions.

What is adversarial review in AI-driven engineering and when is it needed?▼

Adversarial review in AI-driven engineering is a risk governance mechanism where a dedicated Skeptic agent challenges proposed software changes. It is needed when performing risk classification to ensure safe shipping and verify that quality gates are met before delivery.

How do I coordinate planning and implementation across multiple AI agents?▼

You coordinate multiple AI agents by triggering an activation preflight to start a session, prompting the architect to draft a plan, and then spawning an engineer to implement the change. The workflow generates briefs, plans, and telemetry artifacts for traceability.

Can I resume an AI engineering workflow across different sessions?▼

Yes, you can resume an AI engineering workflow across different sessions. The orchestration supports cross-session traceability by generating persistent artifacts like briefs, plans, and telemetry, enabling loop resumes and continuous auditability throughout the software delivery process.

What's the best way to ensure safe software changes during automated AI development?▼

The best way to ensure safe software changes during automated AI development is to enforce risk-aware governance controls. This approach uses risk classification, adversarial Skeptic reviews, and quality gates to verify correctness and prevent unsafe modifications before shipping.

Do I need specific dependencies to run a multi-agent software engineering workflow?▼

No specific external dependencies are required to run this multi-agent software engineering workflow. It operates autonomously using internal scripts and references to manage the orchestration sequence, artifact generation, and quality-gate enforcement for your development tasks.