architect

Automate goal-driven execution for AI agents via the ARCHITECT loop.

5|1|Updated Feb 9, 2026

Search Tags:#planning #integration #agent #orchestration #memory #execution #autonomous

Ready to run

npx skills add https://github.com/contrario/aetherlang --skill architect-contrario

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: architect
Source: https://github.com/contrario/aetherlang/tree/main/skills/architect
Command: npx skills add https://github.com/contrario/aetherlang --skill architect-contrario

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ARCHITECT automates goal-driven execution for AI agents by providing a dedicated execution layer that can decompose, plan, execute, validate, and adapt, eliminating the need for manual step-by-step guidance.

Core Features & Use Cases

Autonomous goal decomposition into a dependency-aware task graph.
Execution with validation and self-correction on failure.
Zero dependencies and zero-config setup; works with any model.
Pairs with apex-agent and agent-memoria to complete the autonomous stack.
Use case: transforms reactive QA agents into proactive execution engines for complex AI workflows.

Quick Start

Provide ARCHITECT with a goal, and it will autonomously parse, decompose, execute, and deliver results.

Frequently Asked Questions about architect

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

FAQPage Schema

How do I automate complex AI workflows with autonomous planning and execution?▼

Automate complex AI workflows by providing a high-level goal to an autonomous execution layer. The system parses the goal, decomposes it into a dependency-aware task graph, executes the steps, and validates the results without requiring manual guidance.

Can AI agents self-correct and recover from failures during autonomous execution?▼

AI agents can self-correct during autonomous execution by validating task outputs against expected results. When a failure occurs, the execution loop adapts and retries the specific step, eliminating the need to manually restart the entire complex workflow.

What is the best way to decompose complex goals into actionable tasks for AI agents?▼

The best way to decompose complex goals is using an autonomous execution loop that parses the objective and sequences it into a dependency-aware task graph. This approach transforms reactive QA agents into proactive execution engines.

Do I need external libraries or complex configurations to set up autonomous AI agents?▼

You do not need external libraries or complex configurations to set up autonomous AI agents. This execution layer operates with zero dependencies and zero-config, allowing it to work with any model immediately.

How does the ARCHITECT loop handle goal-driven execution for AI agents?▼

The ARCHITECT loop handles goal-driven execution by cycling through parse, decompose, sequence, execute, validate, adapt, synthesize, and reflect stages. This structured process ensures complex objectives are fully completed and validated.

What tools should I pair with autonomous execution layers for a complete AI agent stack?▼

Pair the autonomous execution layer with apex-agent and agent-memoria to complete the autonomous stack. This combination provides the necessary orchestration and memory components for sustained, complex AI workflows.