recommend

Name: recommend
Availability: InStock
Author: Morgan

Generate evidence-based knowledge-system design recommendations with cited research claims.

Updated Aug 23, 2026

Search Tags:#architecture #knowledge-management #presets #decision-support #advisory #research-backed

Ready to run

npx skills add https://github.com/hellofrommorgan/intent-computer --skill recommend-hellofrommorgan

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: recommend
Source: https://github.com/hellofrommorgan/intent-computer/tree/main/packages/plugin/src/plugin-skills/recommend
Command: npx skills add https://github.com/hellofrommorgan/intent-computer --skill recommend-hellofrommorgan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The recommend skill provides evidence-based architectural guidance for designing knowledge systems, turning ambiguous goals into structured, research-backed recommendations that you can act on.

Core Features & Use Cases

Advisory sketches that map user constraints to architecture presets and dimension settings.
Phase-driven research support: identify relevant claims, perform targeted searches, and justify design choices with citations.
Compare presets or evolve an existing system toward research-backed configurations with traceable reasoning.

Quick Start

Describe your use case and constraints, then ask for /recommend to start an evidence-based architecture sketch.

Frequently Asked Questions about recommend

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

FAQPage Schema

What is evidence-based knowledge-system architecture design?▼

Evidence-based knowledge-system architecture design maps user constraints to dimension presets and searches research claims to produce recommended configurations with cited rationale. It transforms ambiguous design goals into structured, actionable advisory sketches.

How do I get research-backed recommendations for knowledge management architecture?▼

To get research-backed architecture recommendations, describe your use case and constraints, then initiate the advisory process. The system identifies relevant research claims, maps dimensions, and produces a recommended configuration with cited supporting evidence.

Can I use this to compare presets for decision-support system design?▼

Yes, you can compare architecture presets for decision-support systems. The process maps user signals to presets, evaluates trade-offs against research claims, and justifies design choices with traceable citations and documented constraints.

When do I need research-cited guidance for knowledge system evolution?▼

You need research-cited guidance when evolving an existing knowledge system toward research-backed configurations. It documents potential evolution paths, constraints, and trade-offs, ensuring your architecture changes are justified by traceable reasoning.

What are the limitations of phase-driven advisory sketches for knowledge systems?▼

Phase-driven advisory sketches are limited by the scope of available research claims and mapped dimension presets. Recommendations depend on accurately interpreting user signals and constraints, meaning ambiguous inputs may yield less precise architectural configurations.