product-domain-engine

Orchestrates seven skills through a five-phase protocol to formalize domain-tied products.

Updated May 16, 2026
One-click install
npx skills add https://github.com/organvm-i-theoria/_agent-ontology --skill product-domain-engine-organvm-i-theoria
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: product-domain-engine
Source: https://github.com/organvm-i-theoria/_agent-ontology/tree/main/.agents/skills/product-domain-engine
Command: npx skills add https://github.com/organvm-i-theoria/_agent-ontology --skill product-domain-engine-organvm-i-theoria

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? Building a product tied to a domain (chess improvement, public records, wellness, behavioral economics) requires converting informal knowledge into computable structure and then expressing it across technical, credibility, narrative, and timing channels. Without a repeatable system, teams re-derive the same formalization process for every new product. ## Core Features & Use Cases - Five-Phase Formalization Protocol: Sequences identify, map, encode, express, and deploy phases, driving seven existing skills (systemic-product-analyst, market-gap-analysis, research-synthesis-workflow, product-requirements-designer, brand-guidelines, pitch-deck-patterns, content-distribution) without duplicating their content. - Four Rhetorical Modes Framework: Scores every output against logos, ethos, pathos, and kairos using a composition matrix, with a bash audit script (domain-audit.sh) that detects mode presence from repo structure (tests, READMEs, brand directories, roadmaps, git activity). - Stage-Adaptive Prompt Sequences: Orders five build tiers differently for new, theory-heavy, already-deployed, and client-driven domains, plus a seed.yaml template for declaring organ membership and graduation readiness. - Use Case: When launching a new domain product, run the domain audit on any existing repo, select the tier order matching the domain stage, compose the orchestrated skills per tier, and re-audit after each output to close mode gaps. ## Quick Start Ask the agent to run the product domain engine on your domain repo, starting with the domain audit script to score logos, ethos, pathos, and kairos coverage.

Frequently Asked Questions about product-domain-engine

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

FAQPage Schema
How do I formalize a new product domain into a structured system?▼

Run the five-phase protocol: identify the domain's agents, map its structures, encode them as types and tests, express through rhetorical modes, and deploy with analytics. The skill sequences existing skills per tier rather than inventing new patterns.

What does the domain-audit.sh script measure?▼

It scores a repository against four rhetorical modes by detecting structural signals: tests and schemas for logos, flagship READMEs and CI for ethos, brand directories and landing pages for pathos, and roadmaps plus git activity for kairos. It outputs a dominant mode and composition gaps.

When should I use this skill instead of a single-purpose skill?▼

Use it only for systemic product creation tied to a domain. Skip it for pure infrastructure work, stable deployed products, or single-output requests like one landing page, where invoking brand-guidelines or pitch-deck-patterns directly is more appropriate.

Does the prompt sequence order change for different product stages?▼

Yes. New domains run tiers 1 through 5 in order, theory-heavy domains deepen tier 2 research, already-deployed products start at tier 3, and client-driven engagements run expression (tier 4) before architecture (tier 3).

What are the limitations of the domain audit scoring?▼

The script reads structure, not voice or quality, so a pathos-strong product without a brand directory will under-score. Treat results as a coverage check; human judgment is still required to assess whether detected surfaces are dense or hollow.