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co-reference

Loads the Cognitive Orchestration methodology reference covering eight principles, five layers, and six workflow phases.

Updated Apr 2, 2026
Search Tags:#workflow#methodology#reference#guardrails#institutional knowledge#human-ai collaboration#cognitive orchestration
One-click install
npx skills add https://github.com/Dchuuuuuu/disease-risk-classifier --skill co-reference-dchuuuuuu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: co-reference
Source: https://github.com/Dchuuuuuu/disease-risk-classifier/tree/main/.claude/skills/co-reference
Command: npx skills add https://github.com/Dchuuuuuu/disease-risk-classifier --skill co-reference-dchuuuuuu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Teams adopting AI-assisted workflows lack a shared, structured methodology for encoding institutional knowledge, guardrails, and human oversight, leading to inconsistent and untrustworthy AI output. ## Core Features & Use Cases - Methodology Reference: Distills the eight first principles, five-layer architecture, and six-phase workflow model of Cognitive Orchestration (CO) into a single self-contained reference. - Framework Mapping: Explains how CO connects to CARE (human role) and EATP (accountability), and how domain applications like COC, COR, and COG derive from the base methodology. - Use Case: When designing a new domain application of CO or explaining why guardrails must be enforced deterministically outside AI context, load this reference to ground the discussion in the official principles and architecture. ## Quick Start Ask the AI to explain the CO five-layer architecture and how the six-phase workflow maps to domain applications like COC.

Frequently Asked Questions about co-reference

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

FAQPage Schema
What is Cognitive Orchestration (CO) methodology?▼

Cognitive Orchestration is a domain-agnostic methodology for structuring institutional knowledge, guardrails, and processes so AI agents produce trustworthy output under human oversight. It defines eight first principles, a five-layer architecture, and a six-phase workflow model.

What are the five layers of the CO architecture?▼

The five layers are Intent (routing to specialized agents), Context (institutional knowledge), Guardrails (deterministic enforcement), Instructions (structured workflows with approval gates), and Learning (knowledge compounding across sessions).

How does CO relate to CARE and EATP frameworks?▼

CO inherits CARE's Human-on-the-Loop philosophy, mapping trust planes to Context and Guardrails layers. Its guardrails connect to EATP's Constraint Envelopes, approval gates map to Trust Postures, and learning observations become EATP Audit Anchors.

What are the limitations of the CO methodology?▼

CO does not help in truly novel domains lacking institutional knowledge, does not solve the alignment problem, and depends on the quality of knowledge humans provide. Its three failure modes reflect current AI limitations, not permanent boundaries.

What domain applications derive from CO?▼

Domain applications include COC for codegen, COR for research, COE for education, COG for governance, COComp for compliance, and COL for learners. COC is the most mature, with agents, skills, rules, hooks, and commands implementing the six phases.

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