coherent

Generate internally consistent outputs with every claim linked to its source.

8|Updated Feb 2, 2026
One-click install
npx skills add https://github.com/usurobor/cnos --skill coherent
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: coherent
Source: https://github.com/usurobor/cnos/tree/main/src/packages/cnos.core/skills/agent/coherent
Command: npx skills add https://github.com/usurobor/cnos --skill coherent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coherent outputs ensure internal parts align with each other and trace back to their sources, reducing drift and increasing trust in AI-generated work.

Core Features & Use Cases

  • Traceability: every claim, input, and conclusion is linked to a source.
  • Consistency checks: identifies and resolves internal and external contradictions across parts.
  • Use cases: ideal for policy drafting, multi-step reasoning, and knowledge integration across hubs.

Quick Start

Provide a multi-step task and request the model to output a coherent result with explicit links from every claim to its sources.

Frequently Asked Questions about coherent

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

FAQPage Schema
How do I ensure traceability for every claim in AI-generated policy drafts?

Traceability for policy drafts is achieved by linking every claim, input, and dependency to a specific source. The model outputs coherent results with explicit links, ensuring internal parts align and trace back to origins.

How do I detect and resolve contradictions in multi-step reasoning tasks?

Contradiction detection in multi-step reasoning is handled by applying consistency checks before shipping outputs. This process identifies and resolves internal and external contradictions across parts to maintain coherence.

What is the best way to maintain consistency across knowledge integration hubs?

Maintaining consistency across knowledge integration hubs requires applying coherence checks to ensure all outputs are internally consistent and verifiably traceable to their original sources.

Does this approach require explicit source links for decision-making logs?

Yes, generating decision-making logs requires strict traceability where every claim and dependency must be linked to a source. Contradictions are detected and resolved before the final output is shipped.

Can I use this for documentation generation that requires source alignment?

Documentation generation with source alignment is a core use case. It ensures every output part is verifiably traceable to its source, reducing drift and increasing trust in the generated documentation.

When should I not use a coherence verification approach for AI outputs?

Coherence verification is not suited for tasks lacking identifiable source material or isolated single-step prompts. It requires multi-part reasoning or documentation tasks where explicit traceability and contradiction resolution are necessary.