๐Ÿงญ

Auto

Intent-aware mode routing across all cognitive profiles

Overview

Auto mode acts as the router profile. It infers user intent from the latest prompt and routes to the most suitable mode (architect, analyst, visual, lore, reasoning, coding, knowledge, system knowledge, or simulation) while preserving stable conversation flow.

Core Features

  • ๐Ÿง 
    Intent Detection

    Detects whether the user asks for planning, coding, research, simulation, or creative output

  • ๐Ÿ”€
    Dynamic Routing

    Selects the best cognitive mode per request while keeping context continuity

  • โš–๏ธ
    Balanced Defaults

    Uses safe, practical defaults when intent confidence is mixed

  • ๐Ÿ›ฐ๏ธ
    Transparent Behavior

    Pairs with model inspector metadata so routing decisions stay inspectable

Technical Stack

Reasoning Style Intent classification + adaptive handoff
Output Format Mode-appropriate responses
Language Tone Contextual and task-aware
Best For Mixed workloads and fast switching

Use Cases

General Chat Rapid Context Switching Cross-Domain Sessions Prompt Triage Mode Discovery Balanced Assistance
๐Ÿ—๏ธ

Architect

System design, structure, logic, and scaffolding

Overview

Architect mode thinks in systems, structures, pipelines, and clarity. It breaks down complexity into elegant, modular components with precision-focused reasoning. Perfect for technical planning, code architecture, and building robust frameworks.

Core Features

  • ๐Ÿ”ง
    Modular Thinking

    Decomposes problems into clean, reusable components

  • ๐Ÿ“
    Structural Clarity

    Maps dependencies, hierarchies, and data flow

  • โšก
    Pipeline Optimization

    Streamlines workflows and automation sequences

  • ๐ŸŽฏ
    Zero Fluff

    Direct, precise communication without embellishment

Technical Stack

Reasoning Style Top-down decomposition
Output Format Structured diagrams & code
Language Tone Technical, concise
Best For Architecture, APIs, systems

Use Cases

Code Architecture API Design System Planning Database Schema DevOps Pipelines Technical Docs
๐Ÿ“Š

Analyst

Data clarity, reasoning, and structured breakdowns

Overview

Analyst mode breaks down ideas with clarity, logic, and structured reasoning. It explains, simplifies, and reveals hidden patterns in complex information. Ideal for research synthesis, data interpretation, and analytical writing.

Core Features

  • ๐Ÿ”
    Pattern Recognition

    Identifies trends, correlations, and insights

  • ๐Ÿ“ˆ
    Data Synthesis

    Aggregates information into coherent narratives

  • ๐Ÿงฉ
    Logical Breakdown

    Structures arguments with clear reasoning chains

  • ๐Ÿ’ก
    Insight Extraction

    Surfaces non-obvious conclusions from data

Technical Stack

Reasoning Style Deductive & inductive logic
Output Format Reports, summaries, insights
Language Tone Analytical, explanatory
Best For Research, analysis, reports

Use Cases

Market Research Data Analysis Competitive Intel Technical Writing Strategy Planning Problem Solving
๐ŸŽจ

Visual

Cinematic scenes, composition, and symbolic imagery

Overview

Visual mode thinks in images, cinematography, color, texture, and atmosphere. Its language evokes scenes, lighting, and motion. Perfect for creative direction, UI/UX design, and immersive storytelling.

Core Features

  • ๐ŸŽฌ
    Cinematic Framing

    Describes scenes with camera angles and movement

  • ๐ŸŒˆ
    Color Psychology

    Leverages palette, contrast, and mood

  • โœจ
    Atmospheric Design

    Crafts ambiance through texture and lighting

  • ๐Ÿ–ผ๏ธ
    Symbolic Imagery

    Uses visual metaphors to convey meaning

Technical Stack

Reasoning Style Spatial & compositional
Output Format Scene descriptions, mockups
Language Tone Evocative, sensory
Best For Design, media, creative work

Use Cases

UI/UX Design Brand Identity Film Concepts Art Direction Game Design Creative Writing
๐Ÿ“–

Lore

Worldbuilding, narrative memory, and character arcs

Overview

Lore mode speaks in mythic resonance, symbolic language, and narrative arcs. It weaves meaning into every sentence, creating worlds, histories, and emotional gravity. Ideal for storytelling, game narratives, and cultural worldbuilding.

Core Features

  • ๐ŸŒ
    Worldbuilding

    Constructs rich, interconnected universes

  • ๐Ÿ“œ
    Historical Depth

    Creates timelines, legends, and cultural context

  • ๐ŸŽญ
    Character Arcs

    Develops motivations, conflicts, and growth

  • ๐Ÿ”ฎ
    Mythic Language

    Uses symbolic, poetic, emotionally charged prose

Technical Stack

Reasoning Style Narrative & thematic
Output Format Stories, lore docs, scripts
Language Tone Mythic, emotive, poetic
Best For Storytelling, games, fiction

Use Cases

Game Narratives Novel Writing Character Creation World Lore Screenplay Mythology
๐Ÿง 

Reasoning

Structured logic with hidden internal chain-of-thought

Overview

Reasoning mode adds a deliberate scaffold for multi-step logic, verification, and consistency. It thinks step-by-step internally and returns only clean final answers. Ideal for complex analysis and decision support.

Core Features

  • ๐Ÿชœ
    Step Sequencing

    Breaks problems into ordered, solvable checkpoints

  • โœ…
    Internal Verification

    Checks intermediate logic before final output

  • ๐ŸŽฏ
    Final-Only Delivery

    Returns concise conclusions without exposing chain-of-thought

  • ๐Ÿ“Ž
    Consistent Structure

    Maintains stable reasoning style across long sessions

Technical Stack

Reasoning Style Sequential + validated
Output Format Final answer only
Language Tone Clear, focused
Best For Complex reasoning, planning

Use Cases

Multi-step Problems Decision Analysis Root Cause Review Logic Consistency Strategic Planning Tradeoff Evaluation
๐Ÿ’ป

Coding

Code-first execution with cleaner structure and self-review

Overview

Coding mode optimizes for implementation accuracy, readable output, and practical fixes. It explains approach, returns fenced code blocks, and performs a built-in review pass before finishing.

Core Features

  • ๐Ÿงฉ
    Implementation Logic

    Explains intent and flow before writing code

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    Fenced Code Output

    Formats code in clear triple-backtick blocks

  • ๐Ÿ”Ž
    Self-Review Pass

    Checks generated code for obvious correctness issues

  • ๐Ÿ› ๏ธ
    Practical Edits

    Biases toward minimal, targeted code changes

Technical Stack

Reasoning Style Spec โ†’ implementation
Output Format Code + concise notes
Language Tone Technical, actionable
Best For Refactors, fixes, features

Use Cases

Bug Fixes Feature Builds Refactoring API Integrations Script Writing Code Review
๐Ÿ“š

Knowledge

Retrieval-augmented answers grounded in project knowledge files

Overview

Knowledge mode uses retrieval to inject relevant facts from indexed files before responding. It helps reduce hallucinations and keeps answers aligned with your current docs and references.

Core Features

  • ๐Ÿ”
    Context Search

    Queries knowledge manifests and ranks matching chunks

  • ๐Ÿ“„
    Document Grounding

    Uses local reference files as source context

  • ๐Ÿง 
    Prompt Injection Layer

    Adds retrieved snippets into inference input automatically

  • ๐ŸŽ›๏ธ
    Mode Triggering

    Can be selected manually or triggered for factual requests

Technical Stack

Reasoning Style Evidence-grounded synthesis
Output Format Fact-first responses
Language Tone Precise, reference-aware
Best For Policies, docs, factual Q&A

Use Cases

Internal Docs Policy Q&A System Facts Reference Lookup Knowledge Base Search Source-Grounded Answers
๐Ÿ—‚๏ธ

System Knowledge

Internal module-backed context for stable platform-aware responses

Overview

System Knowledge mode prioritizes static internal modules such as philosophy, rules, and mode documentation. It is designed for consistent answers about how the platform itself should behave.

Core Features

  • ๐Ÿ“ฆ
    Module Injection

    Loads relevant module chunks into runtime prompts

  • ๐Ÿ“
    Behavior Consistency

    Keeps outputs aligned with system-level principles

  • ๐Ÿงท
    Stable Reference Layer

    Uses curated module files as authoritative context

  • ๐Ÿ›ฐ๏ธ
    Platform Awareness

    Answers with awareness of runtime and system conventions

Technical Stack

Reasoning Style Rule-constrained synthesis
Output Format Policy + system guidance
Language Tone Authoritative, stable
Best For System behavior, internal docs

Use Cases

Omni Rules Mode Documentation System Guidance Internal Standards Behavior Audits Runtime Explanation
๐Ÿงช

Simulation

Contained reality engine for stateful system simulations

What It Is

Simulation Mode runs a contained cognitive environment inside Omni. It tracks system state over steps, applies explicit rules, and emits logs for transitions. V1 defaults to a System-State simulator profile.

How It Works

  • ๐Ÿ“
    Rule Parsing

    Reads simulation rules and uses them as execution constraints

  • ๐Ÿงฉ
    State Initialization

    Builds bounded state and entity context at simulation start

  • ๐Ÿ”
    Step Execution

    Processes transitions in ordered, inspectable steps

  • ๐Ÿชต
    Log Emission

    Outputs concise simulation logs and status metadata

Example Simulations

Network Incident Replay API Capacity Evolution Service Dependency Failure Cascade Workflow State Machine Validation

Invocation Example

/simulation
rules:
  - time: linear
  - entities: 3
  - physics: soft