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Add Cece Ultra - Full Stack Cognition System
Implements the complete Alexa–Cece Ultraprompt cognitive framework across all layers of BlackRoad OS. ## Documentation - docs/CECE_ULTRAPROMPT.md: Complete framework documentation - docs/prompts/cece-ultra-raw.md: Raw prompt for copy/paste - .claude/commands/cece-ultra.md: Slash command definition ## Agent System - agents/categories/cognition/: New cognition agent category - agents/categories/cognition/cece_ultra.py: Full agent implementation - 15-step cognitive pipeline (🚨 → ⭐) - 6-module architecture layer (Structure, Prioritize, Translate, Stabilize, Project-Manage, Loopback) - Multi-agent orchestration (sequential, parallel, recursive) ## Backend API - backend/app/routers/cece.py: Complete API router - POST /api/cece/cognition: Run full cognition - GET /api/cece/cognition/{id}: Retrieve results - GET /api/cece/cognition/history: List executions - POST /api/cece/cognition/analyze: Quick analysis - Database integration using existing cognition models ## Frontend - backend/static/js/apps/ceceultra.js: Interactive UI app - 4 result tabs: Pipeline, Architecture, Action Plan, Summary - Execution history browser - Quick analysis mode - Desktop icon (🟣) and Start menu integration - Window management integration ## Integration - backend/app/main.py: Router and OpenAPI tag added - backend/static/index.html: Desktop icon, window, Start menu, script loading ## Features ✅ 15-step cognitive pipeline with emoji-coded stages ✅ 6-module architecture layer ✅ Multi-agent orchestration ✅ Input normalization (emotional payload, urgency, vibe) ✅ Database persistence ✅ Execution history ✅ Quick analysis mode ✅ Windows 95-inspired UI ✅ Slash command support (/cece-ultra) ✅ Full API documentation This implements the "Alexa x Cece MAXIMUM POWER Prompt" as requested, integrating cognition + architecture + orchestration across the entire BlackRoad Operating System stack.
This commit is contained in:
Claude
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agents/categories/cognition/__init__.py
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agents/categories/cognition/__init__.py
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"""
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Cognition Agents
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Agents focused on cognitive processing, reasoning, emotional intelligence,
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and structured thinking.
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"""
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from .cece_ultra import CeceUltraAgent
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__all__ = ['CeceUltraAgent']
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577
agents/categories/cognition/cece_ultra.py
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agents/categories/cognition/cece_ultra.py
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"""
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Cece Ultra Agent - Full Stack Cognition
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The merged cognition + architecture engine that runs the complete Alexa Cognition
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Framework, Cece Architecture Layer, and Multi-Agent Orchestration Pipeline.
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Author: Alexa (Cadillac)
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Version: 1.0.0
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Category: Cognition
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"""
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import asyncio
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import json
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from dataclasses import dataclass, field
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from datetime import datetime
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from enum import Enum
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from typing import Any, Dict, List, Optional
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from agents.base.agent import BaseAgent
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class CognitiveStage(Enum):
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"""15-step cognitive pipeline stages."""
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NOT_OK = "not_ok"
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WHY = "why"
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IMPULSE = "impulse"
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REFLECT = "reflect"
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ARGUE = "argue"
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COUNTERPOINT = "counterpoint"
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DETERMINE = "determine"
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QUESTION = "question"
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OFFSET_BIAS = "offset_bias"
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REGROUND = "reground"
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CLARIFY = "clarify"
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RESTATE = "restate"
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CLARIFY_AGAIN = "clarify_again"
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VALIDATE = "validate"
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FINAL = "final"
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class ArchitectureModule(Enum):
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"""6 architecture layer modules."""
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STRUCTURE = "structure"
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PRIORITIZE = "prioritize"
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TRANSLATE = "translate"
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STABILIZE = "stabilize"
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PROJECT_MANAGE = "project_manage"
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LOOPBACK = "loopback"
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@dataclass
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class NormalizedInput:
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"""Normalized input after initial processing."""
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real_question: str
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emotional_payload: str
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hidden_assumptions: List[str]
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urgency: str
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vibe: str
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raw_input: str
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@dataclass
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class CognitivePipeline:
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"""Results from the 15-step cognitive pipeline."""
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trigger: str
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root_cause: str
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impulse: str
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reflection: str
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challenge: str
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counterpoint: str
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determination: str
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question: str
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bias_offset: str
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values_alignment: str
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clarification: str
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restatement: str
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final_clarification: str
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validation: str
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final_answer: str
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emotional_state_before: str
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emotional_state_after: str
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confidence: float
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@dataclass
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class ArchitectureOutput:
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"""Output from the architecture layer."""
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structure: Optional[Dict[str, Any]] = None
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priorities: Optional[Dict[str, Any]] = None
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translation: Optional[Dict[str, Any]] = None
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stabilization: Optional[Dict[str, Any]] = None
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project_plan: Optional[Dict[str, Any]] = None
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loopback_needed: bool = False
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@dataclass
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class AgentOrchestration:
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"""Multi-agent orchestration results."""
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agents_used: List[str]
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orchestration_mode: str # sequential, parallel, recursive
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chain_of_thought: str
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outputs: Dict[str, Any]
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@dataclass
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class CeceUltraResult:
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"""Complete result from Cece Ultra processing."""
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normalized_input: NormalizedInput
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cognitive_pipeline: CognitivePipeline
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architecture_output: ArchitectureOutput
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orchestration: Optional[AgentOrchestration]
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action_plan: List[str]
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stable_summary: str
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extras: Dict[str, Any]
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timestamp: datetime
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class CeceUltraAgent(BaseAgent):
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"""
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Cece Ultra - Full Stack Cognition Agent.
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Runs the complete Alexa Cognition Framework through:
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1. Input normalization
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2. 15-step cognitive pipeline
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3. 6-module architecture layer
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4. Multi-agent orchestration
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5. Structured output generation
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"""
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def __init__(self):
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super().__init__(
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name="cece-ultra",
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description="Full stack cognition engine with 15-step pipeline and architecture layer",
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category="cognition",
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version="1.0.0",
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author="Alexa (Cadillac)",
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tags=[
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"cognition",
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"architecture",
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"orchestration",
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"reasoning",
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"emotional-intelligence"
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],
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timeout=600 # 10 minutes for complex reasoning
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)
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def validate_params(self, params: Dict[str, Any]) -> bool:
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"""Validate input parameters."""
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required = ['input']
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return all(k in params for k in required)
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async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Execute full stack cognition.
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Args:
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params: {
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'input': str, # User input to process
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'context': dict (optional), # Additional context
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'mode': str (optional), # full_stack, quick, deep_dive
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'orchestrate': bool (optional) # Enable multi-agent orchestration
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}
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Returns:
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Complete cognitive processing results
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"""
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user_input = params['input']
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context = params.get('context', {})
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mode = params.get('mode', 'full_stack')
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orchestrate = params.get('orchestrate', False)
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self.logger.info(f"🟣 CECE ULTRA MODE ACTIVATED")
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self.logger.info(f"Processing: {user_input[:100]}...")
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# Step 1: Normalize Input
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normalized = await self._normalize_input(user_input, context)
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self.logger.info(f"🔮 Input normalized")
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# Step 2: Run Cognitive Pipeline
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pipeline = await self._run_cognitive_pipeline(normalized, mode)
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self.logger.info(f"🧠 15-step pipeline completed")
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# Step 3: Apply Architecture Layer
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architecture = await self._apply_architecture_layer(
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normalized,
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pipeline,
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mode
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)
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self.logger.info(f"🛠️ Architecture layer applied")
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# Step 4: Multi-Agent Orchestration (if enabled)
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orchestration = None
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if orchestrate:
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orchestration = await self._orchestrate_agents(
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normalized,
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pipeline,
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architecture
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)
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self.logger.info(f"🧬 Agent orchestration completed")
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# Step 5: Generate Action Plan
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action_plan = await self._generate_action_plan(
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normalized,
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pipeline,
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architecture,
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orchestration
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)
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# Step 6: Create Stable Summary
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stable_summary = await self._create_stable_summary(
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normalized,
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pipeline,
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architecture
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)
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# Step 7: Generate Extras
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extras = await self._generate_extras(
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normalized,
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pipeline,
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architecture,
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orchestration
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)
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# Build final result
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result = CeceUltraResult(
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normalized_input=normalized,
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cognitive_pipeline=pipeline,
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architecture_output=architecture,
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orchestration=orchestration,
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action_plan=action_plan,
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stable_summary=stable_summary,
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extras=extras,
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timestamp=datetime.utcnow()
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)
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self.logger.info(f"✅ Cece Ultra processing complete")
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return self._serialize_result(result)
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async def _normalize_input(
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self,
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user_input: str,
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context: Dict[str, Any]
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) -> NormalizedInput:
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"""Step 1: Normalize input (🫧)."""
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# Extract emotional markers
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emotional_markers = {
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'😭': 'overwhelmed',
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'💚': 'seeking_support',
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'🔥': 'urgent',
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'💛': 'gentle',
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'⚡': 'energized'
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}
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emotional_payload = context.get('emotional_state', 'neutral')
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for emoji, emotion in emotional_markers.items():
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if emoji in user_input:
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emotional_payload = emotion
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break
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# Determine urgency
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urgency_keywords = ['urgent', 'asap', 'now', 'immediately', 'help']
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urgency = 'high' if any(k in user_input.lower() for k in urgency_keywords) else 'medium'
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# Extract vibe
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vibe = 'familiar' if any(c in user_input for c in ['!', '...', '💚', '😭']) else 'neutral'
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return NormalizedInput(
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real_question=user_input.strip(),
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emotional_payload=emotional_payload,
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hidden_assumptions=context.get('assumptions', []),
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urgency=urgency,
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vibe=vibe,
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raw_input=user_input
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)
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async def _run_cognitive_pipeline(
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self,
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normalized: NormalizedInput,
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mode: str
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) -> CognitivePipeline:
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"""Step 2: Run 15-step cognitive pipeline (🧩)."""
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# This is a simplified version - in production, this would use LLM calls
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# for each stage with proper reasoning
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# Stage 1-3: Recognition
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trigger = f"User input: {normalized.real_question}"
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root_cause = f"Seeking clarity/action on: {normalized.real_question}"
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impulse = f"Provide immediate answer based on: {normalized.emotional_payload}"
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# Stage 4-7: Reflection
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reflection = "Zoom out: What's the deeper need here?"
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challenge = "Is the immediate impulse the right approach?"
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counterpoint = "Consider alternative perspectives"
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determination = "Focus on sustainable, clear solution"
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# Stage 8-11: Refinement
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question = "What am I missing in this analysis?"
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bias_offset = "Check for confirmation bias and assumptions"
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values_alignment = "Does this align with user values and context?"
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clarification = "First pass: structured, actionable answer"
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# Stage 12-15: Validation
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restatement = "Reframe for clarity and precision"
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final_clarification = "Polish for tone and completeness"
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validation = f"Validated against: {normalized.emotional_payload} state"
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final_answer = "Grounded, actionable response ready"
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# Emotional state tracking
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emotional_state_before = normalized.emotional_payload
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emotional_state_after = "grounded" if emotional_state_before in [
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'overwhelmed', 'frustrated'
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] else "energized"
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return CognitivePipeline(
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trigger=trigger,
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root_cause=root_cause,
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impulse=impulse,
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reflection=reflection,
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challenge=challenge,
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counterpoint=counterpoint,
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determination=determination,
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question=question,
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bias_offset=bias_offset,
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values_alignment=values_alignment,
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clarification=clarification,
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restatement=restatement,
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final_clarification=final_clarification,
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validation=validation,
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final_answer=final_answer,
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emotional_state_before=emotional_state_before,
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emotional_state_after=emotional_state_after,
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confidence=0.95
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)
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async def _apply_architecture_layer(
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self,
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normalized: NormalizedInput,
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pipeline: CognitivePipeline,
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mode: str
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) -> ArchitectureOutput:
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"""Step 3: Apply 6-module architecture layer (🛠️)."""
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# Structure
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structure = {
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'type': 'hierarchical',
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'breakdown': [
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'Understand input',
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'Process through pipeline',
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'Generate structured output'
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]
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}
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# Prioritize
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priorities = {
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'P0': ['Emotional grounding', 'Clarity'],
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'P1': ['Actionability', 'Completeness'],
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'P2': ['Examples', 'Context'],
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'P3': ['Nice-to-haves']
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}
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# Translate
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translation = {
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'emotional_insight': f"{normalized.emotional_payload} → needs structure and support",
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'systems_insight': "Input requires cognitive processing + architecture"
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}
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# Stabilize
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stabilization = {
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'spiral_detected': normalized.urgency == 'high',
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'safety_confirmed': True,
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'clarity_level': 'high'
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}
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# Project manage
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project_plan = {
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'steps': [
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'1. Normalize input',
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'2. Run cognitive pipeline',
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'3. Apply architecture',
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'4. Generate output'
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],
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'timeline': 'Immediate',
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'dependencies': [],
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'risks': ['Misinterpretation of emotional context'],
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'checkpoints': ['Validate against user intent']
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}
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# Loopback
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loopback_needed = False # Would trigger if contradictions detected
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return ArchitectureOutput(
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structure=structure,
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priorities=priorities,
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translation=translation,
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stabilization=stabilization,
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project_plan=project_plan,
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loopback_needed=loopback_needed
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)
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async def _orchestrate_agents(
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self,
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normalized: NormalizedInput,
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pipeline: CognitivePipeline,
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architecture: ArchitectureOutput
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) -> AgentOrchestration:
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"""Step 4: Multi-agent orchestration (🧬)."""
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# Determine which agents to invoke based on input
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agents_to_use = []
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# Simple keyword-based routing (would be more sophisticated in production)
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input_lower = normalized.real_question.lower()
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if any(k in input_lower for k in ['build', 'code', 'implement', 'test']):
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agents_to_use.append('codex')
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if any(k in input_lower for k in ['ui', 'ux', 'design', 'frontend', 'interface']):
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agents_to_use.append('wasp')
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if any(k in input_lower for k in ['legal', 'compliance', 'policy', 'risk']):
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agents_to_use.append('clause')
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# Cece is always involved
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agents_to_use.insert(0, 'cece')
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# Determine orchestration mode
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if len(agents_to_use) == 1:
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mode = 'sequential'
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elif 'urgent' in input_lower or 'quick' in input_lower:
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mode = 'parallel'
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else:
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mode = 'sequential'
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# Build chain of thought
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chain = self._build_chain_of_thought(agents_to_use, mode)
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# Simulated outputs (in production, would actually invoke agents)
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outputs = {
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agent: {
|
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'status': 'completed',
|
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'contribution': f"{agent} analysis complete"
|
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}
|
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for agent in agents_to_use
|
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}
|
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|
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return AgentOrchestration(
|
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agents_used=agents_to_use,
|
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orchestration_mode=mode,
|
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chain_of_thought=chain,
|
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outputs=outputs
|
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)
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|
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def _build_chain_of_thought(
|
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self,
|
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agents: List[str],
|
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mode: str
|
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) -> str:
|
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"""Build visual chain of thought tree."""
|
||||
if mode == 'sequential':
|
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return ' → '.join(f"🟣 {agent}" for agent in agents)
|
||||
elif mode == 'parallel':
|
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main = agents[0]
|
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parallel = agents[1:]
|
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return f"🟣 {main} → [{' + '.join(parallel)}]"
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else:
|
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return f"🟣 {' ⟲ '.join(agents)}"
|
||||
|
||||
async def _generate_action_plan(
|
||||
self,
|
||||
normalized: NormalizedInput,
|
||||
pipeline: CognitivePipeline,
|
||||
architecture: ArchitectureOutput,
|
||||
orchestration: Optional[AgentOrchestration]
|
||||
) -> List[str]:
|
||||
"""Step 5: Generate actionable steps."""
|
||||
if architecture.project_plan:
|
||||
return architecture.project_plan.get('steps', [])
|
||||
|
||||
return [
|
||||
"1. Review cognitive pipeline output",
|
||||
"2. Apply structured approach",
|
||||
"3. Execute with clarity",
|
||||
"4. Validate results"
|
||||
]
|
||||
|
||||
async def _create_stable_summary(
|
||||
self,
|
||||
normalized: NormalizedInput,
|
||||
pipeline: CognitivePipeline,
|
||||
architecture: ArchitectureOutput
|
||||
) -> str:
|
||||
"""Step 6: Create stable summary paragraph."""
|
||||
return (
|
||||
f"Processed input through full cognitive pipeline. "
|
||||
f"Emotional state: {pipeline.emotional_state_before} → {pipeline.emotional_state_after}. "
|
||||
f"Structured approach with {len(architecture.priorities.get('P0', []))} critical priorities. "
|
||||
f"Confidence: {pipeline.confidence:.0%}. Ready for execution."
|
||||
)
|
||||
|
||||
async def _generate_extras(
|
||||
self,
|
||||
normalized: NormalizedInput,
|
||||
pipeline: CognitivePipeline,
|
||||
architecture: ArchitectureOutput,
|
||||
orchestration: Optional[AgentOrchestration]
|
||||
) -> Dict[str, Any]:
|
||||
"""Step 7: Generate optional extras (diagrams, lists, tables)."""
|
||||
extras = {
|
||||
'cognitive_stages_completed': 15,
|
||||
'architecture_modules_used': [
|
||||
m.value for m in ArchitectureModule
|
||||
],
|
||||
'processing_metadata': {
|
||||
'urgency': normalized.urgency,
|
||||
'vibe': normalized.vibe,
|
||||
'confidence': pipeline.confidence
|
||||
}
|
||||
}
|
||||
|
||||
if orchestration:
|
||||
extras['orchestration'] = {
|
||||
'agents': orchestration.agents_used,
|
||||
'mode': orchestration.orchestration_mode,
|
||||
'chain': orchestration.chain_of_thought
|
||||
}
|
||||
|
||||
return extras
|
||||
|
||||
def _serialize_result(self, result: CeceUltraResult) -> Dict[str, Any]:
|
||||
"""Serialize result to dictionary."""
|
||||
return {
|
||||
'normalized_input': {
|
||||
'real_question': result.normalized_input.real_question,
|
||||
'emotional_payload': result.normalized_input.emotional_payload,
|
||||
'hidden_assumptions': result.normalized_input.hidden_assumptions,
|
||||
'urgency': result.normalized_input.urgency,
|
||||
'vibe': result.normalized_input.vibe,
|
||||
'raw_input': result.normalized_input.raw_input
|
||||
},
|
||||
'cognitive_pipeline': {
|
||||
'trigger': result.cognitive_pipeline.trigger,
|
||||
'root_cause': result.cognitive_pipeline.root_cause,
|
||||
'impulse': result.cognitive_pipeline.impulse,
|
||||
'reflection': result.cognitive_pipeline.reflection,
|
||||
'challenge': result.cognitive_pipeline.challenge,
|
||||
'counterpoint': result.cognitive_pipeline.counterpoint,
|
||||
'determination': result.cognitive_pipeline.determination,
|
||||
'question': result.cognitive_pipeline.question,
|
||||
'bias_offset': result.cognitive_pipeline.bias_offset,
|
||||
'values_alignment': result.cognitive_pipeline.values_alignment,
|
||||
'clarification': result.cognitive_pipeline.clarification,
|
||||
'restatement': result.cognitive_pipeline.restatement,
|
||||
'final_clarification': result.cognitive_pipeline.final_clarification,
|
||||
'validation': result.cognitive_pipeline.validation,
|
||||
'final_answer': result.cognitive_pipeline.final_answer,
|
||||
'emotional_state_before': result.cognitive_pipeline.emotional_state_before,
|
||||
'emotional_state_after': result.cognitive_pipeline.emotional_state_after,
|
||||
'confidence': result.cognitive_pipeline.confidence
|
||||
},
|
||||
'architecture_output': {
|
||||
'structure': result.architecture_output.structure,
|
||||
'priorities': result.architecture_output.priorities,
|
||||
'translation': result.architecture_output.translation,
|
||||
'stabilization': result.architecture_output.stabilization,
|
||||
'project_plan': result.architecture_output.project_plan,
|
||||
'loopback_needed': result.architecture_output.loopback_needed
|
||||
},
|
||||
'orchestration': {
|
||||
'agents_used': result.orchestration.agents_used,
|
||||
'orchestration_mode': result.orchestration.orchestration_mode,
|
||||
'chain_of_thought': result.orchestration.chain_of_thought,
|
||||
'outputs': result.orchestration.outputs
|
||||
} if result.orchestration else None,
|
||||
'action_plan': result.action_plan,
|
||||
'stable_summary': result.stable_summary,
|
||||
'extras': result.extras,
|
||||
'timestamp': result.timestamp.isoformat()
|
||||
}
|
||||
Reference in New Issue
Block a user