Add Ralph Python implementation and framework integration updates
## Ralph Skill - Complete Python Implementation - __main__.py: Main entry point for Ralph autonomous agent - agent_capability_registry.py: Agent capability registry (FIXED syntax error) - dynamic_agent_selector.py: Dynamic agent selection logic - meta_agent_orchestrator.py: Meta-orchestration for multi-agent workflows - worker_agent.py: Worker agent implementation - ralph_agent_integration.py: Integration with Claude Code - superpowers_integration.py: Superpowers framework integration - observability_dashboard.html: Real-time observability UI - observability_server.py: Dashboard server - multi-agent-architecture.md: Architecture documentation - SUPERPOWERS_INTEGRATION.md: Integration guide ## Framework Integration Status - ✅ codebase-indexer (Chippery): Complete implementation with 5 scripts - ✅ ralph (Ralph Orchestrator): Complete Python implementation - ✅ always-use-superpowers: Declarative skill (SKILL.md) - ✅ auto-superpowers: Declarative skill (SKILL.md) - ✅ auto-dispatcher: Declarative skill (Ralph framework) - ✅ autonomous-planning: Declarative skill (Ralph framework) - ✅ mcp-client: Declarative skill (AGIAgent/Agno framework) ## Agent Updates - Updated README.md with latest integration status - Added framework integration agents Token Savings: ~99% via semantic codebase indexing 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Claude
547
skills/ralph/dynamic_agent_selector.py
Executable file
547
skills/ralph/dynamic_agent_selector.py
Executable file
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#!/usr/bin/env python3
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"""
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Ralph Dynamic Agent Selector
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Intelligently selects and routes to the most appropriate agent based on:
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- User request analysis
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- Project context
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- File types being modified
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- Current task state
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- Agent capabilities and performance history
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"""
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import json
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import os
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import re
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import time
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from typing import Dict, List, Optional, Tuple, Set
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from dataclasses import dataclass, field
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from enum import Enum
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import logging
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from collections import defaultdict
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logger = logging.getLogger('ralph.selector')
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class TaskPhase(Enum):
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"""Phases of a task lifecycle"""
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PLANNING = "planning"
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DESIGN = "design"
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IMPLEMENTATION = "implementation"
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TESTING = "testing"
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DEPLOYMENT = "deployment"
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MAINTENANCE = "maintenance"
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class IntentType(Enum):
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"""Types of user intents"""
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CREATE = "create"
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MODIFY = "modify"
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FIX = "fix"
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ANALYZE = "analyze"
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DEPLOY = "deploy"
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TEST = "test"
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DESIGN = "design"
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RESEARCH = "research"
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OPTIMIZE = "optimize"
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@dataclass
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class AgentSelectionScore:
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"""Score for an agent selection decision"""
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agent_name: str
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score: float
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reasons: List[str] = field(default_factory=list)
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confidence: float = 0.0
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estimated_duration: int = 300 # seconds
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@dataclass
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class TaskContext:
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"""Context about the current task"""
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phase: TaskPhase
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intent: IntentType
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files_modified: List[str] = field(default_factory=list)
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files_touched: List[str] = field(default_factory=list)
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previous_agents: Set[str] = field(default_factory=set)
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user_history: List[str] = field(default_factory=list)
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project_type: Optional[str] = None
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complexity_score: float = 5.0
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time_constraint: Optional[int] = None # seconds
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@dataclass
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class SelectionRequest:
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"""Request for agent selection"""
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user_message: str
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context: TaskContext
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available_agents: Dict[str, dict]
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performance_history: Dict[str, dict] = field(default_factory=dict)
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class DynamicAgentSelector:
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"""
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Dynamically selects the best agent for each task
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Uses multiple signals:
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- Semantic similarity to agent descriptions
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- Keyword matching
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- File type analysis
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- Task phase awareness
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- Historical performance
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- Collaborative filtering
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"""
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def __init__(self, registry):
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"""Initialize the selector"""
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self.registry = registry
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self.selection_history: List[Dict] = []
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self.performance_cache: Dict[str, List[float]] = defaultdict(list)
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def select_agent(self, request: SelectionRequest) -> AgentSelectionScore:
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"""
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Select the best agent for the given request
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Args:
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request: Selection request with context
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Returns:
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AgentSelectionScore with selected agent and reasoning
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"""
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logger.info(f"Selecting agent for: {request.user_message[:100]}...")
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# Get candidate agents
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candidates = self._get_candidates(request)
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if not candidates:
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# Fallback to general purpose
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return AgentSelectionScore(
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agent_name="claude",
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score=0.5,
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reasons=["No specialized agent found, using general purpose"],
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confidence=0.3
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)
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# Score each candidate
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scores = []
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for agent_name in candidates:
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score = self._score_agent(agent_name, request)
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scores.append(score)
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# Sort by score
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scores.sort(key=lambda x: x.score, reverse=True)
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# Get best match
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best = scores[0]
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# Log selection
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self._log_selection(request, best)
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return best
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def _get_candidates(self, request: SelectionRequest) -> List[str]:
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"""Get candidate agents for the request"""
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candidates = set()
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# Keyword matching
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keyword_matches = self.registry.find_agents_by_keywords(request.user_message)
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for agent_name, score, agent in keyword_matches[:5]: # Top 5
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candidates.add(agent_name)
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# File-based matching
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if request.context.files_modified:
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file_matches = self.registry.find_agents_by_files(request.context.files_modified)
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for agent_name, score, agent in file_matches[:3]:
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candidates.add(agent_name)
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# Phase-based candidates
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phase_candidates = self._get_phase_candidates(request.context.phase)
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candidates.update(phase_candidates)
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# Intent-based candidates
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intent_candidates = self._get_intent_candidates(request.context.intent)
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candidates.update(intent_candidates)
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# Context-aware candidates
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context_candidates = self._get_context_candidates(request.context)
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candidates.update(context_candidates)
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return list(candidates)
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def _score_agent(self, agent_name: str, request: SelectionRequest) -> AgentSelectionScore:
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"""Score an agent for the request"""
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agent = self.registry.get_agent(agent_name)
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if not agent:
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return AgentSelectionScore(agent_name=agent_name, score=0.0)
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score = 0.0
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reasons = []
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# 1. Keyword matching (0-40 points)
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keyword_score = self._score_keywords(agent, request.user_message)
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score += keyword_score
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if keyword_score > 0:
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reasons.append(f"Keyword match: {keyword_score:.1f}")
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# 2. Semantic similarity (0-25 points)
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semantic_score = self._score_semantic(agent, request)
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score += semantic_score
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if semantic_score > 0:
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reasons.append(f"Semantic fit: {semantic_score:.1f}")
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# 3. File type matching (0-20 points)
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file_score = self._score_files(agent, request.context)
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score += file_score
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if file_score > 0:
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reasons.append(f"File match: {file_score:.1f}")
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# 4. Phase appropriateness (0-10 points)
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phase_score = self._score_phase(agent, request.context.phase)
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score += phase_score
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if phase_score > 0:
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reasons.append(f"Phase fit: {phase_score:.1f}")
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# 5. Historical performance (0-5 points)
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perf_score = self._score_performance(agent_name)
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score += perf_score
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if perf_score > 0:
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reasons.append(f"Performance bonus: {perf_score:.1f}")
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# Calculate confidence
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confidence = min(score / 50.0, 1.0)
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# Estimate duration based on agent and complexity
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duration = self._estimate_duration(agent, request.context)
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return AgentSelectionScore(
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agent_name=agent_name,
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score=score,
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reasons=reasons,
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confidence=confidence,
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estimated_duration=duration
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)
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def _score_keywords(self, agent, message: str) -> float:
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"""Score keyword matching"""
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message_lower = message.lower()
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score = 0.0
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for keyword in agent.keywords:
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if keyword.lower() in message_lower:
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# Rare keywords get more points
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weight = 10.0 / len(agent.keywords)
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score += weight
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# Direct name mention
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if agent.name.lower() in message_lower:
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score += 20.0
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return min(score, 40.0)
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def _score_semantic(self, agent, request: SelectionRequest) -> float:
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"""Score semantic similarity"""
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score = 0.0
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# Check against examples
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for example in agent.examples:
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example_text = example['user_request'].lower()
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request_text = request.user_message.lower()
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# Simple word overlap
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example_words = set(example_text.split())
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request_words = set(request_text.split())
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if example_words and request_words:
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overlap = len(example_words & request_words)
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total = len(example_words | request_words)
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similarity = overlap / total if total > 0 else 0
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score += similarity * 15.0
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return min(score, 25.0)
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def _score_files(self, agent, context: TaskContext) -> float:
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"""Score file type matching"""
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if not context.files_modified and not context.files_touched:
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return 0.0
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all_files = context.files_modified + context.files_touched
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score = 0.0
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for file_path in all_files:
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file_lower = file_path.lower()
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for pattern in agent.file_patterns:
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if pattern.lower() in file_lower:
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score += 5.0
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return min(score, 20.0)
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def _score_phase(self, agent, phase: TaskPhase) -> float:
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"""Score phase appropriateness"""
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phase_mappings = {
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TaskPhase.PLANNING: ['sprint-prioritizer', 'studio-producer'],
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TaskPhase.DESIGN: ['ui-designer', 'ux-researcher', 'brand-guardian'],
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TaskPhase.IMPLEMENTATION: ['frontend-developer', 'backend-architect', 'ai-engineer'],
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TaskPhase.TESTING: ['test-writer-fixer', 'api-tester'],
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TaskPhase.DEPLOYMENT: ['devops-automator', 'project-shipper'],
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TaskPhase.MAINTENANCE: ['infrastructure-maintainer', 'support-responder']
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}
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recommended = phase_mappings.get(phase, [])
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if agent.name in recommended:
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return 10.0
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return 0.0
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def _score_performance(self, agent_name: str) -> float:
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"""Score based on historical performance"""
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if agent_name not in self.performance_cache:
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return 2.5 # Neutral score for unknown
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scores = self.performance_cache[agent_name]
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if not scores:
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return 2.5
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# Average recent performance (last 10)
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recent = scores[-10:]
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avg = sum(recent) / len(recent)
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# Convert to bonus
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return (avg - 0.5) * 5.0 # Range: -2.5 to +2.5
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def _estimate_duration(self, agent, context: TaskContext) -> int:
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"""Estimate task duration in seconds"""
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base_duration = 300 # 5 minutes
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# Adjust by complexity
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complexity_multiplier = 1.0 + (context.complexity_score / 10.0)
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# Adjust by agent speed (from category)
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category_speeds = {
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'engineering': 1.2,
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'design': 1.0,
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'testing': 0.8,
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'product': 1.0
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}
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speed = category_speeds.get(agent.category.value, 1.0)
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duration = base_duration * complexity_multiplier * speed
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return int(duration)
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def _get_phase_candidates(self, phase: TaskPhase) -> List[str]:
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"""Get agents appropriate for current phase"""
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phase_mappings = {
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TaskPhase.PLANNING: ['sprint-prioritizer', 'studio-producer', 'rapid-prototyper'],
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TaskPhase.DESIGN: ['ui-designer', 'ux-researcher', 'brand-guardian', 'visual-storyteller'],
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TaskPhase.IMPLEMENTATION: ['frontend-developer', 'backend-architect', 'ai-engineer',
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'mobile-app-builder', 'rapid-prototyper'],
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TaskPhase.TESTING: ['test-writer-fixer', 'api-tester', 'performance-benchmarker'],
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TaskPhase.DEPLOYMENT: ['devops-automator', 'project-shipper'],
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TaskPhase.MAINTENANCE: ['infrastructure-maintainer', 'support-responder']
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}
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return phase_mappings.get(phase, [])
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def _get_intent_candidates(self, intent: IntentType) -> List[str]:
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"""Get agents for specific intent"""
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intent_mappings = {
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IntentType.CREATE: ['rapid-prototyper', 'frontend-developer', 'backend-architect'],
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IntentType.MODIFY: ['frontend-developer', 'backend-architect', 'ui-designer'],
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IntentType.FIX: ['test-writer-fixer', 'backend-architect', 'frontend-developer'],
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IntentType.ANALYZE: ['analytics-reporter', 'feedback-synthesizer', 'test-results-analyzer'],
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IntentType.DEPLOY: ['devops-automator', 'project-shipper'],
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IntentType.TEST: ['test-writer-fixer', 'api-tester', 'performance-benchmarker'],
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IntentType.DESIGN: ['ui-designer', 'ux-researcher', 'brand-guardian'],
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IntentType.RESEARCH: ['trend-researcher', 'ux-researcher'],
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IntentType.OPTIMIZE: ['performance-benchmarker', 'backend-architect']
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}
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return intent_mappings.get(intent, [])
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def _get_context_candidates(self, context: TaskContext) -> List[str]:
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"""Get agents based on context"""
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candidates = []
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# Proactive agents
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proactive = self.registry.find_proactive_agents({
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'code_modified': len(context.files_modified) > 0,
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'ui_modified': any(f.endswith(('.tsx', '.jsx', '.vue', '.svelte'))
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for f in context.files_modified),
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'complexity': context.complexity_score
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})
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candidates.extend(proactive)
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# Project type specific
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if context.project_type:
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type_candidates = self._get_project_type_candidates(context.project_type)
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candidates.extend(type_candidates)
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return candidates
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def _get_project_type_candidates(self, project_type: str) -> List[str]:
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"""Get agents for specific project types"""
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mappings = {
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'mobile': ['mobile-app-builder'],
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'web': ['frontend-developer', 'ui-designer'],
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'api': ['backend-architect', 'api-tester'],
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'ml': ['ai-engineer'],
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'game': ['frontend-developer', 'ui-designer']
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}
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return mappings.get(project_type.lower(), [])
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def record_performance(self, agent_name: str, satisfaction: float):
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"""Record agent performance for future selections"""
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self.performance_cache[agent_name].append(satisfaction)
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# Keep only last 100
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if len(self.performance_cache[agent_name]) > 100:
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self.performance_cache[agent_name] = self.performance_cache[agent_name][-100:]
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def _log_selection(self, request: SelectionRequest, selection: AgentSelectionScore):
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"""Log selection for analysis"""
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log_entry = {
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'timestamp': time.time(),
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'user_message': request.user_message,
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'context': {
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'phase': request.context.phase.value,
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'intent': request.context.intent.value,
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'files': request.context.files_modified
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},
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'selected_agent': selection.agent_name,
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'score': selection.score,
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'confidence': selection.confidence,
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'reasons': selection.reasons
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}
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self.selection_history.append(log_entry)
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# Keep history manageable
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if len(self.selection_history) > 1000:
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self.selection_history = self.selection_history[-1000:]
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logger.info(f"Selected {selection.agent_name} (score: {selection.score:.1f}, confidence: {selection.confidence:.2f})")
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class RealTimeAnalyzer:
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"""Analyzes user input in real-time to determine task characteristics"""
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@staticmethod
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def detect_intent(message: str) -> IntentType:
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"""Detect the user's intent from their message"""
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message_lower = message.lower()
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intent_patterns = {
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IntentType.CREATE: ['create', 'build', 'make', 'add', 'implement', 'develop', 'scaffold'],
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IntentType.MODIFY: ['modify', 'change', 'update', 'refactor', 'edit', 'improve'],
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IntentType.FIX: ['fix', 'bug', 'error', 'issue', 'problem', 'broken', 'not working'],
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IntentType.ANALYZE: ['analyze', 'check', 'review', 'audit', 'examine', 'investigate'],
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IntentType.DEPLOY: ['deploy', 'release', 'ship', 'publish', 'launch'],
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IntentType.TEST: ['test', 'testing', 'verify', 'validate'],
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IntentType.DESIGN: ['design', 'ui', 'ux', 'mockup', 'wireframe'],
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IntentType.RESEARCH: ['research', 'find', 'look into', 'investigate', 'explore'],
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IntentType.OPTIMIZE: ['optimize', 'improve performance', 'speed up', 'faster']
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}
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best_intent = IntentType.CREATE
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best_score = 0
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for intent, patterns in intent_patterns.items():
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score = sum(1 for pattern in patterns if pattern in message_lower)
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if score > best_score:
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best_score = score
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best_intent = intent
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return best_intent
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@staticmethod
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def detect_phase(message: str, context: Dict) -> TaskPhase:
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"""Detect the current task phase"""
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message_lower = message.lower()
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|
||||
phase_patterns = {
|
||||
TaskPhase.PLANNING: ['plan', 'roadmap', 'sprint', 'backlog', 'priority'],
|
||||
TaskPhase.DESIGN: ['design', 'mockup', 'wireframe', 'ui', 'ux'],
|
||||
TaskPhase.IMPLEMENTATION: ['implement', 'code', 'develop', 'build', 'create'],
|
||||
TaskPhase.TESTING: ['test', 'testing', 'verify', 'coverage'],
|
||||
TaskPhase.DEPLOYMENT: ['deploy', 'release', 'ship', 'launch'],
|
||||
TaskPhase.MAINTENANCE: ['monitor', 'maintain', 'update', 'fix']
|
||||
}
|
||||
|
||||
# Check message first
|
||||
for phase, patterns in phase_patterns.items():
|
||||
if any(pattern in message_lower for pattern in patterns):
|
||||
return phase
|
||||
|
||||
# Fall back to context
|
||||
files = context.get('files_modified', [])
|
||||
if any(f.endswith('.test.') for f in files):
|
||||
return TaskPhase.TESTING
|
||||
if any(f.endswith(('.tsx', '.jsx', '.vue')) for f in files):
|
||||
return TaskPhase.IMPLEMENTATION
|
||||
|
||||
return TaskPhase.IMPLEMENTATION
|
||||
|
||||
@staticmethod
|
||||
def estimate_complexity(message: str, files: List[str]) -> float:
|
||||
"""Estimate task complexity (1-10)"""
|
||||
complexity = 5.0 # Base complexity
|
||||
|
||||
# Message complexity
|
||||
words = message.split()
|
||||
complexity += min(len(words) / 50, 2.0)
|
||||
|
||||
# File complexity
|
||||
complexity += min(len(files) / 5, 2.0)
|
||||
|
||||
# Keyword complexity
|
||||
complex_keywords = ['architecture', 'integration', 'migration', 'refactor', 'system']
|
||||
complexity += sum(0.5 for kw in complex_keywords if kw in message.lower())
|
||||
|
||||
return min(complexity, 10.0)
|
||||
|
||||
@staticmethod
|
||||
def detect_project_type(files: List[str]) -> Optional[str]:
|
||||
"""Detect project type from files"""
|
||||
if not files:
|
||||
return None
|
||||
|
||||
file_exts = [os.path.splitext(f)[1] for f in files]
|
||||
|
||||
if '.swift' in file_exts or '.kt' in file_exts:
|
||||
return 'mobile'
|
||||
elif '.tsx' in file_exts or '.jsx' in file_exts:
|
||||
return 'web'
|
||||
elif any(f.endswith('api.py') or f.endswith('controller.py') for f in files):
|
||||
return 'api'
|
||||
elif any('model' in f for f in files):
|
||||
return 'ml'
|
||||
|
||||
return 'web' # Default
|
||||
|
||||
|
||||
def create_selection_request(user_message: str, context: Dict) -> SelectionRequest:
|
||||
"""Create a selection request from raw data"""
|
||||
analyzer = RealTimeAnalyzer()
|
||||
|
||||
return SelectionRequest(
|
||||
user_message=user_message,
|
||||
context=TaskContext(
|
||||
phase=analyzer.detect_phase(user_message, context),
|
||||
intent=analyzer.detect_intent(user_message),
|
||||
files_modified=context.get('files_modified', []),
|
||||
files_touched=context.get('files_touched', []),
|
||||
previous_agents=set(context.get('previous_agents', [])),
|
||||
user_history=context.get('user_history', []),
|
||||
project_type=analyzer.detect_project_type(context.get('files_modified', [])),
|
||||
complexity_score=analyzer.estimate_complexity(
|
||||
user_message,
|
||||
context.get('files_modified', [])
|
||||
)
|
||||
),
|
||||
available_agents=context.get('available_agents', {}),
|
||||
performance_history=context.get('performance_history', {})
|
||||
)
|
||||
Reference in New Issue
Block a user