358 lines
13 KiB
Markdown
358 lines
13 KiB
Markdown
# Memory-Enhanced Context Restoration Task
|
|
|
|
## Purpose
|
|
Intelligently restore context using both session state and accumulated memory insights to provide comprehensive, actionable context for persona activation and task execution.
|
|
|
|
## Multi-Layer Context Restoration Process
|
|
|
|
### 1. Session State Analysis
|
|
**Immediate Context Loading**:
|
|
```python
|
|
def load_session_context():
|
|
session_state = load_file('.ai/orchestrator-state.md')
|
|
return {
|
|
"project_name": extract_project_name(session_state),
|
|
"current_phase": extract_current_phase(session_state),
|
|
"active_personas": extract_persona_history(session_state),
|
|
"recent_decisions": extract_decision_log(session_state),
|
|
"pending_items": extract_blockers_and_concerns(session_state),
|
|
"last_activity": extract_last_activity(session_state),
|
|
"session_duration": calculate_session_duration(session_state)
|
|
}
|
|
```
|
|
|
|
### 2. Memory Intelligence Integration
|
|
**Historical Context Queries**:
|
|
```python
|
|
def gather_memory_intelligence(session_context, target_persona):
|
|
memory_queries = []
|
|
|
|
# Direct persona relevance
|
|
memory_queries.append(f"{target_persona} successful patterns {session_context.project_type}")
|
|
|
|
# Current phase insights
|
|
memory_queries.append(f"{session_context.current_phase} challenges solutions {target_persona}")
|
|
|
|
# Pending items resolution
|
|
if session_context.pending_items:
|
|
memory_queries.append(f"solutions for {session_context.pending_items}")
|
|
|
|
# Cross-project learning
|
|
memory_queries.append(f"successful {target_persona} approaches {session_context.tech_context}")
|
|
|
|
# Anti-pattern prevention
|
|
memory_queries.append(f"common mistakes {target_persona} {session_context.current_phase}")
|
|
|
|
return execute_memory_queries(memory_queries)
|
|
|
|
def execute_memory_queries(queries):
|
|
memory_insights = {
|
|
"relevant_patterns": [],
|
|
"success_strategies": [],
|
|
"anti_patterns": [],
|
|
"optimization_opportunities": [],
|
|
"personalization_insights": []
|
|
}
|
|
|
|
for query in queries:
|
|
memories = search_memory(query, limit=3, threshold=0.7)
|
|
categorize_memories(memories, memory_insights)
|
|
|
|
return memory_insights
|
|
```
|
|
|
|
### 3. Proactive Intelligence Generation
|
|
**Intelligent Anticipation**:
|
|
```python
|
|
def generate_proactive_insights(session_context, memory_insights, target_persona):
|
|
proactive_intelligence = {}
|
|
|
|
# Predict likely next actions
|
|
proactive_intelligence["likely_next_actions"] = predict_next_actions(
|
|
session_context, memory_insights, target_persona
|
|
)
|
|
|
|
# Identify potential roadblocks
|
|
proactive_intelligence["potential_issues"] = identify_potential_issues(
|
|
session_context, memory_insights
|
|
)
|
|
|
|
# Suggest optimizations
|
|
proactive_intelligence["optimization_opportunities"] = suggest_optimizations(
|
|
session_context, memory_insights
|
|
)
|
|
|
|
# Personalize recommendations
|
|
proactive_intelligence["personalized_suggestions"] = personalize_recommendations(
|
|
session_context, target_persona
|
|
)
|
|
|
|
return proactive_intelligence
|
|
```
|
|
|
|
## Context Presentation Templates
|
|
|
|
### Enhanced Context Briefing for Persona Activation
|
|
```markdown
|
|
# 🧠 Memory-Enhanced Context Restoration for {Target Persona}
|
|
|
|
## 📍 Current Project State
|
|
**Project**: {project_name} | **Phase**: {current_phase} | **Duration**: {session_duration}
|
|
**Last Activity**: {last_persona} completed {last_task} {time_ago}
|
|
**Progress Status**: {completion_percentage}% through {current_epic}
|
|
|
|
## 🎯 Your Role Context
|
|
**Activation Reason**: {why_this_persona_now}
|
|
**Expected Contribution**: {anticipated_value_from_persona}
|
|
**Key Stakeholders**: {relevant_other_personas_and_user}
|
|
|
|
## 📚 Relevant Memory Intelligence
|
|
### Successful Patterns (from {similar_situations_count} similar cases)
|
|
- ✅ **{Success Pattern 1}**: Applied in {project_example} with {success_metric}
|
|
- ✅ **{Success Pattern 2}**: Used {usage_frequency} times with {average_outcome}
|
|
- ✅ **{Success Pattern 3}**: Proven effective for {context_specifics}
|
|
|
|
### Lessons Learned
|
|
- ⚠️ **Avoid**: {anti_pattern} (caused issues in {failure_count} cases)
|
|
- 🔧 **Best Practice**: {optimization_approach} (improved outcomes by {improvement_metric})
|
|
- 💡 **Insight**: {strategic_insight} (discovered from {learning_source})
|
|
|
|
## 🚀 Proactive Recommendations
|
|
### Immediate Actions
|
|
1. **{Priority Action 1}** - {rationale_with_memory_support}
|
|
2. **{Priority Action 2}** - {rationale_with_memory_support}
|
|
|
|
### Optimization Opportunities
|
|
- **{Optimization 1}**: {memory_based_suggestion}
|
|
- **{Optimization 2}**: {efficiency_improvement}
|
|
|
|
### Potential Issues to Watch
|
|
- **{Risk 1}**: {early_warning_signs} → **Prevention**: {mitigation_strategy}
|
|
- **{Risk 2}**: {indicators_to_monitor} → **Response**: {response_plan}
|
|
|
|
## 🎨 Personalization Insights
|
|
**Your Working Style**: {learned_preferences}
|
|
**Effective Approaches**: {what_works_well_for_user}
|
|
**Communication Preferences**: {optimal_interaction_style}
|
|
|
|
## ❓ Contextual Questions for Validation
|
|
Based on memory patterns, please confirm:
|
|
1. {context_validation_question_1}
|
|
2. {context_validation_question_2}
|
|
3. {preference_confirmation_question}
|
|
|
|
---
|
|
💬 **Memory Access**: Use `/recall {topic}` or ask "What do you remember about..."
|
|
🔍 **Deep Dive**: Use `/insights` for additional proactive intelligence
|
|
```
|
|
|
|
### Lightweight Context Summary (for experienced users)
|
|
```markdown
|
|
# ⚡ Quick Context for {Target Persona}
|
|
|
|
**Current**: {project_phase} | **Last**: {previous_activity}
|
|
**Memory Insights**: {key_pattern} proven in {success_cases} similar cases
|
|
**Recommended**: {next_action} based on {success_probability}% success rate
|
|
**Watch For**: {primary_risk} (early signs: {warning_indicators})
|
|
|
|
**Ready to proceed with {suggested_approach}?**
|
|
```
|
|
|
|
## Context Restoration Intelligence
|
|
|
|
### Pattern Recognition Engine
|
|
```python
|
|
def recognize_context_patterns(session_context, memory_base):
|
|
pattern_analysis = {
|
|
"workflow_stage": classify_workflow_stage(session_context),
|
|
"success_probability": calculate_success_probability(session_context, memory_base),
|
|
"risk_assessment": assess_contextual_risks(session_context, memory_base),
|
|
"optimization_potential": identify_optimization_opportunities(session_context),
|
|
"user_alignment": assess_user_preference_alignment(session_context)
|
|
}
|
|
|
|
return pattern_analysis
|
|
|
|
def classify_workflow_stage(session_context):
|
|
stage_indicators = {
|
|
"project_initiation": ["no_prd", "analyst_activity", "brainstorming"],
|
|
"requirements_definition": ["prd_draft", "pm_activity", "scope_discussion"],
|
|
"architecture_design": ["architect_activity", "tech_decisions", "component_design"],
|
|
"development_preparation": ["po_activity", "story_creation", "validation"],
|
|
"active_development": ["dev_activity", "implementation", "testing"],
|
|
"refinement_cycle": ["multiple_persona_switches", "iterative_changes"]
|
|
}
|
|
|
|
return match_stage_indicators(session_context, stage_indicators)
|
|
```
|
|
|
|
### Success Prediction Algorithm
|
|
```python
|
|
def calculate_success_probability(current_context, memory_insights):
|
|
success_factors = {
|
|
"pattern_match_strength": calculate_pattern_similarity(current_context, memory_insights),
|
|
"context_completeness": assess_context_completeness(current_context),
|
|
"resource_availability": evaluate_resource_readiness(current_context),
|
|
"risk_mitigation": assess_risk_preparation(current_context, memory_insights),
|
|
"user_engagement": evaluate_user_engagement_patterns(current_context)
|
|
}
|
|
|
|
weighted_score = calculate_weighted_success_score(success_factors)
|
|
confidence_interval = calculate_confidence_interval(memory_insights.sample_size)
|
|
|
|
return {
|
|
"success_probability": weighted_score,
|
|
"confidence": confidence_interval,
|
|
"key_factors": identify_critical_success_factors(success_factors),
|
|
"improvement_opportunities": suggest_probability_improvements(success_factors)
|
|
}
|
|
```
|
|
|
|
## Memory Creation During Context Restoration
|
|
|
|
### Context Restoration Outcome Tracking
|
|
```python
|
|
def track_context_restoration_effectiveness():
|
|
restoration_memory = {
|
|
"type": "context_restoration",
|
|
"session_context": current_session_state,
|
|
"memory_insights_provided": memory_intelligence_summary,
|
|
"persona_activation_success": measure_activation_effectiveness(),
|
|
"user_satisfaction": capture_user_feedback(),
|
|
"task_completion_improvement": measure_efficiency_gains(),
|
|
"accuracy_of_predictions": validate_proactive_insights(),
|
|
"learning_opportunities": identify_restoration_improvements()
|
|
}
|
|
|
|
add_memories(restoration_memory, tags=["context-restoration", "effectiveness", "learning"])
|
|
```
|
|
|
|
### Proactive Intelligence Validation
|
|
```python
|
|
def validate_proactive_insights(provided_insights, actual_outcomes):
|
|
validation_results = {}
|
|
|
|
for insight_type, predictions in provided_insights.items():
|
|
validation_results[insight_type] = {
|
|
"accuracy": calculate_prediction_accuracy(predictions, actual_outcomes),
|
|
"usefulness": measure_insight_application_rate(predictions),
|
|
"impact": assess_outcome_improvement(predictions, actual_outcomes)
|
|
}
|
|
|
|
# Update memory intelligence based on validation
|
|
update_proactive_intelligence_patterns(validation_results)
|
|
|
|
return validation_results
|
|
```
|
|
|
|
## Integration with Persona Activation
|
|
|
|
### Pre-Activation Context Assembly
|
|
```python
|
|
def prepare_persona_activation_context(target_persona, session_state):
|
|
# 1. Load immediate session context
|
|
immediate_context = load_session_context()
|
|
|
|
# 2. Gather memory intelligence
|
|
memory_intelligence = gather_memory_intelligence(immediate_context, target_persona)
|
|
|
|
# 3. Generate proactive insights
|
|
proactive_insights = generate_proactive_insights(
|
|
immediate_context, memory_intelligence, target_persona
|
|
)
|
|
|
|
# 4. Synthesize comprehensive context
|
|
comprehensive_context = synthesize_context(
|
|
immediate_context, memory_intelligence, proactive_insights
|
|
)
|
|
|
|
# 5. Personalize for target persona
|
|
personalized_context = personalize_context(comprehensive_context, target_persona)
|
|
|
|
return personalized_context
|
|
```
|
|
|
|
### Post-Activation Context Validation
|
|
```python
|
|
def validate_context_restoration_success(persona_response, user_feedback):
|
|
validation_metrics = {
|
|
"context_completeness": assess_context_gaps(persona_response),
|
|
"memory_insight_relevance": evaluate_memory_application(persona_response),
|
|
"proactive_intelligence_value": measure_proactive_insight_usage(persona_response),
|
|
"user_satisfaction": capture_user_satisfaction(user_feedback),
|
|
"efficiency_improvement": measure_time_to_productivity(persona_response)
|
|
}
|
|
|
|
# Create learning memory for future context restoration improvement
|
|
create_context_restoration_learning_memory(validation_metrics)
|
|
|
|
return validation_metrics
|
|
```
|
|
|
|
## Error Handling & Fallback Strategies
|
|
|
|
### Memory System Unavailable
|
|
```python
|
|
def fallback_context_restoration():
|
|
# Enhanced session state analysis
|
|
enhanced_session_context = analyze_session_state_deeply()
|
|
|
|
# Pattern recognition from session data
|
|
local_patterns = extract_patterns_from_session()
|
|
|
|
# Heuristic-based recommendations
|
|
heuristic_insights = generate_heuristic_insights(enhanced_session_context)
|
|
|
|
# Clear capability communication
|
|
communicate_reduced_capability_scope()
|
|
|
|
return create_fallback_context_briefing(
|
|
enhanced_session_context, local_patterns, heuristic_insights
|
|
)
|
|
```
|
|
|
|
### Memory Query Failures
|
|
```python
|
|
def handle_memory_query_failures(failed_queries, session_context):
|
|
# Attempt alternative query formulations
|
|
alternative_queries = reformulate_queries(failed_queries)
|
|
|
|
# Use cached memory insights if available
|
|
cached_insights = retrieve_cached_memory_insights(session_context)
|
|
|
|
# Generate context with available information
|
|
partial_context = create_partial_context(cached_insights, session_context)
|
|
|
|
# Flag limitations clearly
|
|
flag_context_limitations(failed_queries)
|
|
|
|
return partial_context
|
|
```
|
|
|
|
## Quality Assurance & Continuous Improvement
|
|
|
|
### Context Quality Metrics
|
|
- **Relevance Score**: How well memory insights match current context needs
|
|
- **Completeness Score**: Coverage of important contextual factors
|
|
- **Accuracy Score**: Correctness of proactive predictions and insights
|
|
- **Usefulness Score**: Practical value of context information for persona activation
|
|
- **Efficiency Score**: Time saved through effective context restoration
|
|
|
|
### Continuous Learning Integration
|
|
```python
|
|
def continuous_context_restoration_learning():
|
|
# Analyze recent context restoration outcomes
|
|
recent_restorations = get_recent_context_restorations()
|
|
|
|
# Identify improvement patterns
|
|
improvement_opportunities = analyze_restoration_effectiveness(recent_restorations)
|
|
|
|
# Update context restoration algorithms
|
|
update_context_intelligence(improvement_opportunities)
|
|
|
|
# Refine memory query strategies
|
|
optimize_memory_query_patterns(recent_restorations)
|
|
|
|
# Enhance proactive intelligence generation
|
|
improve_proactive_insight_algorithms(recent_restorations)
|
|
``` |