conversation-memory

# Conversation Memory Orchestrates intelligent skill selection and execution for conversation memory workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python def score_and_select_memory_context( current_query: str, conversation_history: List[Dict], max_context_turns: int = 10, relevance_threshold: float = 0.65 ) -> List[Dict]: """Score conversation history turns against the current query. Implements multi-factor scoring for memory retrieval: - Semantic similarity between query and past turns - Recency weighting (Law 1: Early Exit for empty history) - Entity overlap and constraint matching Args: current_query: The active user prompt requiring context conversation_history: List of past message dicts with 'role', 'content', 'timestamp' max_context_turns: Maximum turns to include in context window relevance_threshold: Minimum semantic score to include a turn Returns: Filtered and sorted list of relevant conversation turns """ # Guard clause - Early Exit (Law 1) if not current_query or not conversation_history: return [] # Parse input - Make Illegal States Unrepresentable (Law 2) parsed_query = _normalize_text(current_query) scored_turns = [] for turn in conversation_history: # Calculate semantic similarity using embedding model similarity = _compute_embedding_similarity(parsed_query, turn["content"]) # Apply recency decay and entity overlap bonus recency_score = _calculate_recency_weight(turn["timestamp"]) entity_overlap = _count_shared_entities(parsed_query, turn["content"]) composite_score = (similarity * 0.6) + (recency_score * 0.25) + (entity_overlap * 0.15) if composite_score >= relevance_threshold: scored_turns.append({ "turn": turn, "relevance_score": composite_score, "factors": {"similarity": similarity, "recency": recency_score, "overlap": entity_overlap} }) # Atomic Predictability (Law 3) - Return new sorted list, never mutate history scored_turns.sort(key=lambda x: x["relevance_score"], reverse=True) return scored_turns[:max_context_turns] ``` ### Pattern 2: Execution with Fallback ```python def execute_memory_orchestration( selected_context: List[Dict], current_query: str, memory_store: MemoryBackend, fallback_strategy: str = "recent_history" ) -> Dict: """Execute memory retrieval with fallback chain for resilience. Implements Fail Fast, Fail Loud (Law 4): - Invalid memory states halt immediately with descriptive errors - No silent context assembly failures Fallback chain: 1. Retrieve from vector store with semantic search 2. Fallback to recent chronological history 3. Defer to default system prompt with minimal context Args: selected_context: Pre-scored memory turns from Pattern 1 current_query: Active prompt requiring context memory_store: Initialized memory backend instance fallback_strategy: Fallback mode when primary retrieval fails Returns: Execution result with assembled context, timing, and confidence metadata """ # Guard clause - validate memory store (Early Exit) if not memory_store or not memory_store.is_healthy(): raise MemoryOrchestrationError("Memory backend is unavailable or unhealthy") # Parse context - Ensure trusted state (Law 2) validated_context = _assemble_context_payload(selected_context, current_query) try: # Primary execution - Semantic memory retrieval retrieved_memory = memory_store.query_context(validated_context) # Success - Atomic Predictability (Law 3) return { "success": True, "context_turns": len(retrieved_memory), "assembled_context": retrieved_memory, "confidence": _calculate_context_confidence(retrieved_memory), "latency_ms": _measure_execution_time() } except MemoryStoreTimeoutError as e: # Fail Fast - Don't retry indefinitely (Law 4) if fallback_strategy == "recent_history": return _apply_recent_history_fallback(memory_store, current_query) raise MemoryOrchestrationError(f"Primary memory retrieval failed: {str(e)}") from e except ContextAssemblyError as e: # Invalid state - halt immediately (Law 4) raise MemoryOrchestrationError(f"Context assembly failed: {str(e)}") from e ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Conversation History Management Patterns](<https://www.promptingguide.ai/techniques/memory>) - [LangChain Memory Module Docs](<https://python.langchain.com/docs/modules/memory/>) - [State Machines in Conversational AI (Wikipedia)](<https://en.wikipedia.org/wiki/Dialogue_manager>) - [Context Window Optimization Techniques](<https://arxiv.org/abs/2307.03172>) - [Conversational State Management Survey](<https://arxiv.org/abs/2103.13026>) ## Related Skills | Skill | Purpose | |