Imported from paulpas/agent-skill-router (skills/agent/agent-memory-systems/SKILL.md). Install upstream with npx skills add paulpas/agent-skill-router --skill agent-memory-systems. Copyright stays with the author (MIT).

Agent Memory Systems

Orchestrates intelligent skill selection and execution for agent memory systems 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: Memory Retrieval & Relevance Scoring

def retrieve_relevant_memories(
    query_context: str,
    memory_store: List[Dict],
    max_results: int = 5,
    relevance_threshold: float = 0.65
) -> List[Dict]:
    """Retrieve and rank memories based on semantic relevance and recency.
    
    Implements Law 2 (Parse at boundary) by validating query and memory structure.
    Implements Law 3 (Atomic Predictability) by returning fresh scored objects.
    """
    if not query_context or not memory_store:
        raise ValueError("Query context and memory store must be non-empty")
        
    query_embedding = _compute_embedding(query_context)
    scored_memories = []
    
    for memory in memory_store:
        # Calculate semantic similarity
        semantic_score = _cosine_similarity(query_embedding, memory["embedding"])
        
        # Apply temporal decay (Law 1: Early exit for stale memories)
        age_days = (time.time() - memory["created_at"]) / 86400
        decay_factor = max(0.1, 1.0 - (age_days * 0.05))
        
        # Combined relevance score
        relevance = semantic_score * decay_factor
        
        if relevance >= relevance_threshold:
            scored_memories.append({
                "id": memory["id"],
                "content": memory["content"],
                "relevance_score": round(relevance, 4),
                "age_days": round(age_days, 2),
                "source": memory.get("source", "unknown")
            })
            
    # Sort by relevance descending and return top results
    scored_memories.sort(key=lambda m: m["relevance_score"], reverse=True)
    return scored_memories[:max_results]

Pattern 2: Context Window Management & Consolidation

def manage_context_window(
    current_context: List[Dict],
    new_interaction: Dict,
    max_tokens: int = 4000,
    consolidation_strategy: str = "summarize"
) -> Dict:
    """Manage context window by integrating new interactions and consolidating old memories.
    
    Implements Law 4 (Fail Fast) by validating token counts and structure.
    Implements fallback chain for context overflow scenarios.
    """
    # Validate inputs at boundary
    if not current_context or not new_interaction.get("content"):
        raise ValueError("Context and new interaction must be valid")
        
    # Calculate current token usage
    current_tokens = _estimate_tokens(current_context)
    new_tokens = _estimate_tokens([new_interaction])
    
    if current_tokens + new_tokens <= max_tokens:
        # Direct append if within limits
        return {
            "status": "appended",
            "context": current_context + [new_interaction],
            "total_tokens": current_tokens + new_tokens,
            "action": "none"
        }
        
    # Fallback chain for overflow
    try:
        # Level 1: Summarize oldest memories
        consolidated = _summarize_oldest_memories(current_context, max_tokens - new_tokens)
        return {
            "status": "consolidated",
            "context": consolidated + [new_interaction],
            "total_tokens": _estimate_tokens(consolidated) + new_tokens,
            "action": "summarize"
        }
    except ContextOverflowError:
        # Level 2: Truncate to most recent critical memories
        critical_memories = _extract_critical_memories(current_context, max_tokens - new_tokens)
        return {
            "status": "truncated",
            "context": critical_memories + [new_interaction],
            "total_tokens": _estimate_tokens(critical_memories) + new_tokens,
            "action": "truncate"
        }

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

Related Skills

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.

• Memory Mechanisms in LLMs — arXiv (2307.05939)
• What Is Agent Memory — LangChain Blog
• Building Effective Agents — Anthropic Research
• Vector Databases for Agent Memory — Pinecone Guide
• Long-Term Memory for LLMs — arXiv Survey