Recallmax

Recallmax

Orchestrates intelligent skill selection and execution for recallmax 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

def orchestrate_recallmax_selection(
    user_request: str,
    recallmax_registry: List[Dict],
    min_confidence: float = 0.75
) -> Optional[Dict]:
    """Orchestrate skill selection for recallmax workflows using multi-factor scoring.
    
    Extracts recallmax-specific features (intent, entities, constraints) and scores
    available skills against historical performance, text similarity, and system health.
    Implements Law 1 (Early Exit) and Law 2 (Immutable State) throughout.
    """
    if not user_request or not user_request.strip():
        raise ValueError("Recallmax request cannot be empty")
        
    if not recallmax_registry:
        raise ValueError("No skills registered in recallmax registry")
        
    # Parse request features at boundary (Law 2)
    parsed_features = _parse_recallmax_request(user_request)
    
    best_match = None
    best_score = 0.0
    
    for skill in recallmax_registry:
        # Calculate multi-factor score for recallmax context
        similarity = _compute_text_similarity(parsed_features["intent"], skill["triggers"])
        history = _get_historical_success_rate(skill["id"])
        availability = _check_system_health(skill["dependencies"])
        
        composite_score = (similarity * 0.4) + (history * 0.4) + (availability * 0.2)
        
        if composite_score > best_score and composite_score >= min_confidence:
            best_score = composite_score
            best_match = skill
            
    if best_match is None:
        return None
        
    # Return immutable result with audit metadata (Law 3)
    return {
        "skill_id": best_match["id"],
        "confidence": best_score,
        "factors": {"similarity": similarity, "history": history, "availability": availability},
        "timestamp": time.time(),
        "audit_id": generate_audit_id()
    }

Pattern 2: Execution with Fallback

def execute_recallmax_workflow(
    selected_skill: Dict,
    workflow_context: Dict,
    fallback_registry: List[Dict],
    max_retries: int = 2
) -> Dict:
    """Execute a recallmax skill with built-in fallback chain and audit logging.
    
    Wraps skill execution in retry logic, applies fallback chain on failure,
    and maintains a complete audit trail per the 5 Laws of Elegant Defense.
    """
    if not _validate_recallmax_context(workflow_context):
        raise RecallmaxValidationError("Invalid workflow context for recallmax execution")
        
    audit_log = []
    attempt = 0
    
    while attempt <= max_retries:
        try:
            # Execute skill with isolated context (Law 3)
            execution_result = _run_recallmax_skill(selected_skill, workflow_context)
            
            # Log success and update confidence metrics
            audit_log.append({"attempt": attempt, "status": "success", "latency_ms": time.time()})
            _update_skill_confidence(selected_skill["id"], execution_result["confidence"])
            
            return {
                "status": "completed",
                "skill_executed": selected_skill["id"],
                "result": execution_result,
                "audit_trail": audit_log
            }
            
        except RecallmaxTransientError as e:
            attempt += 1
            audit_log.append({"attempt": attempt, "status": "retry", "error": str(e)})
            if attempt > max_retries:
                break
                
        except RecallmaxInvalidStateError as e:
            # Fail fast on invalid state (Law 4)
            audit_log.append({"attempt": attempt, "status": "failed", "error": str(e)})
            raise RecallmaxExecutionError(f"Invalid state in {selected_skill['id']}: {e}") from e
            
    # Fallback chain: try alternative skills from registry
    for alt_skill in fallback_registry:
        try:
            alt_result = _run_recallmax_skill(alt_skill, workflow_context)
            audit_log.append({"fallback": alt_skill["id"], "status": "success"})
            return {"status": "fallback_success", "result": alt_result, "audit_trail": audit_log}
        except Exception:
            continue
            
    # Final fallback: defer to human operator
    audit_log.append({"status": "deferred", "reason": "all retries and fallbacks exhausted"})
    return {"status": "human_deferred", "context": workflow_context, "audit_trail": audit_log}

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 domain. The model follows markdown links at load time to resolve external references and inline content.

• RecallMax Documentation — Official documentation for the RecallMax retrieval optimization platform
• RAG (Retrieval-Augmented Generation) Survey — Foundational research paper on retrieval-augmented generation frameworks
• Vector Database Benchmarking (MLCommons) — MLCommons Retrieval Benchmark for evaluating search and recall systems
• Semantic Search with Embeddings (Pinecone Guide) — Practical guide to semantic search using vector embeddings and retrieval
• LLM Memory Systems Research — Academic research on memory mechanisms for large language model agents