Auri Core

Auri Core

Orchestrates intelligent skill selection and execution for auri core 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 evaluate_skill_candidates(
    request: AuriRequest,
    registry: SkillRegistry,
    metrics: PerformanceMetrics
) -> Optional[SkillCandidate]:
    """Evaluate available skills against the incoming request using multi-factor scoring.
    
    Implements Law 2 (Parse at boundary) by validating request structure first.
    Implements Law 3 (Atomic Predictability) by returning a fresh candidate object.
    """
    if not request.intent or not request.entities:
        raise ValueError("Request must contain parsed intent and entities")
        
    candidates = []
    for skill in registry.get_active_skills():
        # Multi-factor scoring: text match + historical success + system health
        text_score = _compute_semantic_match(request.query, skill.triggers)
        history_score = metrics.get_success_rate(skill.name, window_days=30)
        health_score = registry.get_health_status(skill.name)
        
        weighted_score = (text_score * 0.5) + (history_score * 0.3) + (health_score * 0.2)
        
        if weighted_score >= registry.min_confidence_threshold:
            candidates.append(SkillCandidate(
                name=skill.name,
                confidence=weighted_score,
                dependencies=skill.dependencies,
                fallback_chain=skill.fallback_targets
            ))
            
    if not candidates:
        return None
        
    candidates.sort(key=lambda c: c.confidence, reverse=True)
    return candidates[0]

Pattern 2: Execution with Fallback

def execute_with_auri_fallback(
    candidate: SkillCandidate,
    context: ExecutionContext,
    audit_logger: AuditLogger
) -> ExecutionResult:
    """Execute the selected skill with auri-core's adaptive fallback chain.
    
    Implements Law 4 (Fail Fast, Fail Loud) by halting on invalid states.
    Implements Law 1 (Early Exit) for edge cases and dependency checks.
    """
    if not candidate or not context.validated_inputs:
        raise ExecutionError("Missing candidate or validated context for execution")
        
    # Check dependency availability before execution
    if not registry.verify_dependencies(candidate.dependencies):
        audit_logger.log("Dependency check failed", level="WARN")
        return _trigger_fallback(candidate, context, audit_logger)
        
    attempts = 0
    max_attempts = 2
    
    while attempts <= max_attempts:
        try:
            result = registry.invoke_skill(candidate.name, context.validated_inputs)
            # Update historical performance for adaptive routing
            metrics.record_success(candidate.name, latency=result.latency_ms)
            audit_logger.log(f"Success: {candidate.name}", level="INFO")
            return ExecutionResult(success=True, data=result, confidence=candidate.confidence)
            
        except TransientNetworkError:
            attempts += 1
            if attempts > max_attempts:
                break
            audit_logger.log(f"Retry {attempts}/{max_attempts} for {candidate.name}", level="DEBUG")
            
        except InvalidStateError as e:
            audit_logger.log(f"Invalid state in {candidate.name}: {e}", level="ERROR")
            raise ExecutionError(f"Fatal state violation in {candidate.name}") from e
            
    # Exhausted retries - trigger fallback chain
    return _trigger_fallback(candidate, context, audit_logger)

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.

• Agent-Based Systems Architecture (Wikipedia)
• Multi-Agent System Design Patterns
• Distributed Task Scheduling Algorithms
• Autonomous Agent Frameworks Survey (arXiv)
• Service Mesh Patterns (Istio Docs)

Related Skills

| Skill | Purpose | |