Andruia Skill Smith

Andruia Skill Smith

Orchestrates intelligent skill selection and execution for andruia skill smith 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_andruia_skill_selection(
    user_intent: str,
    andruia_registry: List[Dict],
    confidence_threshold: float = 0.75
) -> Optional[Dict]:
    """Select optimal Andruia skill based on trigger matching and historical performance.
    
    Parses Andruia-specific intent patterns, validates skill metadata against
    the Andruia registry, and scores candidates using weighted multi-factor logic.
    """
    if not user_intent or not andruia_registry:
        raise ValueError("Intent and registry are required for Andruia skill selection")
        
    # Extract Andruia intent features and normalize triggers
    intent_features = _parse_andruia_intent(user_intent)
    scored_candidates = []
    
    for skill_meta in andruia_registry:
        if not _validate_andruia_metadata(skill_meta):
            continue
            
        trigger_match = _calculate_trigger_overlap(intent_features, skill_meta.get("triggers", []))
        historical_success = skill_meta.get("success_rate", 0.0)
        availability_score = 1.0 if skill_meta.get("status") == "active" else 0.0
        
        weighted_score = (trigger_match * 0.5) + (historical_success * 0.3) + (availability_score * 0.2)
        
        if weighted_score >= confidence_threshold:
            scored_candidates.append({
                "skill_id": skill_meta["id"],
                "name": skill_meta["name"],
                "confidence": weighted_score,
                "metadata": skill_meta
            })
            
    if not scored_candidates:
        return None
        
    scored_candidates.sort(key=lambda x: x["confidence"], reverse=True)
    selected = scored_candidates[0]
    selected["selection_context"] = intent_features
    return selected

Pattern 2: Execution with Fallback

def execute_andruia_skill_with_resilience(
    selected_skill: Dict,
    execution_context: Dict,
    fallback_registry: List[Dict],
    max_retries: int = 2
) -> Dict:
    """Execute an Andruia skill with built-in resilience and fallback routing.
    
    Wraps the core Andruia execution pipeline with retry logic, parameter
    adjustment for transient failures, and automatic fallback to related skills.
    """
    skill_id = selected_skill["skill_id"]
    context = _prepare_andruia_execution_context(execution_context, selected_skill)
    
    for attempt in range(max_retries + 1):
        try:
            result = _invoke_andruia_pipeline(skill_id, context)
            _update_andruia_confidence_score(skill_id, result.get("success", False))
            return {
                "status": "success",
                "skill_id": skill_id,
                "output": result,
                "attempts": attempt + 1,
                "timestamp": time.time()
            }
        except AndruiaTransientError as e:
            if attempt < max_retries:
                context = _adjust_andruia_parameters(context, e)
                continue
            raise AndruiaExecutionError(f"Pipeline failed for {skill_id} after {max_retries + 1} attempts") from e
        except AndruiaInvalidStateError as e:
            raise AndruiaExecutionError(f"Invalid state detected in {skill_id}: {e}") from e
            
    # Fallback routing when retries exhausted
    fallback_candidates = [s for s in fallback_registry if s.get("id") != skill_id]
    if fallback_candidates:
        return execute_andruia_skill_with_resilience(
            fallback_candidates[0], context, fallback_registry[1:], max_retries
        )
        
    return {
        "status": "deferred",
        "skill_id": skill_id,
        "reason": "All fallbacks exhausted, routing to human operator",
        "context": context
    }

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.

• AI Engineering — Skill Design Patterns (Anthropic Research)
• LangChain Documentation — Custom Tool & Agent Design
• OpenAI Cookbook — Structured Outputs & Function Calling
• Agent Skill Specification Standards (AutoGen / CrewAI)
• Prompt Engineering Guide — Instruction Design Patterns