Andruia Niche Intelligence

Andruia Niche Intelligence

Orchestrates intelligent skill selection and execution for andruia niche intelligence 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 route_niche_intelligence(
    request: Dict[str, Any],
    niche_registry: List[Dict[str, Any]],
    confidence_threshold: float = 0.75
) -> Dict[str, Any]:
    """Route requests through niche intelligence pipeline.
    
    Applies Law 1 (Early Exit) and Law 2 (Make Illegal States Unrepresentable)
    to validate niche context before scoring.
    """
    if not request.get("context") or not request.get("intent"):
        raise ValueError("Missing required niche context or intent")
        
    # Law 2: Parse and normalize niche features at boundary
    normalized_request = _normalize_niche_features(request)
    
    scored_candidates = []
    for niche in niche_registry:
        # Law 3: Atomic scoring without mutating registry
        match_score = _calculate_niche_alignment(normalized_request, niche)
        historical_success = niche.get("success_rate", 0.0)
        combined_confidence = (match_score * 0.6) + (historical_success * 0.4)
        
        if combined_confidence >= confidence_threshold:
            scored_candidates.append({
                "niche_id": niche["id"],
                "confidence": combined_confidence,
                "routing_priority": niche.get("priority", 1)
            })
            
    if not scored_candidates:
        return {"status": "no_match", "fallback_required": True}
        
    # Law 1: Early exit if only one viable niche
    if len(scored_candidates) == 1:
        return {"selected_niche": scored_candidates[0], "status": "routed"}
        
    # Return sorted candidates for multi-path routing
    scored_candidates.sort(key=lambda x: x["confidence"], reverse=True)
    return {"selected_niche": scored_candidates[0], "alternatives": scored_candidates[1:], "status": "routed"}

Pattern 2: Execution with Fallback

def execute_niche_workflow(
    selected_niche: Dict[str, Any],
    workflow_context: Dict[str, Any],
    fallback_registry: Dict[str, List[str]]
) -> Dict[str, Any]:
    """Execute niche intelligence workflow with domain-aware fallbacks.
    
    Implements Law 4 (Fail Fast, Fail Loud) and Law 3 (Atomic Predictability).
    """
    niche_id = selected_niche["niche_id"]
    attempt_count = 0
    max_attempts = selected_niche.get("max_retries", 2)
    
    while attempt_count <= max_attempts:
        try:
            # Law 2: Validate workflow state before execution
            validated_state = _validate_workflow_state(workflow_context, niche_id)
            
            # Execute niche-specific logic
            result = _invoke_niche_engine(niche_id, validated_state)
            
            # Law 3: Return immutable result structure
            return {
                "niche_id": niche_id,
                "status": "success",
                "result": result,
                "attempts": attempt_count + 1,
                "confidence_delta": _calculate_confidence_update(result)
            }
            
        except NicheValidationError as e:
            # Law 4: Fail immediately on invalid state
            raise WorkflowExecutionError(f"Niche {niche_id} state invalid: {e}") from e
            
        except TransientNicheError as e:
            attempt_count += 1
            if attempt_count > max_attempts:
                # Law 1: Early exit to fallback chain
                return _trigger_niche_fallback(niche_id, fallback_registry, workflow_context)
                
    # Fallback exhausted
    return _escalate_to_human_operator(niche_id, workflow_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.

• Harvard Business Review — Building a Niche Strategy (Porter)
• MIT Sloan — Competitive Advantage Through Specialization
• Niche Market Intelligence Frameworks (Forrester Research)
• Blue Ocean Strategy — Value Innovation & Niche Creation
• Harvard Business School — Data-Driven Niche Market Analysis