Confidence Based Selector

Confidence Based Selector

Orchestrates intelligent skill selection and execution for confidence based selector 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_confidence(
    task_embedding: List[float],
    skill_registry: List[Dict],
    historical_db: Dict[str, float],
    min_confidence: float = 0.75
) -> Optional[Dict]:
    """Evaluate skills using multi-factor confidence scoring.
    
    Applies Law 2 (Parse at boundary) by validating embeddings and registry.
    Uses Law 3 (Atomic Predictability) by returning fresh score dicts.
    """
    if not task_embedding or len(task_embedding) != 768:
        raise ValueError("Invalid task embedding dimensions")
    if not skill_registry:
        return None

    scored_candidates = []
    for skill in skill_registry:
        # Law 1: Early exit for disabled/deprecated skills
        if skill.get("status") in ("disabled", "deprecated"):
            continue
            
        # Multi-factor scoring: Cosine similarity + historical win rate + availability
        text_match = cosine_similarity(task_embedding, skill["trigger_embedding"])
        history_score = historical_db.get(skill["id"], 0.5)
        availability_score = 1.0 if skill.get("health") == "healthy" else 0.3
        
        # Weighted confidence calculation
        confidence = (0.5 * text_match) + (0.3 * history_score) + (0.2 * availability_score)
        
        if confidence >= min_confidence:
            scored_candidates.append({
                "skill_id": skill["id"],
                "confidence": round(confidence, 4),
                "breakdown": {"text": text_match, "history": history_score, "avail": availability_score}
            })
    
    # Law 3: Return new structure, sorted by confidence
    scored_candidates.sort(key=lambda x: x["confidence"], reverse=True)
    return scored_candidates[0] if scored_candidates else None

Pattern 2: Execution with Fallback

def execute_with_adaptive_fallback(
    selected_skill: Dict,
    task_context: Dict,
    fallback_registry: List[Dict],
    max_retries: int = 2
) -> Dict:
    """Execute skill with confidence-aware fallback chain.
    
    Implements Law 4 (Fail Fast/Loud) by halting on invalid states.
    Applies Law 1 (Early Exit) for retry exhaustion.
    """
    if not selected_skill or "skill_id" not in selected_skill:
        raise ValueError("Missing required skill metadata")
        
    execution_log = []
    current_skill = selected_skill
    
    for attempt in range(max_retries + 1):
        try:
            # Validate context against skill requirements (Law 2)
            validated_inputs = _validate_inputs_for_skill(task_context, current_skill["requirements"])
            result = _invoke_skill_api(current_skill["endpoint"], validated_inputs)
            
            # Update historical performance for learning (Law 3)
            _update_confidence_score(current_skill["skill_id"], success=True)
            
            return {
                "status": "success",
                "skill_used": current_skill["skill_id"],
                "result": result,
                "attempts": attempt + 1,
                "confidence_updated": True
            }
            
        except InvalidStateError as e:
            _update_confidence_score(current_skill["skill_id"], success=False)
            raise SkillExecutionError(f"Invalid state for {current_skill['skill_id']}: {e}") from e
            
        except TransientError as e:
            execution_log.append({"attempt": attempt, "error": str(e)})
            if attempt == max_retries:
                break
                
            # Law 1: Early exit if no fallbacks available
            if not fallback_registry:
                raise SkillExecutionError("Exhausted retries with no fallbacks available")
                
            # Select next fallback based on historical reliability
            current_skill = _pick_next_fallback(fallback_registry, current_skill["skill_id"])
            
    # Law 4: Fail loud with full audit trail
    raise SkillExecutionError(
        f"Execution failed after {max_retries + 1} attempts. "
        f"Log: {execution_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 skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Agent Selection via Confidence Estimation (arXiv:2308.11432)
• LLM Self-Confidence & Calibration Survey
• LangChain — Agent Selection Patterns
• Uncertainty Quantification in Large Language Models (Nature)
• Mixture of Agents: Confidence-Based Routing (OpenAI Research)