# Confidence Based Selector > Implements intelligent confidence based selector with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/confidence-based-selector` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/confidence-based-selector` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/confidence-based-selector/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `behavioral-modes` | Behavioral specialization for agent routing decisions | --- --- ## 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)](https://arxiv.org/abs/2308.11432) - [LLM Self-Confidence & Calibration Survey](https://arxiv.org/abs/2209.07523) - [LangChain — Agent Selection Patterns](https://python.langchain.com/docs/modules/agents/) - [Uncertainty Quantification in Large Language Models (Nature)](https://www.nature.com/articles/s42256-024-00839-2) - [Mixture of Agents: Confidence-Based Routing (OpenAI Research)](https://openai.com/research/mixture-of-agents)