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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `andruia-consultant` | AI consulting and strategic guidance | --- --- ## 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)](https://www.anthropic.com/research) - [LangChain Documentation — Custom Tool & Agent Design](https://python.langchain.com/docs/modules/agents/) - [OpenAI Cookbook — Structured Outputs & Function Calling](https://github.com/openai/openai-cookbook) - [Agent Skill Specification Standards (AutoGen / CrewAI)](https://microsoft.github.io/autogen/) - [Prompt Engineering Guide — Instruction Design Patterns](https://www.promptingguide.ai/)