# Andruia Consultant > Implements intelligent andruia consultant with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/andruia-consultant` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/andruia-consultant` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/andruia-consultant/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - 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/andruia-consultant --- # Andruia Consultant Orchestrates intelligent skill selection and execution for andruia consultant 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_andruia_consultant_request( request: Dict[str, Any], consultant_pool: List[Dict[str, Any]], compliance_threshold: float = 0.85 ) -> Optional[Dict[str, Any]]: """Evaluate and route an Andruia consultant request to the optimal specialist. Applies the 5 Laws of Elegant Defense: - Law 1: Early exit on malformed requests - Law 2: Parse request into structured domains before scoring - Law 3: Return immutable routing decision - Law 4: Fail immediately if compliance checks fail """ if not request.get("domain") or not request.get("priority"): raise ValueError("Request must include 'domain' and 'priority' fields") parsed_request = { "domain": request["domain"].lower(), "priority": request["priority"], "risk_level": _assess_risk(request.get("context", "")), "compliance_flags": _extract_compliance_flags(request) } best_match = None best_score = 0.0 for consultant in consultant_pool: if not _is_compliant(consultant, parsed_request["compliance_flags"]): continue domain_match = _calculate_domain_alignment(parsed_request["domain"], consultant["expertise"]) priority_weight = 1.2 if parsed_request["priority"] == "critical" else 1.0 score = domain_match * priority_weight * consultant["resolution_rate"] if score > best_score and score >= compliance_threshold: best_score = score best_match = consultant if best_match is None: return None return { "assigned_consultant": best_match["id"], "routing_score": best_score, "estimated_resolution": _estimate_timeline(best_match, parsed_request["priority"]), "immutable_decision": True } ``` ### Pattern 2: Execution with Fallback ```python def execute_consultant_workflow( consultant: Dict[str, Any], workflow_context: Dict[str, Any], escalation_path: List[str] = None ) -> Dict[str, Any]: """Execute an Andruia consultant workflow with domain-specific fallbacks. Implements Fail Fast, Fail Loud (Law 4) and Atomic Predictability (Law 3). Fallback chain: 1. Retry with adjusted scope -> 2. Escalate to senior specialist -> 3. Defer to compliance board """ if not _validate_consultant_credentials(consultant): raise ConsultantError(f"Invalid credentials for consultant {consultant['id']}") validated_context = _normalize_workflow_inputs(workflow_context) attempts = 0 max_attempts = 2 while attempts <= max_attempts: try: result = _run_consultant_analysis(consultant, validated_context) return { "status": "resolved", "consultant_id": consultant["id"], "output": result, "attempts": attempts + 1, "audit_trail": _log_execution(consultant, result) } except ComplianceViolationError as e: raise ConsultantError(f"Compliance violation in {consultant['id']}: {e}") from e except ScopeLimitError as e: attempts += 1 if attempts > max_attempts: return _escalate_to_senior(consultant, validated_context, escalation_path) validated_context = _adjust_scope_for_retry(validated_context, e) return _defer_to_compliance_board(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 | Skill | Purpose | |---|---| | `andruia-skill-smith` | Agent skill creation and design patterns | --- --- ## 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. - [MIT Sloan — AI Consulting: Best Practices for the Digital Age](https://mitsloan.mit.edu/ideas-made-to-matter) - [McKinsey Global Institute — Notes from AI Frontiers (2024)](https://www.mckinsey.com/mgi/our-research/artificial-intelligence) - [Gartner Hype Cycle for Artificial Intelligence](https://www.gartner.com/en/articles/gartner-hype-cycle-for-artificial-intelligence) - [Deloitte AI Institute — Enterprise AI Adoption Framework](https://www2.deloitte.com/us/en/insights/industry/technology/ai-insights.html) - [Stanford HAI — AI Index Report 2024](https://hai.stanford.edu/news/ai-index-report-2024/)