---

name: andruia-consultant compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent andruia consultant with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: andruia-consultant, andruia consultant, how do i andruia-consultant, orchestrate andruia-consultant, automate andruia-consultant, agent andruia-consultant archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

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

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

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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
• McKinsey Global Institute — Notes from AI Frontiers (2024)
• Gartner Hype Cycle for Artificial Intelligence
• Deloitte AI Institute — Enterprise AI Adoption Framework
• Stanford HAI — AI Index Report 2024