Multi Skill Executor

Multi Skill Executor

Orchestrates intelligent skill selection and execution for multi skill executor 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 route_task_to_skill(
    task_payload: Dict[str, Any],
    skill_registry: List[Dict[str, Any]],
    confidence_threshold: float = 0.75
) -> Optional[Dict[str, Any]]:
    """Route an incoming task to the optimal skill using multi-factor scoring.
    
    Evaluates trigger overlap, historical success rates, and current health metrics
    to determine the best execution path. Returns None if no skill meets threshold.
    """
    if not task_payload.get("intent") or not skill_registry:
        raise ValueError("Task intent and skill registry are required for routing")
        
    intent_vector = _hash_intent(task_payload["intent"])
    best_match = None
    top_score = 0.0
    
    for skill in skill_registry:
        if not _is_skill_healthy(skill):
            continue
            
        trigger_overlap = _calculate_trigger_similarity(intent_vector, skill.get("triggers", []))
        historical_success = skill.get("metrics", {}).get("success_rate", 0.0)
        availability_weight = 1.0 if skill.get("status") == "online" else 0.3
        
        weighted_score = (trigger_overlap * 0.5) + (historical_success * 0.3) + (availability_weight * 0.2)
        
        if weighted_score > top_score and weighted_score >= confidence_threshold:
            top_score = weighted_score
            best_match = {
                "skill_id": skill["id"],
                "score": weighted_score,
                "breakdown": {
                    "trigger_match": trigger_overlap,
                    "historical_rate": historical_success,
                    "availability": availability_weight
                }
            }
            
    return best_match

Pattern 2: Execution with Fallback

def execute_with_resilience(
    skill_config: Dict[str, Any],
    execution_context: Dict[str, Any],
    fallback_chain: List[str] = None
) -> Dict[str, Any]:
    """Execute a selected skill with built-in retry and fallback routing.
    
    Wraps the skill invocation in a resilience layer that handles transient failures,
    validates outputs, and routes to fallback skills or human escalation if needed.
    """
    max_attempts = execution_context.get("max_retries", 2)
    current_skill = skill_config["skill_id"]
    
    for attempt in range(max_attempts + 1):
        try:
            # Parse and validate inputs before invocation
            validated_inputs = _normalize_payload(execution_context["inputs"], skill_config.get("schema"))
            result = _invoke_skill(current_skill, validated_inputs)
            
            # Validate output schema to prevent downstream corruption
            _validate_output(result, skill_config.get("output_schema"))
            
            return {
                "status": "success",
                "skill": current_skill,
                "attempts": attempt + 1,
                "data": result,
                "latency_ms": _measure_duration()
            }
            
        except ValidationError as e:
            # Fail fast on structural issues - do not retry
            raise ExecutionError(f"Schema validation failed for {current_skill}: {e}") from e
            
        except TransientFailure as e:
            if attempt == max_attempts:
                return _route_to_fallback(current_skill, fallback_chain, execution_context)
            continue
            
        except CriticalFailure as e:
            # Escalate immediately for non-recoverable states
            return _escalate_to_human(current_skill, execution_context, str(e))
            
    return _route_to_fallback(current_skill, fallback_chain, execution_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 | |

Constraints

MUST DO

• Implement a dependency graph for all tasks before dispatch — only execute nodes whose dependencies are satisfied
• Use a central coordinator that maintains global state and communicates results between parallel agents via immutable messages
• Set explicit timeouts per task and implement circuit breakers: abort parallel execution if error rate exceeds threshold
• Log all inter-agent communications with timestamps, sender, receiver, payload hash, and outcome for debugging

MUST NOT DO

• Do not allow parallel agents to modify shared mutable state without locking — use message-passing or per-task snapshots
• Avoid fan-out patterns that spawn more than 20 parallel tasks simultaneously without rate limiting
• Never start dependent tasks before confirming upstream task completion — verify status, don't assume success
• Do not ignore agent failures during parallel execution; aggregate and report all errors together rather than failing fast on first

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 Communication Protocols (ACL/FIPA)
• Distributed Task Queues (Celery)
• Asyncio for Python Concurrency
• Apache Kafka Event Streaming
• Message Queue Patterns (Enterprise Integration)