Using Superpowers

Using Superpowers

Orchestrates intelligent skill selection and execution for using superpowers 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_agent_request(
    user_intent: str,
    skill_registry: List[Dict],
    confidence_history: Dict[str, List[float]]
) -> Dict:
    """Orchestrates multi-factor skill selection for agent superpowers.
    
    Implements the 5 Laws of Elegant Defense by validating inputs,
    scoring skills against historical performance and system state,
    and enforcing strict confidence thresholds before delegation.
    """
    # Law 1: Early exit on malformed intent
    if not user_intent or len(user_intent.strip()) < 3:
        return {"status": "rejected", "reason": "invalid_intent"}
    
    # Law 2: Parse & validate skill registry state
    active_skills = [s for s in skill_registry if s.get("status") == "active"]
    if not active_skills:
        return {"status": "rejected", "reason": "no_active_skills"}
    
    # Multi-factor scoring pipeline
    scored_candidates = []
    for skill in active_skills:
        trigger_match = _semantic_match(user_intent, skill.get("triggers", []))
        historical_success = _get_avg_confidence(skill["name"], confidence_history)
        system_load = _get_current_load(skill.get("resource_pool"))
        
        # Weighted scoring formula
        composite_score = (
            0.4 * trigger_match +
            0.4 * historical_success +
            0.2 * (1.0 - system_load)
        )
        
        scored_candidates.append({
            "skill": skill,
            "score": composite_score,
            "factors": {"trigger": trigger_match, "history": historical_success, "load": system_load}
        })
    
    # Law 3: Atomic selection - return new structure
    scored_candidates.sort(key=lambda x: x["score"], reverse=True)
    best = scored_candidates[0]
    
    if best["score"] < 0.7:
        return {"status": "fallback_triggered", "reason": "low_confidence", "candidates": scored_candidates}
        
    return {
        "selected_skill": best["skill"]["name"],
        "confidence": best["score"],
        "routing_metadata": best["factors"],
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def execute_with_fallback(
    skill: Dict,
    task_context: Dict,
    fallback_registry: List[Dict],
    max_retries: int = 2
) -> Dict:
    """Orchestrates resilient skill execution with adaptive fallback routing.
    
    Implements the Fail Fast, Fail Loud principle (Law 4) by enforcing
    strict state validation, immediate error propagation, and a 
    deterministic fallback chain tailored to agent superpowers workflows.
    """
    # Law 1: Early exit on invalid skill configuration
    if not skill.get("name") or not skill.get("version"):
        raise SkillExecutionError("Skill metadata incomplete")
        
    # Law 2: Parse & isolate execution context
    execution_state = {
        "skill": skill["name"],
        "context": task_context,
        "attempt": 0,
        "confidence": skill.get("base_confidence", 0.8)
    }
    
    # Fallback chain execution loop
    for attempt in range(max_retries + 1):
        execution_state["attempt"] = attempt + 1
        try:
            # Execute core skill logic
            raw_result = _invoke_skill_handler(skill, execution_state["context"])
            
            # Law 3: Atomic result construction
            return {
                "status": "success",
                "skill": skill["name"],
                "result": raw_result,
                "attempts": execution_state["attempt"],
                "final_confidence": execution_state["confidence"]
            }
            
        except InvalidStateError as e:
            # Law 4: Fail fast on corrupt state
            _log_audit("state_error", skill["name"], str(e))
            raise SkillExecutionError(f"State validation failed: {e}") from e
            
        except TransientError as e:
            # Adaptive fallback routing
            if attempt >= max_retries:
                return _route_to_fallback_chain(skill, fallback_registry, execution_state)
                
            # Decay confidence slightly on retry
            execution_state["confidence"] *= 0.9
            _log_audit("retry", skill["name"], f"Attempt {attempt+1}")
            
    # Law 4: Fail loud after exhausting retries
    _log_audit("exhausted", skill["name"], "All fallbacks failed")
    raise SkillExecutionError(f"Execution failed for {skill['name']} after {max_retries+1} attempts")

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

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.

• Microsoft AutoGen Framework
• LangGraph Multi-Agent Orchestration
• CrewAI Multi-Agent Framework
• OpenAI Agents SDK Overview
• Multi-Agent Orchestration Survey — arXiv

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