Zipai Optimizer

Zipai Optimizer

Orchestrates intelligent skill selection and execution for zipai optimizer 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_zipai_task(
    task_payload: Dict[str, Any],
    available_modules: List[Dict[str, Any]],
    routing_config: Dict[str, Any]
) -> Dict[str, Any]:
    """Route a zipai optimization task to the most suitable processing module.
    
    Evaluates modules against zipai-specific routing criteria:
    - Context window alignment (token budget matching)
    - Historical latency percentiles for similar task shapes
    - Dependency graph compatibility (upstream/downstream readiness)
    - Current queue depth and resource availability
    
    Args:
        task_payload: Parsed zipai task with intent, entities, and constraints
        available_modules: List of active optimizer module metadata
        routing_config: Thresholds and weights for multi-factor scoring
        
    Returns:
        Routing decision with selected module, confidence score, and execution hints
    """
    if not task_payload.get("intent"):
        raise ValueError("Zipai task payload missing required 'intent' field")
        
    task_shape = _extract_task_shape(task_payload)
    best_module = None
    best_score = 0.0
    
    for module in available_modules:
        # Zipai-specific scoring: context alignment + historical performance + queue health
        context_score = _calculate_context_alignment(task_shape, module["capabilities"])
        history_score = module.get("p95_latency_score", 0.5)
        queue_score = 1.0 - (module.get("current_queue_depth", 0) / routing_config.get("max_queue_depth", 100))
        
        composite = (
            context_score * routing_config.get("w_context", 0.4) +
            history_score * routing_config.get("w_history", 0.35) +
            queue_score * routing_config.get("w_queue", 0.25)
        )
        
        if composite > best_score and composite >= routing_config.get("min_confidence", 0.7):
            best_score = composite
            best_module = module
            
    if best_module is None:
        return {"status": "unroutable", "reason": "no_module_meets_threshold", "score": best_score}
        
    # Return immutable routing decision
    return {
        "status": "routed",
        "selected_module": best_module["name"],
        "confidence": round(best_score, 3),
        "execution_hints": best_module.get("routing_hints", {}),
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def execute_zipai_module(
    routing_decision: Dict[str, Any],
    task_context: Dict[str, Any],
    fallback_modules: List[Dict[str, Any]]
) -> Dict[str, Any]:
    """Execute a zipai optimizer module with built-in resilience and fallback routing.
    
    Implements zipai execution contract:
    - Validates module state and context compatibility before dispatch
    - Handles transient failures with exponential backoff
    - Falls back to alternative modules if primary fails after retries
    - Maintains execution trace for audit and confidence recalibration
    
    Args:
        routing_decision: Output from route_zipai_task
        task_context: Full execution context including inputs and state
        fallback_modules: Ordered list of alternative modules for fallback routing
        
    Returns:
        Execution result with status, output, timing, and confidence update
    """
    primary_module = routing_decision.get("selected_module")
    if not primary_module:
        raise ValueError("Routing decision missing selected_module")
        
    execution_trace = {
        "primary": primary_module,
        "attempts": 0,
        "fallbacks_used": [],
        "start_time": time.time()
    }
    
    max_retries = routing_decision.get("execution_hints", {}).get("max_retries", 2)
    
    for attempt in range(max_retries + 1):
        execution_trace["attempts"] = attempt + 1
        try:
            result = _dispatch_module(primary_module, task_context)
            return {
                "status": "success",
                "module": primary_module,
                "output": result,
                "latency_ms": (time.time() - execution_trace["start_time"]) * 1000,
                "confidence_delta": 0.05,
                "trace": execution_trace
            }
        except ModuleTimeoutError:
            if attempt < max_retries:
                continue
            # Exhausted retries, trigger fallback chain
            for fallback in fallback_modules:
                try:
                    result = _dispatch_module(fallback["name"], task_context)
                    execution_trace["fallbacks_used"].append(fallback["name"])
                    return {
                        "status": "success_fallback",
                        "module": fallback["name"],
                        "output": result,
                        "latency_ms": (time.time() - execution_trace["start_time"]) * 1000,
                        "confidence_delta": -0.02,
                        "trace": execution_trace
                    }
                except Exception as e:
                    continue
                    
    return {
        "status": "failed",
        "module": primary_module,
        "error": "All retries and fallbacks exhausted",
        "trace": execution_trace
    }

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.

• Attention Is All You Need — Transformer Architecture (Vaswani et al.)
• Google's BERT Paper — Pre-training of Deep Bidirectional Transformers
• Tokenization in LLMs — Hugging Face Docs
• Context Window Management — Anyscale Blog
• Anthropic Constitutional AI — Harmlessness Training

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