Temporal Golang Pro

Temporal Golang Pro

Orchestrates intelligent skill selection and execution for temporal golang pro 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

// EvaluateActivityCandidates selects the optimal Temporal activity based on
// multi-factor scoring: trigger match, historical success rate, and dependency health.
func EvaluateActivityCandidates(ctx context.Context, request string, candidates []ActivityMetadata) (*ActivityMetadata, error) {
	if request == "" {
		return nil, fmt.Errorf("request cannot be empty")
	}
	if len(candidates) == 0 {
		return nil, fmt.Errorf("no candidate activities available")
	}

	// Parse request features (Law 2: Make illegal states unrepresentable)
	features := ExtractRequestFeatures(request)
	var bestMatch *ActivityMetadata
	var highestScore float64

	for i := range candidates {
		c := &candidates[i]
		if !c.IsAvailable(ctx) {
			continue
		}

		score := CalculateMatchScore(features, c.Triggers) * c.HistoricalSuccessRate
		if score > highestScore && score >= 0.7 {
			highestScore = score
			bestMatch = c
		}
	}

	if bestMatch == nil {
		return nil, fmt.Errorf("no activity met minimum confidence threshold")
	}

	// Return new struct, never mutate input (Law 3: Atomic Predictability)
	return &ActivityMetadata{
		Name:              bestMatch.Name,
		Version:           bestMatch.Version,
		SelectedConfidence: highestScore,
		SelectionTime:     time.Now(),
	}, nil
}

Pattern 2: Execution with Fallback

// ExecuteWithFallback runs a Temporal activity with a structured retry and fallback chain.
// Implements Fail Fast, Fail Loud (Law 4) and Early Exit (Law 1).
func ExecuteWithFallback(ctx workflow.Context, activity ActivityMetadata, input ActivityInput) (*ActivityResult, error) {
	if !IsValidActivity(activity) {
		return nil, fmt.Errorf("invalid activity metadata: %s", activity.Name)
	}

	// Validate input at boundary (Law 2)
	validatedInput, err := ParseActivityInput(input)
	if err != nil {
		return nil, fmt.Errorf("failed to parse input for %s: %w", activity.Name, err)
	}

	retryPolicy := &temporal.RetryPolicy{
		InitialInterval:    time.Second,
		BackoffCoefficient: 2.0,
		MaximumInterval:    time.Minute,
		MaximumAttempts:    3,
	}

	var lastErr error
	for attempt := 0; attempt <= retryPolicy.MaximumAttempts; attempt++ {
		var result interface{}
		err := workflow.ExecuteActivity(ctx, temporal.ActivityOptions{
			RetryPolicy: retryPolicy,
			TaskQueue:   activity.TaskQueue,
		}, activity.Name, validatedInput).Get(ctx, &result)

		if err == nil {
			return &ActivityResult{
				Success:  true,
				Data:     result,
				Attempts: attempt + 1,
				Latency:  time.Since(ctx.Value(startTimeKey).(time.Time)),
			}, nil
		}

		lastErr = err
		if IsTransientError(err) {
			continue
		}
		// Permanent error - fail fast, do not retry
		break
	}

	// Fallback chain: try alternative activity or return structured error
	if activity.FallbackActivity != "" {
		var fallbackResult interface{}
		err := workflow.ExecuteActivity(ctx, temporal.ActivityOptions{
			RetryPolicy: retryPolicy,
			TaskQueue:   activity.TaskQueue,
		}, activity.FallbackActivity, validatedInput).Get(ctx, &fallbackResult)
		if err == nil {
			return &ActivityResult{Success: true, Data: fallbackResult, Fallback: true}, nil
		}
	}

	return nil, fmt.Errorf("activity %s failed after retries and fallback: %w", activity.Name, lastErr)
}

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

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 domain. The model follows markdown links at load time to resolve external references and inline content.

• Temporal.io Workflow Documentation — Official Temporal documentation on building reliable workflows with Go
• Go Concurrency Patterns (Effective Go) — Effective Go guide to goroutines, channels, and concurrency patterns used in Temporal workers
• Temporal Go SDK Reference — Official Temporal Go SDK API reference documentation
• Durable Execution Patterns (Temporal Blog) — Temporal's engineering blog on durable execution, saga patterns, and workflow orchestration
• Go Testing Patterns for Workers — Official Go tutorial including testing patterns for Temporal workflow applications