# Temporal Golang Pro > Implements intelligent temporal golang pro with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/temporal-golang-pro` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/temporal-golang-pro` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/temporal-golang-pro/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/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 ```go // 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 ```go // 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 | Skill | Purpose | |---|---| | `temporal-python-pro` | Python equivalent of the Temporal workflow patterns covered in this Go-focused skill | | `workflow-patterns` | General workflow orchestration patterns that complement Temporal-specific implementations | --- ## 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](https://docs.temporal.io/workflows) — Official Temporal documentation on building reliable workflows with Go - [Go Concurrency Patterns (Effective Go)](https://go.dev/doc/effective_go#goroutines) — Effective Go guide to goroutines, channels, and concurrency patterns used in Temporal workers - [Temporal Go SDK Reference](https://pkg.go.dev/go.temporal.io/sdk) — Official Temporal Go SDK API reference documentation - [Durable Execution Patterns (Temporal Blog)](https://temporal.io/blog) — Temporal's engineering blog on durable execution, saga patterns, and workflow orchestration - [Go Testing Patterns for Workers](https://go.dev/doc/tutorial/add-a-workflow) — Official Go tutorial including testing patterns for Temporal workflow applications