temporal-golang-pro
Use when building durable distributed systems with Temporal Go SDK. Covers deterministic workflow rules, mTLS worker configs, and advanced patterns.
Project & Program ManagementOperations & Supply ChainAI & Automation
Temporal Go SDK (temporal-golang-pro)
Overview
Expert-level guide for building resilient, scalable, and deterministic distributed systems using the Temporal Go SDK. This skill transforms vague orchestration requirements into production-grade Go implementations, focusing on durable execution, strict determinism, and enterprise-scale worker configuration.
When to Use This Skill
- Designing Distributed Systems: When building microservices that require durable state and reliable orchestration.
- Implementing Complex Workflows: Using the Go SDK to handle long-running processes (days/months) or complex Saga patterns.
- Optimizing Performance: When workers need fine-tuned concurrency, mTLS security, or custom interceptors.
- Ensuring Reliability: Implementing idempotent activities, graceful error handling, and sophisticated retry policies.
- Maintenance & Evolution: Versioning running workflows or performing zero-downtime worker updates.
Do not use this skill when
- Using Temporal with other SDKs (Python, Java, TypeScript) - refer to their specific
-proskills. - The task is a simple request/response without durability or coordination needs.
- High-level design without implementation (use
workflow-orchestration-patterns).
Step-by-Step Guide
- Gather Context: Proactively ask for:
- Target Temporal Cluster (Cloud vs. Self-host
Subscribers only
The full skill, its 3 bundled files and every download is included with every paid Complete AI plan.
Details
| Source | self |
|---|---|
| License | — |
| Risk label | safe ("critical" means the skill may run commands or touch files — read before use) |
| Files | SKILL.md, resources/implementation-playbook.md, resources/testing-strategies.md |
| Added | 2026-02-27 |
Related skills
acceptance-orchestrator
Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
address-github-comments
Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.
ai-loop
Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.
airflow-dag-patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
antigravity-workflows
Use when asked to ship a SaaS MVP, audit application security, build an AI agent, run browser QA, or design a domain model with multiple skills and verified checkpoints.
ask-questions-if-underspecified
Clarify requirements before implementing. Use when serious doubts arise.
