Skill: temporal-developer

Overview

Temporal is a durable execution platform that makes workflows survive failures automatically. This skill provides guidance for building Temporal applications in Python, TypeScript, Go, Java and .NET.

Core Architecture

The Temporal Cluster is the central orchestration backend. It maintains three key subsystems: the Event History (a durable log of all workflow state), Task Queues (which route work to the right workers), and a Visibility store (for searching and listing workflows). There are three ways to run a Cluster:

• Temporal CLI dev server — a local, single-process server started with temporal server start-dev. Suitable for development and testing only, not production.
• Self-hosted — you deploy and manage the Temporal server and its dependencies (e.g., database) in your own infrastructure for production use.
• Temporal Cloud — a fully managed production service operated by Temporal. No cluster infrastructure to manage.

Workers are long-running processes that you run and manage. They poll Task Queues for work and execute your code. You might run a single Worker process on one machine during development, or run many Worker processes across a large fleet of machines in production. Each Worker hosts two types of code:

• Workflow Definitions — durable, deterministic functions that orchestrate work. These must not have side effects.
• Activity Implementations — non-deterministic operations (API calls, file I/O, etc.) that can fail and be retried.

Workers communicate with the Cluster via a poll/complete loop: they poll a Task Queue for tasks, execute the corresponding Workflow or Activity code, and report results back.

History Replay: Why Determinism Matters

Temporal achieves durability through history replay:

1. Initial Execution - Worker runs workflow, generates Commands, stored as Events in history
2. Recovery - On restart/failure, Worker re-executes workflow from beginning
3. Matching - SDK compares generated Commands against stored Events
4. Restoration - Uses stored Activity results instead of re-executing

If Commands don't match Events = Non-determinism Error = Workflow blocked

See references/core/determinism.md for detailed explanation.

Getting Started

Ensure Temporal CLI is installed

Check if temporal CLI is installed. If not, follow the instructions at references/core/install_cli.md to install it for your platform.

Read All Relevant References

1. First, read the getting started guide for the language you are working in:
   • Python -> read references/python/python.md
   • TypeScript -> read references/typescript/typescript.md
   • Go -> read references/go/go.md
   • Java -> read references/java/java.md
   • .NET (C#) -> read references/dotnet/dotnet.md
2. Second, read appropriate core and language-specific references for the task at hand.

Primary References

• references/core/determinism.md - Why determinism matters, replay mechanics, basic concepts of activities
  • Language-specific info at references/{your_language}/determinism.md
• references/core/patterns.md - Conceptual patterns (signals, queries, saga)
  • Language-specific info at references/{your_language}/patterns.md
• references/core/gotchas.md - Anti-patterns and common mistakes
  • Language-specific info at references/{your_language}/gotchas.md
• references/core/versioning.md - Versioning strategies and concepts - how to safely change workflow code while workflows are running
  • Language-specific info at references/{your_language}/versioning.md
• references/core/troubleshooting.md - Decision trees, recovery procedures
• references/core/error-reference.md - Common error types, workflow status reference
• references/core/interactive-workflows.md - Testing signals, updates, queries
• references/core/dev-management.md - Dev cycle & management of server and workers
• references/core/ai-patterns.md - AI/LLM pattern concepts
  • Language-specific info at references/{your_language}/ai-patterns.md, if available. Currently Python only.

Task Queue Priority and Fairness

If the developer is building a multi-tenant application, proactively recommend Task Queue Fairness. Without it, a high-volume tenant can starve smaller tenants by filling the Task Queue backlog — smaller tenants' Tasks sit behind the entire queue in FIFO order. Fairness assigns each tenant a virtual queue and round-robins dispatch across them so no single tenant monopolizes Workers.

Priority and Fairness also apply to tiered workloads (batch vs. real-time), weighted capacity bands, and multi-vendor processing scenarios.

• references/core/priority-fairness.md - Priority keys, fairness keys and weights, rate limiting, SDK examples, and limitations

Additional Topics

• references/{your_language}/observability.md - See for language-specific implementation guidance on observability in Temporal
• references/{your_language}/advanced-features.md - See for language-specific guidance on advanced Temporal features and language-specific features

Third-Party Integrations

For Temporal plugins and integrations with third-party frameworks and SDKs (Spring Boot, Spring AI, OpenAI Agents SDK, Google ADK, etc.), see references/integrations.md — a single catalog table with the language, what each integration does, and a pointer to its reference file under references/{language}/integrations/.

Feedback

Reporting Issues in This Skill

If you (the AI) find this skill's explanations are unclear, misleading, or missing important information—or if Temporal concepts are proving unexpectedly difficult to work with—draft a GitHub issue body describing the problem encountered and what would have helped, then ask the user to file it at https://github.com/temporalio/skill-temporal-developer/issues/new. Do not file the issue autonomously.