SKILL.md
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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:
- Initial Execution - Worker runs workflow, generates Commands, stored as Events in history
- Recovery - On restart/failure, Worker re-executes workflow from beginning
- Matching - SDK compares generated Commands against stored Events
- Restoration - Uses stored Activity results instead of re-executing
If Commands don't match Events = Non-determinism Error = Workflow blocked
Workflow Code
Command
Event
Execute activity
ScheduleActivityTask
ActivityTaskScheduled
Sleep/timer
StartTimer
TimerStarted
Child workflow
StartChildWorkflowExecution
ChildWorkflowExecutionStarted
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
- 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
- Second, read appropriate
coreand 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.