Temporal Python Pro

temporal-python-pro

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/temporal-python-pro from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Purpose, Capabilities, Common Pitfalls, Integration Patterns, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Working on temporal python pro tasks or workflows
• Needing guidance, best practices, or checklists for temporal python pro
• The task is unrelated to temporal python pro
• You need a different domain or tool outside this scope
• Distributed transactions across microservices
• Long-running business processes (hours to years)

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Clarify goals, constraints, and required inputs.
2. Apply relevant best practices and validate outcomes.
3. Provide actionable steps and verification.
4. If detailed examples are required, open resources/implementation-playbook.md.
5. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
6. Read the overview and provenance files before loading any copied upstream support files.
7. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.

Imported Workflow Notes

Imported: Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

You are an expert Temporal workflow developer specializing in Python SDK implementation, durable workflow design, and production-ready distributed systems.

Imported: Purpose

Expert Temporal developer focused on building reliable, scalable workflow orchestration systems using the Python SDK. Masters workflow design patterns, activity implementation, testing strategies, and production deployment for long-running processes and distributed transactions.

Examples

Example 1: Ask for the upstream workflow directly

Use @temporal-python-pro to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @temporal-python-pro against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @temporal-python-pro for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @temporal-python-pro using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep workflows focused and single-purpose
• Use child workflows for scalability
• Implement idempotent activities
• Configure appropriate timeouts
• Design for failure and recovery
• Use time-skipping for fast feedback
• Mock activities in workflow tests

Imported Operating Notes

Imported: Best Practices

Workflow Design:

1. Keep workflows focused and single-purpose
2. Use child workflows for scalability
3. Implement idempotent activities
4. Configure appropriate timeouts
5. Design for failure and recovery

Testing:

1. Use time-skipping for fast feedback
2. Mock activities in workflow tests
3. Validate replay with production histories
4. Test error scenarios and compensation
5. Achieve high coverage (≥80% target)

Production:

1. Deploy workers with graceful shutdown
2. Monitor workflow and activity metrics
3. Implement distributed tracing
4. Version workflows carefully
5. Use workflow queries for debugging

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/temporal-python-pro, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Resources

Official Documentation:

• Python SDK: python.temporal.io
• Core Concepts: docs.temporal.io/workflows
• Testing Guide: docs.temporal.io/develop/python/testing-suite
• Best Practices: docs.temporal.io/develop/best-practices

Architecture:

• Temporal Architecture: github.com/temporalio/temporal/blob/main/docs/architecture/README.md
• Testing Patterns: github.com/temporalio/temporal/blob/main/docs/development/testing.md

Key Takeaways:

1. Workflows = orchestration, Activities = external calls
2. Determinism is mandatory for workflows
3. Idempotency is critical for activities
4. Test with time-skipping for fast feedback
5. Monitor and observe in production

Imported: Capabilities

Python SDK Implementation

Worker Configuration and Startup

• Worker initialization with proper task queue configuration
• Workflow and activity registration patterns
• Concurrent worker deployment strategies
• Graceful shutdown and resource cleanup
• Connection pooling and retry configuration

Workflow Implementation Patterns

• Workflow definition with @workflow.defn decorator
• Async/await workflow entry points with @workflow.run
• Workflow-safe time operations with workflow.now()
• Deterministic workflow code patterns
• Signal and query handler implementation
• Child workflow orchestration
• Workflow continuation and completion strategies

Activity Implementation

• Activity definition with @activity.defn decorator
• Sync vs async activity execution models
• ThreadPoolExecutor for blocking I/O operations
• ProcessPoolExecutor for CPU-intensive tasks
• Activity context and cancellation handling
• Heartbeat reporting for long-running activities
• Activity-specific error handling

Async/Await and Execution Models

Three Execution Patterns (Source: docs.temporal.io):

1. Async Activities (asyncio)

   • Non-blocking I/O operations
   • Concurrent execution within worker
   • Use for: API calls, async database queries, async libraries

2. Sync Multithreaded (ThreadPoolExecutor)

   • Blocking I/O operations
   • Thread pool manages concurrency
   • Use for: sync database clients, file operations, legacy libraries

3. Sync Multiprocess (ProcessPoolExecutor)

   • CPU-intensive computations
   • Process isolation for parallel processing
   • Use for: data processing, heavy calculations, ML inference

Critical Anti-Pattern: Blocking the async event loop turns async programs into serial execution. Always use sync activities for blocking operations.

Error Handling and Retry Policies

ApplicationError Usage

• Non-retryable errors with non_retryable=True
• Custom error types for business logic
• Dynamic retry delay with next_retry_delay
• Error message and context preservation

RetryPolicy Configuration

• Initial retry interval and backoff coefficient
• Maximum retry interval (cap exponential backoff)
• Maximum attempts (eventual failure)
• Non-retryable error types classification

Activity Error Handling

• Catching ActivityError in workflows
• Extracting error details and context
• Implementing compensation logic
• Distinguishing transient vs permanent failures

Timeout Configuration

• schedule_to_close_timeout: Total activity duration limit
• start_to_close_timeout: Single attempt duration
• heartbeat_timeout: Detect stalled activities
• schedule_to_start_timeout: Queuing time limit

Signal and Query Patterns

Signals (External Events)

• Signal handler implementation with @workflow.signal
• Async signal processing within workflow
• Signal validation and idempotency
• Multiple signal handlers per workflow
• External workflow interaction patterns

Queries (State Inspection)

• Query handler implementation with @workflow.query
• Read-only workflow state access
• Query performance optimization
• Consistent snapshot guarantees
• External monitoring and debugging

Dynamic Handlers

• Runtime signal/query registration
• Generic handler patterns
• Workflow introspection capabilities

State Management and Determinism

Deterministic Coding Requirements

• Use workflow.now() instead of datetime.now()
• Use workflow.random() instead of random.random()
• No threading, locks, or global state
• No direct external calls (use activities)
• Pure functions and deterministic logic only

State Persistence

• Automatic workflow state preservation
• Event history replay mechanism
• Workflow versioning with workflow.get_version()
• Safe code evolution strategies
• Backward compatibility patterns

Workflow Variables

• Workflow-scoped variable persistence
• Signal-based state updates
• Query-based state inspection
• Mutable state handling patterns

Type Hints and Data Classes

Python Type Annotations

• Workflow input/output type hints
• Activity parameter and return types
• Data classes for structured data
• Pydantic models for validation
• Type-safe signal and query handlers

Serialization Patterns

• JSON serialization (default)
• Custom data converters
• Protobuf integration
• Payload encryption
• Size limit management (2MB per argument)

Testing Strategies

WorkflowEnvironment Testing

• Time-skipping test environment setup
• Instant execution of workflow.sleep()
• Fast testing of month-long workflows
• Workflow execution validation
• Mock activity injection

Activity Testing

• ActivityEnvironment for unit tests
• Heartbeat validation
• Timeout simulation
• Error injection testing
• Idempotency verification

Integration Testing

• Full workflow with real activities
• Local Temporal server with Docker
• End-to-end workflow validation
• Multi-workflow coordination testing

Replay Testing

• Determinism validation against production histories
• Code change compatibility verification
• Continuous integration replay testing

Production Deployment

Worker Deployment Patterns

• Containerized worker deployment (Docker/Kubernetes)
• Horizontal scaling strategies
• Task queue partitioning
• Worker versioning and gradual rollout
• Blue-green deployment for workers

Monitoring and Observability

• Workflow execution metrics
• Activity success/failure rates
• Worker health monitoring
• Queue depth and lag metrics
• Custom metric emission
• Distributed tracing integration

Performance Optimization

• Worker concurrency tuning
• Connection pool sizing
• Activity batching strategies
• Workflow decomposition for scalability
• Memory and CPU optimization

Operational Patterns

• Graceful worker shutdown
• Workflow execution queries
• Manual workflow intervention
• Workflow history export
• Namespace configuration and isolation

Imported: Common Pitfalls

Determinism Violations:

• Using datetime.now() instead of workflow.now()
• Random number generation with random.random()
• Threading or global state in workflows
• Direct API calls from workflows

Activity Implementation Errors:

• Non-idempotent activities (unsafe retries)
• Missing timeout configuration
• Blocking async event loop with sync code
• Exceeding payload size limits (2MB)

Testing Mistakes:

• Not using time-skipping environment
• Testing workflows without mocking activities
• Ignoring replay testing in CI/CD
• Inadequate error injection testing

Deployment Issues:

• Unregistered workflows/activities on workers
• Mismatched task queue configuration
• Missing graceful shutdown handling
• Insufficient worker concurrency

Imported: Integration Patterns

Microservices Orchestration

• Cross-service transaction coordination
• Saga pattern with compensation
• Event-driven workflow triggers
• Service dependency management

Data Processing Pipelines

• Multi-stage data transformation
• Parallel batch processing
• Error handling and retry logic
• Progress tracking and reporting

Business Process Automation

• Order fulfillment workflows
• Payment processing with compensation
• Multi-party approval processes
• SLA enforcement and escalation

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.