plugins/faos-architect/skills/workflow-orchestration-patterns/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- name: workflow-orchestration-patterns description: Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running business processes or distributed transactions. tags: [workflow, temporal, orchestration] --- # Workflow Orchestration Patterns Master workflow orchestration architecture with Temporal, c
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Master workflow orchestration architecture with Temporal, covering fundamental design decisions, resilience patterns, and best practices for building reliable distributed systems.
The Fundamental Rule (Source: temporal.io/blog/workflow-engine-principles):
Characteristics:
Example workflow tasks:
Characteristics:
Example activity tasks:
Does it touch external systems? -> Activity
Is it orchestration/decision logic? -> Workflow
Purpose: Implement distributed transactions with rollback capability
Pattern (Source: temporal.io/blog/compensating-actions-part-of-a-complete-breakfast-with-sagas):
For each step:
1. Register compensation BEFORE executing
2. Execute the step (via activity)
3. On failure, run all compensations in reverse order (LIFO)
Example: Payment Workflow
Critical Requirements:
Purpose: Long-lived workflow representing single entity instance
Pattern (Source: docs.temporal.io/evaluate/use-cases-design-patterns):
Example Use Cases:
Benefits:
Purpose: Execute multiple tasks in parallel, aggregate results
Pattern:
Scaling Rule (Source: temporal.io/blog/workflow-engine-principles):
Purpose: Wait for external event or human approval
Pattern:
Use Cases:
How Temporal Works (Source: docs.temporal.io/workflows):
Workflows Execute as State Machines:
Prohibited in Workflows (Source: docs.temporal.io/workflows):
random())datetime.now())Allowed in Workflows:
workflow.now() (deterministic time)workflow.random() (deterministic random)Challenge: Changing workflow code while old executions still running
Solutions:
workflow.get_version() for safe changesDefault Behavior: Temporal retries activities forever
Configure Retry:
Non-Retryable Errors:
Why Critical (Source: docs.temporal.io/activities):
Implementation Strategies:
Purpose: Detect stalled long-running activities
Pattern:
Workflow Violations:
datetime.now() instead of workflow.now()Activity Mistakes:
Monitoring:
Scalability:
Official Documentation:
Key Principles:
development
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<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: graphql-architect description: Master modern GraphQL with federation, performance optimization, --- ## Use this skill when - Working on graphql architect tasks or workflows - Needing guidance, best practices, or checklists for graphql architect ## Do not use this skill when - The task is unrelated to graphql architect - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and
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<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: grafana-dashboards description: Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces. --- # Grafana Dashboards Create and manage production-ready Grafana dashboards for comprehensive system observability. ## Do not use this skill when - The task is unrelated
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<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: gptq description: GPTQ post-training quantization for generative models. Use when quantizing large models to 4-bit with calibration-based weight compression. --- # GPTQ (Generative Pre-trained Transformer Quantization) Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization. ## When to use GPTQ **Use GPTQ when:** - Need to fit large models (70B+) on limited GPU