技能 fal-workflow
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fal-workflow

安全

Generate AI Model Workflow JSON

Build complex AI pipelines by chaining multiple models in sequence. Generate structured workflow JSON configurations for fal.ai model orchestration.

支援: Claude Codex Code(CC)
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測試它

正在使用「fal-workflow」。 Create a workflow for text-to-image generation

預期結果:

  • Workflow JSON with input node accepting text prompt
  • Connection mapping text output to image model input
  • Output node configured for image result delivery

正在使用「fal-workflow」。 Chain summarization and translation models

預期結果:

  • Workflow with sequential model definitions
  • Input/output parameter mappings between steps
  • Final output configured for translated summary

安全審計

安全
v1 • 2/24/2026

Documentation-only skill with no executable code. Static analysis produced false positives: URLs are markdown links (not network calls) and 'cryptographic algorithm' detections matched description text (not actual crypto code). No security risks identified.

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審計者: claude

品質評分

38
架構
100
可維護性
87
內容
50
社群
100
安全
83
規範符合性

你能建構什麼

Multi-Step Content Pipeline

Chain text generation, image creation, and refinement models in a single automated workflow

AI Model Testing Framework

Execute the same input across multiple models and compare outputs systematically

Data Processing Workflow

Orchestrate models for data transformation, analysis, and visualization in sequence

試試這些提示

Basic Workflow Definition 
Create a workflow JSON file that chains two AI models: first generate text content, then create an image based on that text output.
Multi-Model Sequence 
Generate a workflow configuration for three sequential models: text summarization, sentiment analysis, and report generation. Map outputs to inputs between each step.
Conditional Workflow Branching 
Create a workflow JSON with conditional branching: if sentiment analysis returns negative, route to apology generation model; otherwise route to thank you message model.
Parallel Model Execution 
Design a workflow that runs three different image generation models in parallel from the same text prompt, then aggregates all outputs.

最佳實務

  • Define clear input/output schemas for each model step to ensure compatibility
  • Include error handling and fallback configurations for production workflows
  • Document workflow purpose and each model's role for maintainability

避免

  • Creating circular dependencies between workflow steps
  • Hardcoding API credentials in workflow JSON files
  • Skipping validation of intermediate outputs before passing to next model

常見問題

What is fal.ai workflow JSON?
A structured configuration format that defines how multiple AI models execute in sequence, including input/output mappings and execution order.
Can I use this skill without a fal.ai account?
You can generate workflow JSON structures, but executing the workflows requires an active fal.ai account with API access.
How many models can I chain in one workflow?
fal.ai supports multiple sequential models. Practical limits depend on API rate limits and your specific use case requirements.
Can workflows run models in parallel?
Yes, workflow JSON can define parallel execution branches that run simultaneously and aggregate results.
How do I handle errors in a workflow?
Include error handling configurations in your workflow JSON with fallback models or termination conditions for each step.
Is workflow JSON reusable across projects?
Yes, workflow configurations are portable. You can version control them and adapt for different projects with minor parameter changes.

開發者詳情

作者

sickn33

授權

MIT

儲存庫

https://github.com/sickn33/antigravity-awesome-skills/tree/main/web-app/public/skills/fal-workflow

引用

main

檔案結構

📄 SKILL.md