Generate typed AxFlow workflow code for AI pipelines.
npx skills add https://github.com/ax-llm/ax --skill ax-flow-ax-llm
Please help me install this Agent Skill. Skill: ax-flow Source: https://github.com/ax-llm/ax/tree/main/website/static/typescript/.well-known/agent-skills/ax-flow Command: npx skills add https://github.com/ax-llm/ax --skill ax-flow-ax-llm
This Skill helps you write correct AxFlow workflows without wrestling with state wiring, execution order, or output typing mistakes.
Ask me to generate an AxFlow workflow for your task, and include the inputs, outputs, and any branching or parallel steps you want.
High-intent search queries and answers about installing and using this skill.
You can generate a typed AI pipeline using AxFlow workflows to handle conditional routing, parallel execution, and map/reduce patterns. This approach uses node-before-execute ordering and flat state conventions to ensure reliable multi-node DAG pipelines.
The best way to structure a multi-node DAG workflow is by using AxFlow orchestration with flat state conventions and .returns() output locking. This creates a reliable program that safely summarizes, classifies, or routes multiple node outputs.
To prevent state wiring and execution order mistakes, generate AxFlow workflows that enforce node-before-execute ordering and safe auto-parallel execution rules. This ensures your branching LLM pipelines run without manual state wiring errors.
Yes, AxFlow supports map/reduce patterns, loops, and agent workflows within typed AI pipelines. It applies safe auto-parallel execution rules to generate reliable workflow code for complex looping and branching structures.
Incorrect typing during parallel execution often stems from missing output locking. AxFlow uses .returns() output locking and flat state conventions to ensure typed AI pipelines maintain strict input and output contracts across parallel nodes.
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