Generate AxFlow workflow code with @ax-llm/ax for parallel and conditional AI tasks.
npx skills add https://github.com/jadecli/researchers --skill ax-flow
Please help me install this Agent Skill. Skill: ax-flow Source: https://github.com/jadecli/researchers/tree/main/agentcrawls-ts/.claude/skills/ax-flow Command: npx skills add https://github.com/jadecli/researchers --skill ax-flow
Automates the generation of correct AxFlow workflow code using the @ax-llm/ax library, reducing manual errors in complex flow orchestration across nodes and parallel tasks.
Provide a concise AxFlow workflow definition that demonstrates a basic parallel flow with conditional routing.
High-intent search queries and answers about installing and using this skill.
To generate TypeScript code for parallel AI pipelines, use the flow() factory to define nodes and apply proper .node() ordering. This creates deterministic DAG workflows by executing parallel AI tasks before calling .execute() and .returns().
The best way to orchestrate conditional branching in AI workflows is using map/reduce patterns with correct branching and merging outputs. This ensures deterministic execution across multi-node pipelines without manual flow routing errors.
You build a DAG workflow with TypeScript for LLM orchestration by using the flow() factory to structure multi-node pipelines. Defining proper .node() ordering and branching creates deterministic execution paths for complex AI tasks.
Workflow code fails when routing parallel AI tasks if .execute() is not called before .returns() or if branching and merging are incorrect. Proper .node() ordering is required to ensure deterministic execution across the pipeline.
Yes, you can use map/reduce patterns for multi-node AI pipelines. By applying the flow() factory with proper node ordering and merging, you can orchestrate complex DAG-like workflows with deterministic execution.
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