Convert natural-language objectives into canvas-loadable .flw workflows with FlowCreator.
npx skills add https://github.com/XAIHT/Tlamatini --skill flow-making
Please help me install this Agent Skill. Skill: flow-making Source: https://github.com/XAIHT/Tlamatini/tree/main/Tlamatini/agent/skills_pkg/flow_making Command: npx skills add https://github.com/XAIHT/Tlamatini --skill flow-making
FlowMaker converts natural-language objectives into canvas-loadable .flw workflows by leveraging the FlowCreator engine, removing manual steps and enabling rapid deployment.
Provide a natural-language objective and let FlowMaker produce a downloadable .flw file ready to load in the ACP canvas.
High-intent search queries and answers about installing and using this skill.
To convert natural-language objectives into canvas-loadable workflows, provide your objective to let the engine generate a downloadable .flw file. This removes manual steps and enables rapid deployment directly onto the canvas.
The best way to generate a .flw file for ACP canvas is using the FlowCreator engine to process your text objective. It provides an export-ready output with proper node wiring for immediate loading.
Yes, you need both Ollama and Python to generate .flw workflows. The workflow generation requires a FlowCreator template, an Ollama model, and a Python runtime to execute the end-to-end process.
Yes, you can run isolated workflow generation without mutating the FlowCreator template. The engine performs isolated runtime executions to avoid mutating templates and ensure safe, repeatable runs.
Node wiring errors on the canvas occur when the workflow generation lacks proper export-ready output. The engine provides a loadable .flw with proper node wiring and robust error handling to prevent this.
Yes, you can use configurable models for .flw workflow generation. The engine operates end-to-end with configurable models to process your natural-language objectives into loadable workflows.
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