Skillware adds entropy-scoring synthetic data generator

Hot take: this is a practical answer to one of the weakest parts of synthetic-data workflows, because it treats diversity as something you can measure instead of hoping prompt variation is enough.

• –The local-first Ollama path makes it useful for private or offline fine-tuning setups.
• –The entropy scoring step is the most interesting part here; it adds a quality gate before dataset export.
• –JSON batch output is a good fit for supervised fine-tuning and other pipeline-driven workflows.
• –The cloud-model fallback broadens the tool for cases where you want stronger reasoning during generation.
• –This is most relevant to people building datasets for smaller local models, where repetitive synthetic data can quickly hurt downstream quality.