Qwen3.6 35B Transforms Workflows Through Skill-Based Prompting

How One User Rebuilt Their Workflow

A LocalLLaMA user shared a detailed account of how Qwen3.6 35B A3B changed not just their coding workflow but their overall computer usage — earning over 340 upvotes. The approach is less about raw model capability and more about a structured system built on top of it.

The Three-Layer Workflow

First, use Codex to perform specific tasks while documenting the process — including errors — as a reusable "skill." Second, feed that skill to the pi agent. Third, Qwen3.6 equipped with these skills reliably handles tasks that previously required significant trial and error.

Real Tasks Automated

DevOps management on a VPS

Converting old PDFs to EPUBs using Docling

The skill documentation acts as pre-loaded context: it tells the model what errors to expect and how to handle them before the task begins.

The Broader Point

Local LLM utility depends as much on how well users structure their knowledge systems as on model capability itself. Qwen3.6's reasoning paired with a well-maintained skill library and agent framework enables personal automation at a level that previously required much larger or proprietary models.