A Four-Skill TRAE Work Pipeline That Writes Release Notes in Two Minutes
The workflow treats AI output as a first draft to be processed, not a final product to be tweaked, which sidesteps the burnout of editing generic LLM prose. Developers who publish on platforms with idiosyncratic formatting rules, like WeChat, get a concrete pattern for chaining small, single-purpose skills instead of wrestling with one giant prompt.
Asking an LLM to produce a publishable article in one shot usually yields grammatically correct filler that sounds nothing like the author. The `wot-ui` maintainer behind this workflow stopped treating AI as a ghostwriter and started treating it as a drafting and editing pipeline inside TRAE Work. Four custom skills handle distinct stages: `humanizer-zh` purges stock phrases and hollow conclusions from a rough draft, `writing-style` reworks the text to match the author's own cadence and vocabulary, `style-extractor` reverse-engineers structural techniques from articles worth learning from, and `wechat-link-formatter` converts Markdown links into the plaintext format WeChat public accounts require. The author supplies the raw material — cases, opinions, real experience — and the pipeline assembles, de-fluffs, and polishes it. Release announcements that once took half a day now finish in about two minutes, with the remaining time going back to actual thinking and building.
The core shift is lowering expectations: the author stopped asking the model to write a finished piece and instead asked it to organize supplied material into a draft, which made the output immediately more useful.
Breaking writing into discrete, toolchain-style steps mirrors how developers already work with code — linting, formatting, refactoring — and applies that same sensibility to prose.
The `style-extractor` skill is used for study, not imitation; the author cherry-picks structural techniques and adds them to `writing-style`, which keeps the output from drifting toward someone else's voice.