Click any tag below to further narrow down your results
Links
This is a GitHub project that teaches Claude (or other LLMs) to write in Simplified Technical English (STE), a controlled language used by aerospace and defense industries for clear, translatable technical documentation. It includes writing rules, an approved word list, and a Python checker tool to validate compliance.
- The skill enforces 53 writing rules and a dictionary of 869 approved words based on the official ASD-STE100 specification, making technical text shorter and easier to translate
- It includes a Python validation tool that catches common violations like overly long sentences, unapproved verbs, passive voice, and words outside the approved list
- The tool has real limitations—it can't verify if words are used with their approved meanings, and a human expert still needs to review critical documents for full specification compliance
As AI models get better at coding, engineers need to stop pretending it's not happening and instead identify what they can do better than the models—mainly deep knowledge of their specific codebase and the ability to write clearly about technical decisions.
- AI models fail on "errors of ignorance" (not knowing which module to use, missing company conventions) and "errors of paranoia" (over-engineering simple systems), mistakes only caught by someone deeply familiar with the actual codebase and willing to confidently disagree with the AI.
- LLMs are paradoxically getting worse at writing even as they improve at coding, because writing quality can't be automatically verified and labs prioritize capability over communication—making human-written technical docs increasingly rare and valuable.
- Being a "meat proxy" who just runs prompts through AI and submits the output is worse than not using AI at all, since you're providing zero value and will be replaced once those workflows get automated into enterprise tools.