ASD-STE100 Simplified Technical English for LLMs

A new open-source toolchain applies ASD-STE100 Simplified Technical English to large language model outputs, achieving a 74% reduction in writing violations on Claude Sonnet and a 50% reduction on GPT-5.5, according to tests by the developer. The package includes a distilled agent skill, a deterministic linter, and reproduction scripts, all requiring only Python 3.

Everything the episode promises "in the description": the distilled STE skill, the linter, and the full first-party test data. Self-contained - no external dependencies beyond Python 3. | File | What it is | |---|---| ste-writing-skill.md | The distilled ASD-STE100 agent skill, two modes: strict procedures, error messages and flavored prose, no dictionary lockdown | ste-lint.py | The heuristic anti-slop linter - the machine-checkable subset of STE. Deterministic; the score delta between two texts is the signal | experiment-results.md | The cross-model test: 6 writing tasks x 4 conditions on Claude and gpt-5.5 | experiment-results-openai.md | Per-category detail for the gpt-5.5 run | before-after-samples.md | The real baseline-vs-STE outputs quoted in the episode | run-openai.py | Reproduction script for the OpenAI side of the experiment | python3 ste-lint.py your-draft.md Score is violations per 100 words - lower is cleaner. Lint a draft, apply the skill, then lint it again - the delta between the two scores is the signal. | Condition | Claude sonnet | gpt-5.5 | |---|---|---| | baseline | 4.36 | 3.54 | | banned-words list | 4.21 -3% | 2.14 -40% | | Orwell's 6 rules | 2.48 -43% | 1.69 -52% | | STE skill | 1.12 -74% | 1.76 -50% | Give the model a writing system and slop drops by half or more, on every model tested. STE was best or tied-best. A banned-words list is the least reliable fix. Not a certified STE checker. The judgment rules of ASD-STE100 need a human; this covers the mechanical subset - which is where the slop lives. Spec: ASD-STE100 Issue 9, free at asd-ste100.org