I engineered a linting framework that strips AI markers and enforces brand personas—evals for prose, not vibes.
Portfolio home, about, and case study copy rewritten under hire-facing rules—same pattern enterprises use for LLM-as-judge and alignment pipelines.
5 voice profiles · contextual lint · hire vs product tone
- 5 profiles
- hire · product · brand hierarchy
- 6 lint rules
- vague · sleaze · AI markers · density
- 2,021
- User messages mined for voice gold
Lint model (v0)
No fixed word bans—contextual rejection when copy is too salesy, vague, flowery, or AI-marked for the surface.
- Vague stack — 3+ low-specificity adjectives without mechanism
- AI marker clusters — delve, landscape, leverage chains
- Filler density — repeated ensure/robust/comprehensive in short spans
- Sleaze / gimmick — manipulative edu-marketing tone
Profile hierarchy
Cross-rules govern all surfaces. Hire copy uses I voice on portfolio and resume. Product brands use we with empathy-forward Weable tone and enterprise-trust Clarifi split.
How I use it
Every portfolio page, case study, and chatbot response passes the same lint—fact-backed, I voice, no jargon meant for my repo. The CV filter doc is the gate before ship.