Style Review
Review and update documents to match the voice and standards defined in style_guide.md.
Quick Reference
Core Voice
- •Direct, calm, grounded
- •Confidence from specificity and outcomes, not tone
- •Plain language over jargon
- •No hype, theatrics, or "thought leadership" language
Writing Rules
- •Lead with outcome and impact, not activity
- •Describe systems, not tasks
- •Mention scale, constraints, and context when relevant
- •Prefer concrete verbs; avoid vague or inflated language
- •Emphasize lifecycle ownership, not handoffs
- •Treat evaluation as first-class
- •Be honest about what failed or was hard
- •Cut anything that doesn't change the reader's understanding
Review Checklist
Copy and complete this checklist:
Style Review: - [ ] No hype or performative language - [ ] Outcomes before activities - [ ] Systems, not tasks - [ ] Concrete verbs (built, shipped, designed, reduced) - [ ] Scale/context included where relevant - [ ] Honest about challenges - [ ] Every sentence earns its place - [ ] Sounds like Evan, not a marketing department
Language Examples
Phrases That Sound Right
- •"Design models, evaluation, and deployment together to move fast"
- •"Make it cheap to experiment and safe to fail"
- •"Unexpected behavior in prod is information"
- •"Simplify as much as possible, but no more"
- •"If you can't evaluate it, you don't understand it"
Red Flags to Rewrite
| Avoid | Why |
|---|---|
| "Spearheaded", "leveraged", "synergized" | Inflated verbs |
| "Thought leader", "visionary" | Hype language |
| "Passionate about..." | Performative |
| "This is the correct way" | Performative absolutism |
| Activity lists without outcomes | Missing impact |
| Vague claims without scale/context | Unearned confidence |
Before/After Examples
Before (activity-focused):
Worked on fraud detection systems and helped improve performance.
After (outcome-focused):
Built daily-refit fraud detection systems processing >1TB/day; prevented ~$14M in annual fraud losses.
Before (hype):
Passionate thought leader driving transformative AI initiatives across the enterprise.
After (grounded):
Build production ML systems; focus on evaluation, reliability, and real-world impact.
Review Process
- •Read the document completely
- •Run through the checklist above
- •Flag sentences that violate the rules
- •Rewrite flagged content using the patterns above
- •Verify all claims have appropriate specificity (numbers, scale, context)
- •Cut anything that doesn't change understanding
- •Read aloud — if it sounds like it's trying to impress someone, rewrite it
Source of Truth
The canonical style guide is at style_guide.md in the project root. If this skill conflicts with that document, the style guide takes precedence.