| name | technical-to-business-translator |
| description | Translate technical analysis into business language. Use when explaining statistical concepts to non-analysts, simplifying technical findings, or bridging communication between data teams and business stakeholders. |
When to use
When technical output (model results, statistical tests, query findings) needs to be understood by a business audience. Also use to review your own writing before sending — it is easy to slip into jargon without noticing.
Process
- Detect jargon — run
scripts/jargon_detector.py on the draft text to flag technical terms that need translation.
- Score readability — run
scripts/readability_scorer.py to get Flesch-Kincaid grade level and sentence complexity metrics; target ≤ grade 10 for executive audiences.
- Identify the audience persona — use
references/stakeholder_personas.md to select the persona that best matches your reader; each persona has vocabulary preferences and typical questions.
- Apply translation patterns — use
references/translation_pattern_library.md to swap technical language for business equivalents (e.g., "p-value < 0.05" → "we're 95% confident this isn't random chance").
- Replace with metaphors where needed — for complex statistical concepts, pick an appropriate metaphor from
references/metaphor_bank.md.
- Draft the translated version — use
assets/translation_template.md to produce the parallel technical/business version; keep the original in an appendix for technical reviewers.
Inputs the skill needs
- Draft technical text or findings
- Target audience role (VP, product manager, operations, finance, etc.)
Output
- Jargon detection report
- Readability score before/after
- Translated text with original in appendix (
translation_template.md)
