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

1. Detect jargon — run scripts/jargon_detector.py on the draft text to flag technical terms that need translation.
2. Score readability — run scripts/readability_scorer.py to get Flesch-Kincaid grade level and sentence complexity metrics; target ≤ grade 10 for executive audiences.
3. 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.
4. 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").
5. Replace with metaphors where needed — for complex statistical concepts, pick an appropriate metaphor from references/metaphor_bank.md.
6. 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)