---
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.
title: technical-to-business-translator
canonical_url: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/technical-to-business-translator
last_updated: 2026-09-29T08:00:34.000Z
---

> **Skill from skilld.dev.** Follow the instructions below for this session. You do not need to install anything.
>
> Supporting files, fetch one when the Skill refers to it: [assets/translation_template.md](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/technical-to-business-translator/assets/translation_template.md), [references/metaphor_bank.md](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/technical-to-business-translator/references/metaphor_bank.md), [references/stakeholder_personas.md](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/technical-to-business-translator/references/stakeholder_personas.md), [references/translation_pattern_library.md](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/technical-to-business-translator/references/translation_pattern_library.md), [scripts/jargon_detector.py](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/technical-to-business-translator/scripts/jargon_detector.py), [scripts/readability_scorer.py](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/technical-to-business-translator/scripts/readability_scorer.py).
>
> If the user asked to install this Skill, run `npx skilld install nimrodfisher/data-analytics-skills/technical-to-business-translator`. Install writes the Skill files into the project, so every session loads them.

# 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`)