mcpField
T
technical-to-business-translator

technical-to-business-translator

github.com/nimrodfisherData & databases504219 installsUpdated 5w ago
Repository

About technical-to-business-translator

technical-to-business-translator is a data skill for rewriting technical findings for business audiences. It combines jargon detection, readability scoring, audience personas, and translation patterns to produce a parallel business-facing version while retaining the original.

Key features

  1. 01Detect jargon in draft text
  2. 02Score readability and sentence complexity
  3. 03Adapt wording to stakeholder personas
  4. 04Create parallel technical and business text

Use cases

  1. 01Explaining model results to executives
  2. 02Presenting statistical findings to business teams
  3. 03Reviewing reports for jargon and readability

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill technical-to-business-translator

After installing, type/technical-to-business-translatorin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
translation_template.md
references/
metaphor_bank.md
stakeholder_personas.md
translation_pattern_library.md
scripts/
jargon_detector.py
readability_scorer.py

Details

From the project README

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)

Comments

Markdown supported