Registry indexed
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 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.
scripts/jargon_detector.py on the draft text to flag technical terms that need translation.scripts/readability_scorer.py to get Flesch-Kincaid grade level and sentence complexity metrics; target ≤ grade 10 for executive audiences.references/stakeholder_personas.md to select the persona that best matches your reader; each persona has vocabulary preferences and typical questions.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").references/metaphor_bank.md.assets/translation_template.md to produce the parallel technical/business version; keep the original in an appendix for technical reviewers.translation_template.md)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.
--- 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 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`)
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Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
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Quality
74/100
Strong
Trust
73/100
Sandbox only
Audit
84/100
Risky
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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