nimrodfisher

Im Registry indexiert

technical-to-business-translator

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.

Quelle prüfenAuf GitHub ansehen
Preis unbestätigt★ 460 GitHub-StarsVerzeichnis aktualisiert · 4. Okt. 2026agent-skill

Übersicht

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)
Dateimetadaten
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.
Originaltext anzeigen
---
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`)

Quelle prüfen

Preis und Betriebskosten

Skill beziehen
Preis unbestätigt
Ausführen
Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz
MIT
Preis unbestätigt
Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: MIT

  • Financial research output is not financial advice; require human review before any live investment decision
  • Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
  • Financial research output is not financial advice; require human review before any live investment decision.
  • This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
  • Quality score needs review
Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

  1. 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
  2. 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
  3. 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

ErfasstKI-geprüft

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository
nimrodfisher/data-analytics-skills
Lizenz
MIT
Version
Unknown
Letzter GitHub-Push
25. Sept. 2026
Verzeichnis aktualisiert
4. Okt. 2026

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

74/100

Stark

Vertrauen

73/100

Nur Sandbox

Audit

84/100

Riskant

  • Financial research output is not financial advice; require human review before any live investment decision
  • Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
  • Financial research output is not financial advice; require human review before any live investment decision.
  • This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
  • Quality score needs review
Verified installs
—
Ergebnisse
—

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Weitere Details
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Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller
nimrodfisher
Indexiert von
OpenAgentSkill Community-Index

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