Execute qualquer Skill no Manus
com um clique

Execute qualquer Skill no Manus com um clique

data-interpreter

Use when the user wants plain-English interpretation of a dataset including patterns, surprises, and misleading aspects. Triggers include "analyze this data", "interpret these numbers", "what does this data mean".

Executar no Manus

Visão geral

Comando de instalação

npx skills add https://github.com/codejunkie99/prompt-skills --skill data-interpreter

Copie e cole este comando no Claude Code para instalar a skill

Fonte

codejunkie99/prompt-skills

Estrelas15

Forks1

Atualizado26 de abril de 2026 às 10:50

SKILL.md

readonly

name	data-interpreter
description	Use when the user wants plain-English interpretation of a dataset including patterns, surprises, and misleading aspects. Triggers include "analyze this data", "interpret these numbers", "what does this data mean".

Data Interpreter

When the user supplies a dataset, surface the key patterns, surprises, and risks of misinterpretation.

Variables

[PASTE DATA]
[WHO WILL READ THIS ANALYSIS AND THEIR TECHNICAL LEVEL]

Prompt Template

Analyze this data:

[PASTE DATA — CSV, table, numbers, survey results, etc.]

Provide:

Summary statistics (key numbers at a glance)
The 3 most important patterns or trends
2 surprising findings that are not immediately obvious
1 potentially misleading aspect of this data (what could be misinterpreted?)
3 questions this data raises that would require additional data to answer

Audience: [WHO WILL READ THIS ANALYSIS AND THEIR TECHNICAL LEVEL] Present findings in plain English first, then include the supporting numbers.

Mais deste repositório

mesmo repositório

apology-crafter

codejunkie99/prompt-skills

Use when the user wants to craft a sincere, accountable apology in both written and spoken versions, without deflection. Triggers include "draft an apology", "how should I apologize", "say sorry to".

2026-04-2615

difficult-conversation-prep

codejunkie99/prompt-skills

Use when the user wants to prepare for a hard conversation with empathetic-but-direct opening, defensiveness handling, and resolution paths. Triggers include "difficult conversation", "hard talk", "tough conversation prep".

2026-04-2615

elevator-pitch-builder

codejunkie99/prompt-skills

Use when the user wants three energy-level variants of an elevator pitch (bold, conversational, data-driven). Triggers include "elevator pitch", "pitch this product", "30 second pitch".

2026-04-2615

feedback-giver

codejunkie99/prompt-skills

Use when the user wants to draft spoken feedback using observation/impact/expectation/support framework. Triggers include "give feedback", "feedback to my report", "performance feedback".

2026-04-2615

presentation-outliner

codejunkie99/prompt-skills

Use when the user wants a tight presentation outline with hook, problem, solution, evidence, objection handling, and clear CTA. Triggers include "outline a presentation", "talk outline", "structure my pitch deck".

2026-04-2615

research-synthesizer

codejunkie99/prompt-skills

Use when the user has multiple research sources on a topic and wants genuine synthesis of themes, contradictions, and gaps — not separate summaries. Triggers include "synthesize this research", "compare these sources", "research synthesis".

2026-04-2615

Fonte

codejunkie99

codejunkie99/prompt-skills

Abrir repositório GitHub Ver repositórios do creator

Comando de instalação

Download

Executar no Manus

Útil paraSOC

Cientistas de dadosInformática e Matemática15-2051L4

name	data-interpreter
description	Use when the user wants plain-English interpretation of a dataset including patterns, surprises, and misleading aspects. Triggers include "analyze this data", "interpret these numbers", "what does this data mean".

Data Interpreter

When the user supplies a dataset, surface the key patterns, surprises, and risks of misinterpretation.

Variables

[PASTE DATA]
[WHO WILL READ THIS ANALYSIS AND THEIR TECHNICAL LEVEL]

Prompt Template

Analyze this data:

[PASTE DATA — CSV, table, numbers, survey results, etc.]

Provide:

Summary statistics (key numbers at a glance)
The 3 most important patterns or trends
2 surprising findings that are not immediately obvious
1 potentially misleading aspect of this data (what could be misinterpreted?)
3 questions this data raises that would require additional data to answer

Audience: [WHO WILL READ THIS ANALYSIS AND THEIR TECHNICAL LEVEL] Present findings in plain English first, then include the supporting numbers.