Master Data Visualization Techniques
Master Data Visualization Techniques
ChatGPTClaudeGemini⚠ Human review required📁 Needs project context
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Trigger Phrase
Use prompt: Master Data Visualization Techniques
Prompt
54 wordsYou are a data-visualisation instructor.
Input:
- Your role/context: {{TARGET_AUDIENCE}}
Outline 5 visualisation types for conveying complex info. For each: what it's best for, a do and a don't, and an example use.
Constraints:
- Match type to purpose; no misleading scales; no invented data.
- Structured list.
Output: 5 visualisation types with guidance.
Input:
- Your role/context: {{TARGET_AUDIENCE}}
Outline 5 visualisation types for conveying complex info. For each: what it's best for, a do and a don't, and an example use.
Constraints:
- Match type to purpose; no misleading scales; no invented data.
- Structured list.
Output: 5 visualisation types with guidance.
Before & After
❌ Without this prompt
Unstructured request with unclear constraints and inconsistent output.
✅ With this prompt
Reusable, testable prompt/skill with clear trigger, inputs, output format, guardrails, and pass criteria.
Install Instructions
Copy the prompt text. Paste into ChatGPT, Claude, Gemini, or any AI chat. Fill in bracketed placeholders with your details. Run and review output.
Test It
Test command:
Trigger with: 'Test the Master Data Visualization Techniques with this input: [provide a short real example]'. Confirm output is specific, structured, and useful.
Expected output:
1) Line - trends; don't truncate the axis. 2) Heatmap - density; don't overuse colour.
Pass criteria:
- Output is specific to the input provided — not generic. Output follows the stated format and length. No invented statistics, facts, prices, or dates. Placeholders are not left unfilled.
⚠️ Guardrails
- Do not invent statistics, prices, laws, medical claims, or financial advice. Do not leave placeholders unfilled in output. Flag when inputs are too vague to produce a quality result — ask for clarification.
📁 Context File Tip
Brand brief, ICP/persona, offer details, source notes, policy constraints, examples of good/bad output.