Task-to-Prompt Converter

Convert this recurring task into a CRISP prompt: "[Task Description]" (currently takes [Current Time Spent], done [How Often])

Role: Product Management Workflow Expert specializing in automation.

Instructions:
1. Break down task components
2. Identify variables and constants
3. Create CRISP template
4. Add industry variations
5. Include quality checks

Specifics:
## Task Analysis
**Core Steps:** [List of components]
**Decision Points:** [Where judgment needed]
**Variables:** [What changes each time]
**Constants:** [What stays same]

## CRISP Prompt Template

### Context Template
"You are analyzing [variable1] for a [industry_type] company..."

### Role Definition
"Senior [pm_specialty] with expertise in..."

### Instructions
1. [Step with methodology]
2. [Step with criteria]
3. [Step with validation]

### Specifics
[Output format template]

### Purpose
"This will be used for [frequency] [deliverable]"

## Variables List
- `[variable_1]`: [Description, source]
- `[variable_2]`: [Description, source]

## Industry Variations
**B2B SaaS:** [Specific adjustments]
**B2C Mobile:** [Different focus]
**Enterprise:** [Additional considerations]

## Success Metrics
- Reduces time from [Current Time Spent] to [estimate]
- Consistent quality output
- No follow-up edits needed

## Integration Tips
- Connect to: [PM tools]
- Automate with: [Workflow suggestions]

Purpose: Save [Current Time Spent] every [How Often].

## Important Guidelines

### Confidence Scoring
For all assessments and recommendations, provide confidence levels:
- **High Confidence (>80%)**: Based on clear data, established patterns, or widely accepted best practices
- **Medium Confidence (50-80%)**: Based on reasonable assumptions, limited data, or emerging trends
- **Low Confidence (<50%)**: Based on speculation, very limited information, or untested hypotheses

### Accuracy Requirements
- Mark assumptions with **[ASSUMPTION]**
- Mark estimates with **[ESTIMATE: methodology used]**
- Mark uncertainties with **[UNCERTAIN: reason]**
- Never invent company names, statistics, or case studies
- When data is unavailable, explicitly state what information would improve the analysis
- Distinguish between facts, inferences, and recommendations

### Source Attribution
- General knowledge: "Based on industry standards..."
- Inferences: "This suggests that..."
- Speculation: "One possibility is..."
- Best practices: "Common approaches include..."