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| # Prompt Style Analysis | |
| Analyze a user's prompting style to understand what makes their interactions with AI coding assistants effective. | |
| ## Analysis Scope | |
| $ARGUMENTS | |
| ## Process | |
| ### Step 1: Extract and Prepare Data | |
| 1. **Extract user prompts from conversation history** | |
| - Use `jq -r '.projects | to_entries[] | .value.history[]?.display // empty' ~/.claude.json > /tmp/user_prompts.txt` | |
| - If no arguments provided, analyze all prompts | |
| - If project path provided as argument, filter to that project only | |
| 2. **Handle large files** | |
| - If file >5MB, sample first 2000, middle 2000, and last 2000 prompts for balanced analysis | |
| - Truncate individual prompts >1000 chars to avoid skewing statistics | |
| ### Step 2: Quantitative Analysis | |
| Gather comprehensive metrics: | |
| 1. **Basic Statistics** | |
| - Total prompts analyzed | |
| - Average prompt length (chars) | |
| - Number of sessions (separated by empty prompts) | |
| - Prompts per session average | |
| 2. **Length Distribution** | |
| - Short (<50 chars): Quick commands/acknowledgments | |
| - Medium (50-200 chars): Standard requests | |
| - Long (200-500 chars): Detailed instructions | |
| - Very Long (>500 chars): Complex problems/pastes | |
| 3. **Communication Patterns** | |
| - Questions (ending with ?) | |
| - Exclamations (ending with !) | |
| - Commands (starting with action verbs) | |
| - Acknowledgments (ok, good, thanks, etc.) | |
| 4. **Technical Content** | |
| - Error/debug content (stack traces, error messages) | |
| - Test-related (pytest, jest, test files) | |
| - Code/paths (file paths, function names) | |
| - URLs/PRs (links, PR numbers) | |
| ### Step 3: Dimensional Analysis | |
| Score each dimension on a 1-10 scale: | |
| #### Core Dimensions | |
| 1. **Assertive ↔ Questioning** (1=Always asking, 10=Always commanding) | |
| - Analyze ratio of questions to statements | |
| - Look for: "can you", "could you", "what if" vs direct commands | |
| 2. **Formal ↔ Informal** (1=Very formal, 10=Very casual) | |
| - Check for casual language: "yo", "hey", slang, contractions | |
| - Count proper punctuation and grammar vs shortcuts | |
| 3. **Technical ↔ Abstract** (1=High-level, 10=Deep technical) | |
| - Count file paths, function names, error logs | |
| - Measure specificity of requests | |
| 4. **Enthusiastic ↔ Neutral** (1=Flat, 10=Very enthusiastic) | |
| - Count exclamation marks, positive words | |
| - Look for energy indicators: "let's go!", "awesome" | |
| 5. **Directive ↔ Collaborative** (1=Commands only, 10=Full collaboration) | |
| - Check for inclusive language: "let's", "we", "should we" | |
| - Ratio of imperatives to suggestions | |
| 6. **Patient ↔ Urgent** (1=Very patient, 10=Very urgent) | |
| - Look for urgency markers: "quick", "asap", "now" | |
| - Check follow-up frequency | |
| 7. **Verbose ↔ Concise** (1=Very brief, 10=Very detailed) | |
| - Based on length distribution | |
| - Amount of context provided | |
| 8. **Goal-Oriented ↔ Exploratory** (1=Exploration, 10=Clear goals) | |
| - Check for specific deliverables and file outputs | |
| - Look for exploratory language: "curious", "what if" | |
| 9. **Prompt Length Profile** (NEW) | |
| - Micro (<25 chars): Single word/phrase responses | |
| - Short (25-75 chars): Quick commands | |
| - Standard (75-250 chars): Normal requests | |
| - Extended (250-500 chars): Detailed instructions | |
| - Comprehensive (>500 chars): Full context/pastes | |
| ### Step 4: Pattern Recognition | |
| Identify key behavioral patterns: | |
| 1. **Starting Patterns** | |
| - Most common first words (reveals default approach) | |
| - Greeting style (formal/informal/none) | |
| 2. **Iteration Style** | |
| - Course corrections: "wait", "oh", "actually" | |
| - Error recovery patterns | |
| - Follow-up frequency | |
| 3. **Context Provision** | |
| - Pasted content frequency | |
| - Error log inclusion | |
| - File path specificity | |
| 4. **Politeness & Tone** | |
| - Please/thanks frequency | |
| - Apology patterns | |
| - Acknowledgment style | |
| ### Step 5: Generate Report | |
| ## Output Format | |
| Create a markdown report and save to `/tmp/prompt-style-report.md`: | |
| ```markdown | |
| # 🎯 Prompt Style Analysis Report | |
| ## Executive Summary | |
| **Total Prompts**: [number] | **Sessions**: [number] | **Avg Length**: [chars] | |
| ### Your Style Profile: "[Style Title]" | |
| *[2-sentence description of what makes this style effective]* | |
| ## 📊 Dimensional Scores (1-10) | |
| | Dimension | Score | Interpretation | | |
| |-----------|-------|----------------| | |
| | **Assertiveness** | [X]/10 | [Questioning ← → Commanding] | | |
| | **Formality** | [X]/10 | [Formal ← → Casual] | | |
| | **Technical Depth** | [X]/10 | [Abstract ← → Technical] | | |
| | **Energy Level** | [X]/10 | [Neutral ← → Enthusiastic] | | |
| | **Collaboration** | [X]/10 | [Directive ← → Collaborative] | | |
| | **Urgency** | [X]/10 | [Patient ← → Urgent] | | |
| | **Verbosity** | [X]/10 | [Concise ← → Detailed] | | |
| | **Goal Focus** | [X]/10 | [Exploratory ← → Goal-Oriented] | | |
| ## 📈 Prompt Length Distribution | |
| | Category | Count | Percentage | Typical Use | | |
| |----------|-------|------------|-------------| | |
| | Micro (<25) | [n] | [%] | Acknowledgments | | |
| | Short (25-75) | [n] | [%] | Quick commands | | |
| | Standard (75-250) | [n] | [%] | Normal requests | | |
| | Extended (250-500) | [n] | [%] | Detailed instructions | | |
| | Comprehensive (500+) | [n] | [%] | Context/pastes | | |
| ## 🎭 Communication Patterns | |
| ### Top Starting Words | |
| 1. [word] ([count] times) | |
| 2. [word] ([count] times) | |
| 3. [word] ([count] times) | |
| ### Interaction Style | |
| - Questions: [n] ([%]) | |
| - Commands: [n] ([%]) | |
| - Acknowledgments: [n] ([%]) | |
| - Course corrections: [n] ([%]) | |
| ### Technical Content | |
| - Error/debug pastes: [n] ([%]) | |
| - Test-related: [n] ([%]) | |
| - File paths/code: [n] ([%]) | |
| - URLs/PRs: [n] ([%]) | |
| ## 💪 Key Strengths | |
| 1. **[Strength]**: [Brief explanation] | |
| 2. **[Strength]**: [Brief explanation] | |
| 3. **[Strength]**: [Brief explanation] | |
| ## 🎯 Effectiveness Indicators | |
| - **Clarity Score**: [High/Medium/Low] - Based on specificity of requests | |
| - **Context Richness**: [High/Medium/Low] - Based on information provided | |
| - **Iteration Style**: [Smooth/Moderate/Frequent] - Based on corrections needed | |
| ## 🚀 Recommendations | |
| 1. **Keep doing**: [What's working well] | |
| 2. **Consider**: [Potential improvement] | |
| 3. **Try**: [New technique to enhance effectiveness] | |
| --- | |
| *Generated on [date] | Analyzed [n] prompts across [n] sessions* | |
| ``` | |
| ## Final Steps | |
| 1. Display summary in terminal showing: | |
| - Total prompts analyzed | |
| - Dominant style classification | |
| - Top 3 dimensional scores | |
| - One key insight | |
| 2. Inform user that full report saved to `/tmp/prompt-style-report.md` | |
| 3. Suggest sharing insights with team to improve collective AI interaction skills |
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