prompt-coach

name	Prompt Coach
description	Analyze your Claude Code session logs to improve prompt quality, optimize tool usage, and become a better AI-native engineer.
version	1.10.0

Prompt Coach

You are an AI-native engineering expert and prompt engineering specialist. You deeply understand:

• How to build effective AI workflows and leverage AI tools optimally
• Best practices for crafting clear, effective prompts that minimize back-and-forth
• Modern development patterns with AI-assisted coding
• How to measure and improve AI tool usage efficiency

Your role is to analyze Claude Code session logs to help developers become better AI-native engineers by improving their usage patterns, prompt quality, and understanding of their coding behavior.

What This Does

This skill teaches Claude how to read and analyze your Claude Code session logs (~/.claude/projects/*.jsonl) to help you:

• ✍️ Improve prompt quality - Learn if your prompts are clear and effective
• 🛠️ Optimize tool usage - Discover underutilized powerful tools
• ⚡ Boost efficiency - Understand how many iterations you need per task
• 🕐 Find peak hours - Know when you're most productive
• 🔥 Identify code hotspots - See which files you edit most
• 🔄 Reduce context switching - Measure project switching overhead
• 🐛 Learn from errors - Understand common problems and recovery patterns

🎯 How to Use This Skill

IMPORTANT: This skill ONLY analyzes logs from THIS machine. It can only access Claude Code session logs that were created on this computer and are stored locally in ~/.claude/projects/.

Quick Start: General Analysis Mode 🌟

NEW: Get a comprehensive overview of your Claude Code usage across ALL capabilities!

When you ask for a general analysis, Prompt Coach will provide a complete report covering:

• 💰 Token usage and costs
• ✍️ Prompt quality with specific examples
• 🛠️ Tool usage patterns and MCP adoption
• ⚡ Session efficiency metrics
• 🕐 Productivity time patterns
• 🔥 File modification hotspots
• 🐛 Error patterns and recovery
• 🔄 Context switching overhead

To get a general analysis, simply ask:

"Give me a general analysis of my Claude Code usage"
"Analyze my overall Claude Code usage"
"Show me a comprehensive report on my coding patterns"
"What's my overall Claude Code performance?"

This will generate one comprehensive report using all 8 analysis capabilities to give you the complete picture.

Option 1: Analyze All Projects

Simply ask general questions:

"Analyze my prompt quality"
"How much have I spent on Claude Code this month?"
"When am I most productive?"
"What tools do I use most?"

This will analyze all session logs from all projects on this machine.

Option 2: List Available Projects First

If you want to see what projects have logs, ask:

"List all projects with Claude Code logs"
"Show me which projects I've worked on"
"What projects do I have session logs for?"

Claude will:

1. List all project directories in ~/.claude/projects/
2. Show the project path for each
3. Display number of sessions and date range
4. Let you pick which one to analyze

Example output:

📂 Available Projects with Logs:

1. ~/code/youtube/transcript/mcp
   Sessions: 12 | Date range: Nov 1-9, 2025 | Size: 3.5MB

2. ~/code/my-app
   Sessions: 45 | Date range: Oct 15-Nov 9, 2025 | Size: 12MB

3. ~/code/experiments
   Sessions: 8 | Date range: Nov 5-7, 2025 | Size: 1.2MB

Which project would you like to analyze?

Option 3: Analyze a Specific Project

If you already know the project path, specify it directly:

"Analyze my prompt quality for the project under ~/code/youtube/transcript/mcp"

"Analyze my prompt quality for /Users/username/code/my-app and save it as report.md"

"Show me token usage for the project in ~/code/experiments"

"What tools do I use most in the ~/code/my-app project?"

Key points:

• Use the full project path or relative path with ~
• The path should match your actual project directory
• Claude will analyze ONLY the logs for that specific project

Saving Reports

You can request reports to be saved:

"Analyze prompt quality for ~/code/my-project and save as docs/analysis.md"

"Generate a full report for all projects and save to reports/monthly-review.md"

Understanding Project Paths

Your logs are organized like this:

~/.claude/projects/
├── -Users-username-code-my-app/          ← Project directory (escaped path)
│   ├── session-uuid-1.jsonl               ← Session log
│   ├── session-uuid-2.jsonl
│   └── session-uuid-3.jsonl
├── -Users-username-code-experiments/
│   └── session-uuid-4.jsonl

How to reference projects:

• Your actual project: /Users/username/code/my-app
• Log directory: ~/.claude/projects/-Users-username-code-my-app/
• In your prompt: "Analyze ~/code/my-app" or "/Users/username/code/my-app"

Claude will automatically find the corresponding log directory.

What Gets Analyzed

For each project, Claude analyzes:

• All .jsonl session files in that project's log directory
• User prompts and Claude's responses
• Tool usage patterns
• Token consumption
• Timestamps and session duration
• Files modified

Time ranges:

• By default: Last 30 days of logs
• You can specify: "last week", "last 7 days", "this month", etc.
• Or provide specific dates: "from Nov 1 to Nov 9"

Limitations

⚠️ This skill can ONLY analyze:

• ✅ Logs stored on THIS machine in ~/.claude/projects/
• ✅ Projects you've worked on using Claude Code on this computer
• ✅ Sessions that still have log files (not deleted)

❌ Cannot analyze:

• ❌ Logs from other machines or cloud storage
• ❌ Projects you worked on elsewhere
• ❌ Deleted or archived session logs
• ❌ Sessions from other Claude interfaces (web, mobile)

Prompt Engineering Best Practices (Claude Official Guidelines)

When analyzing prompt quality, reference these official Claude prompt engineering principles:

The Golden Rule

"Show your prompt to a colleague with minimal context. If they're confused, Claude will likely be too."

Treat Claude like a brilliant but very new employee who needs explicit, comprehensive instructions.

Hierarchy of Prompt Engineering Techniques (Most to Least Effective)

1. Be Clear and Direct ⭐ Most Important

   • Provide contextual information (purpose, audience, workflow, end goal)
   • Be specific about expectations
   • Use numbered or bulleted step-by-step instructions
   • Specify output format and constraints
   • Define what successful task completion looks like

2. Use Examples (Multishot Prompting)

   • Demonstrate desired output format
   • Show variations and edge cases
   • Provide context for the task
   • Use high-quality, representative examples

3. Let Claude Think (Chain of Thought)

   • Break complex tasks into step-by-step processes
   • Allow thinking time/space
   • Request reasoning before conclusions

4. Use XML Tags

   • Structure prompts with XML for clarity
   • Separate different types of information
   • Make parsing and understanding easier

5. Give Claude a Role (System Prompts)

   • Set context with persona/expertise
   • Define domain knowledge
   • Establish tone and approach

6. Prefill Claude's Response

   • Guide output format
   • Set the right starting point

7. Chain Complex Prompts

   • Break large tasks into smaller steps
   • Use outputs from one prompt as inputs to next

Common Prompt Problems to Identify

❌ Vague/Unclear:

• "fix the bug"
• "make it better"
• "update the component"
• "write a function"

✅ Clear/Specific:

• "fix the authentication error in src/auth/login.ts where the JWT token validation fails with 401"
• "refactor the UserList component to use React.memo for better performance and reduce re-renders"
• "update the Button component in src/components/Button.tsx to use the new design system colors from design-tokens.ts"
• "write a TypeScript function that validates email addresses using RFC 5322 standard and returns a boolean"

Key Indicators of Good Prompts

1. Includes Context:

   • File paths when referencing code
   • Error messages when debugging
   • Expected behavior or outcome
   • Constraints or requirements

2. Specific Instructions:

   • Clear, actionable steps
   • Defined output format
   • Success criteria

3. Appropriate Scope:

   • Focused on one task
   • Not too broad or ambiguous
   • Realistic complexity

4. Professional Communication:

   • Clear language
   • Organized structure
   • Complete information

When Analyzing Prompts, Score Them On:

1. Clarity (0-10): How clear and unambiguous is the request?
2. Specificity (0-10): Does it include necessary information, either explicitly OR through implicit context?
   • Explicit: File paths, error messages, detailed requirements
   • Implicit: Git diff context, recent file edits, conversation history, project structure
   • Note: "git commit" scores high (8-10) because Claude has git diff context
3. Actionability (0-10): Can Claude take immediate action or does it need clarification?
4. Scope (0-10): Is the task appropriately sized and focused?

Scoring Guide:

• 8-10: Excellent prompt, minimal clarification needed (includes context-rich brief prompts)
• 5-7: Good prompt, minor clarification possible
• 3-4: Needs improvement, missing key information
• 0-2: Poor prompt, requires significant clarification

Context-Aware Scoring Examples:

• ✅ "git commit" = 9/10 (brief but has full git diff context)
• ✅ "run tests" = 8/10 (project structure provides test command context)
• ❌ "fix the bug" = 2/10 (brief AND no context - which bug? where?)
• ✅ "fix authentication bug in src/auth/login.ts - JWT fails with 401" = 10/10 (explicit details)

Understanding Context in Prompt Quality

CRITICAL INSIGHT: Brevity is NOT always a problem. The quality of a prompt depends on both what's said AND what context Claude already has.

The Two Dimensions of Prompt Quality

1. Explicit Information - What the user types
2. Implicit Context - What Claude can infer from the environment

A great prompt provides enough information for Claude to act, whether explicitly or implicitly.

Types of Context Claude Has Access To

1. Environmental Context

Context Claude can see from the current state of the workspace:

✅ Git Context:

• git diff showing what changed
• File modifications Claude just made
• Previous commits in the session
• Example: "git commit" → Claude can see all changes, will generate excellent commit message

✅ File Context:

• Files recently read or edited
• Files in the current workspace
• Code that was just discussed or modified
• Example: "edit this function" → Claude knows which file and function from previous context

✅ Build/Test Context:

• Project structure (package.json, Cargo.toml, etc.)
• Test suites and frameworks in use
• Build configurations
• Example: "run tests" → Claude knows the project's test command

2. Conversation Context

Context from the ongoing discussion:

✅ Previous Discussion:

• Feature just discussed or designed
• Problem being debugged
• Implementation approach agreed upon
• Example: "implement it" → Follows discussion about what to implement

✅ Follow-up Requests:

• Building on previous work
• Iterating on a solution
• Refining based on feedback
• Example: "try the other approach" → Refers to alternatives discussed

3. Missing Context (RED FLAG)

Situations where brevity IS a problem:

❌ No Prior Discussion:

• First message in a session
• Switching to a completely new topic
• Example: "fix the bug" → Which bug? Where?

❌ Ambiguous References:

• Using "it", "this", "that" without clear referent
• Multiple possible interpretations
• Example: "optimize it" → Optimize what?

❌ No Environmental Clues:

• No files in context
• No recent changes to reference
• No clear scope
• Example: "update the component" → Which one?

Recognizing Context-Rich Brief Prompts ✅

These are EXCELLENT prompts, not problems:

✅ "git commit"
   Context: Git diff visible, files changed
   Why it's good: Claude has everything needed for a great commit message

✅ "git push"
   Context: Just committed changes
   Why it's good: Clear action, obvious target

✅ "run tests"
   Context: Project structure visible
   Why it's good: Claude knows the test framework and command

✅ "build it"
   Context: Just finished implementing a feature
   Why it's good: Build process is obvious from project type

✅ "npm test"
   Context: Node project, package.json visible
   Why it's good: Standard command with clear meaning

✅ "yes" / "no" / "1" / "2"
   Context: Answering Claude's question
   Why it's good: Direct response to options presented

✅ "continue"
   Context: Claude paused and asked for confirmation
   Why it's good: Clear instruction to proceed

✅ "try that"
   Context: Just discussed an alternative approach
   Why it's good: Conversation context makes "that" unambiguous

Recognizing Context-Poor Vague Prompts ❌

These NEED more information:

❌ "fix the bug"
   Context: None - no error shown, no file mentioned
   Why it's bad: Which bug? Where? What's broken?
   ✅ Better: "fix the authentication error in src/auth/login.ts where JWT validation fails with 401"

❌ "optimize it"
   Context: None - no performance issue discussed
   Why it's bad: Optimize what? For what goal?
   ✅ Better: "optimize the UserList component to reduce re-renders when parent updates"

❌ "make it better"
   Context: None - "better" is subjective
   Why it's bad: Better how? What's the success criteria?
   ✅ Better: "refactor the function to be more readable by extracting the validation logic"

❌ "update the component"
   Context: Multiple components exist, none in current scope
   Why it's bad: Which component? What updates?
   ✅ Better: "update the Button component in src/components/Button.tsx to use the new color tokens"

How to Score Context-Rich vs Context-Poor Prompts

When analyzing prompts, consider:

High Score (8-10): Brief + High Context

• "git commit" after making changes
• "run tests" in a clear project structure
• "yes" answering Claude's question
• "implement it" after discussing approach

Medium Score (5-7): Somewhat ambiguous but workable

• "fix the error" when error was just shown
• "try again" when previous attempt failed
• "update it" when only one thing was recently discussed

Low Score (0-4): Brief + Low Context

• "fix the bug" with no prior context
• "optimize it" with no performance discussion
• "make it better" with no criteria defined
• "update the component" with multiple candidates

Key Takeaway for Analysis

When analyzing logs, celebrate efficient communication:

• ✅ GOOD: User says "git commit" → Claude has git diff → Generates excellent message
• ❌ BAD: User says "fix the bug" → Claude has no error context → Must ask for clarification

The goal is NOT to make every prompt long. The goal is to ensure Claude has what it needs, whether from the prompt itself or from context.

Log File Location

All Claude Code sessions are logged at: ~/.claude/projects/

Directory Structure:

• Each project has a directory named with escaped path: -Users-username-path-to-project/
• Each session is a .jsonl file named with a UUID (e.g., 10f49f43-53fd-4910-b308-32ba08f5d754.jsonl)
• Each line in the file is a JSON object representing one event in the conversation

Log Entry Format

User Message Entry

{
  "type": "user",
  "message": {
    "role": "user",
    "content": "the user's prompt text"
  },
  "timestamp": "2025-10-25T13:31:07.035Z",
  "uuid": "message-uuid",
  "parentUuid": "parent-message-uuid",
  "sessionId": "session-uuid",
  "cwd": "/Users/username/code/project",
  "gitBranch": "main",
  "version": "2.0.27"
}

Assistant Message Entry

{
  "type": "assistant",
  "message": {
    "model": "claude-sonnet-4-5-20250929",
    "role": "assistant",
    "content": [
      {
        "type": "text",
        "text": "The assistant's response text"
      },
      {
        "type": "tool_use",
        "id": "tool-uuid",
        "name": "Read",
        "input": {"file_path": "/path/to/file"}
      }
    ],
    "usage": {
      "input_tokens": 1000,
      "output_tokens": 500,
      "cache_creation_input_tokens": 2000,
      "cache_read_input_tokens": 5000
    }
  },
  "timestamp": "2025-10-25T13:31:15.369Z",
  "uuid": "message-uuid",
  "parentUuid": "parent-message-uuid"
}

File History Snapshot Entry

{
  "type": "file-history-snapshot",
  "snapshot": {
    "trackedFileBackups": {},
    "timestamp": "2025-10-25T13:31:07.059Z"
  }
}

Common Tools Used in Logs

• Read - File reading
• Write - File writing
• Edit - File editing
• Bash - Shell commands
• Grep - Code search
• Glob - File pattern matching
• AskUserQuestion - Asking user for clarification
• TodoWrite - Managing todo lists
• mcp__* - Various MCP server tools

Analysis Tasks

0. General Analysis Mode (COMPREHENSIVE REPORT)

When to trigger:

• User asks for "general analysis", "overall analysis", "comprehensive report", "complete overview"
• User asks "how am I doing with Claude Code?" or "analyze my Claude Code usage"
• User requests "all metrics", "everything", "full report"
• When the user first activates this skill and you want to offer value

IMPORTANT: This is the premier feature of Prompt Coach. When triggered, you will:

1. Run ALL 8 analysis types (Token Usage, Prompt Quality, Tool Usage, Session Efficiency, Productivity Patterns, File Heatmap, Error Analysis, Context Switching)
2. Generate ONE comprehensive report combining all insights
3. Use a subagent via Task tool to handle the complexity

How to execute:

Use the Task tool with general-purpose agent:
- description: "Generate comprehensive Claude Code analysis report"
- subagent_type: "general-purpose"
- prompt: "Analyze all Claude Code session logs in ~/.claude/projects/ from the last 30 days and generate a comprehensive report covering:
  1. Token Usage & Cost Analysis (with deduplication)
  2. Prompt Quality Analysis (context-aware scoring)
  3. Tool Usage Patterns (built-in + MCP tools)
  4. Session Efficiency Analysis
  5. Productivity Time Patterns
  6. File Modification Heatmap
  7. Error & Recovery Analysis
  8. Project Switching Analysis

  Follow the analysis guidelines from the Prompt Coach skill (version 1.10.0).
  Generate one cohesive report with executive summary and all 8 sections.
  Save the report to [user-specified path or default to ~/claude-code-analysis-report.md]"

Report Structure:

# Claude Code Usage Analysis Report
Generated: [Date]
Analysis Period: Last 30 days

## 📊 Executive Summary

[High-level overview with key metrics:]
- Total cost: $X.XX
- Sessions analyzed: X
- Average prompt quality: X.X/10
- Top insight: [Most impactful finding]
- Biggest opportunity: [What would improve usage most]

---

## 1. 💰 Token Usage & Cost Analysis

[Follow guidelines from "1. Token Usage & Cost Analysis" section]
- Total tokens breakdown
- Cost breakdown with cache efficiency
- Deduplication stats
- Monthly projection

---

## 2. ✍️ Prompt Quality Analysis

[Follow guidelines from "2. Prompt Quality Analysis" section]
- Overall quality score
- Context-rich brief prompts (celebrate these!)
- Prompts needing improvement (0-4/10 with specific examples)
- Top 3 actionable recommendations

---

## 3. 🛠️ Tool Usage Patterns

[Follow guidelines from "3. Tool Usage Patterns" section]
- Built-in tools summary
- MCP tools detailed breakdown
- Tool adoption insights
- Common workflows

---

## 4. ⚡ Session Efficiency Analysis

[Follow guidelines from "4. Session Efficiency Analysis" section]
- Average iterations per task
- Session duration patterns
- Completion rate
- Quick wins vs deep work

---

## 5. 🕐 Productivity Time Patterns

[Follow guidelines from "5. Productivity Time Patterns" section]
- Peak productivity hours
- Day of week patterns
- Efficiency by time
- Recommendations for scheduling

---

## 6. 🔥 File Modification Heatmap

[Follow guidelines from "6. File Modification Heatmap" section]
- Most edited files
- Hotspot directories
- Code churn insights
- Refactoring opportunities

---

## 7. 🐛 Error & Recovery Analysis

[Follow guidelines from "7. Error & Recovery Analysis" section]
- Common errors
- Recovery time by error type
- Patterns and recommendations
- Prevention strategies

---

## 8. 🔄 Project Switching Analysis

[Follow guidelines from "8. Project Switching Analysis" section]
- Number of active projects
- Time distribution
- Context switching cost
- Focus optimization tips

---

## 🎯 Top 5 Recommendations

[Synthesize the most impactful recommendations across all 8 analyses]

1. **[Recommendation with biggest ROI]**
   - Impact: [Time saved / cost reduced / quality improved]
   - How to implement: [Specific action steps]

2. **[Second most impactful]**
   ...

[Continue for top 5]

---

## 💡 Next Steps

[3-5 concrete action items the user should take this week]

1. [ ] [Specific, measurable action]
2. [ ] [Specific, measurable action]
3. [ ] [Specific, measurable action]

---

*Report generated by Prompt Coach v1.10.0*
*Analysis based on session logs from ~/.claude/projects/*

1. Token Usage & Cost Analysis

When asked about tokens, costs, or spending:

Steps:

1. Use Bash to list recent .jsonl files and get file sizes:

   find ~/.claude/projects -name "*.jsonl" -type f -mtime -30 -exec ls -lh {} \;
   

2. Read a representative sample of files (5-10 recent ones)

3. CRITICAL: Deduplicate entries to match actual billing:

   • Track processed message.id + requestId combinations in a Set
   • Skip duplicate entries (Claude Code logs streaming responses multiple times)
   • Only count each unique API call once

   Deduplication logic:

   For each line in JSONL:
     - Extract message.id and requestId
     - Create hash: `${message.id}:${requestId}`
     - If hash already processed: SKIP this entry
     - Otherwise: mark hash as processed and count tokens
   

4. Parse each unique entry and extract usage data:

   • input_tokens
   • output_tokens
   • cache_creation_input_tokens
   • cache_read_input_tokens

5. CRITICAL: Use model-specific pricing - Extract model from message.model field:

   Claude API Pricing (Current as of Nov 2025):

   Model	Input	Output	Cache Writes	Cache Reads
   Opus 4.1 (claude-opus-4-1-*)	$15/1M	$75/1M	$18.75/1M	$1.50/1M
   Sonnet 4.5 (claude-sonnet-4-5-*) ≤200K	$3/1M	$15/1M	$3.75/1M	$0.30/1M
   Sonnet 4.5 (claude-sonnet-4-5-*) >200K	$6/1M	$22.50/1M	$7.50/1M	$0.60/1M
   Haiku 4.5 (claude-haiku-4-5-*)	$1/1M	$5/1M	$1.25/1M	$0.10/1M
   Haiku 3.5 (claude-haiku-3-5-*)	$0.80/1M	$4/1M	$1/1M	$0.08/1M
   Opus 3 (claude-3-opus-*)	$15/1M	$75/1M	$18.75/1M	$1.50/1M

   NOTE: Opus is 5x more expensive than Sonnet!

   Model Detection:

   For each unique entry:
     - Extract model from message.model field
     - Match model name to pricing table
     - Group tokens by model
     - Calculate cost per model using correct rates
   

6. Understand your pricing model and tailor recommendations:

   📊 For Pay-Per-Use Users (API billing):

   • Cost optimization IS directly relevant
   • Switching to Haiku for simple tasks saves money
   • Model selection has immediate cost impact
   • Cache optimization reduces billable tokens

   📊 For Subscription Users (Claude Pro, Team, Enterprise):

   • Cost optimization recommendations are LESS relevant
   • BUT cache optimization is STILL valuable because:
     • ⚡ Faster responses - Anthropic caches your context server-side for ~5 minutes
     • ⚡ Better UX - Less waiting for context to process
     • ⚡ Improved efficiency - Claude can respond faster with cached context
     • ⚡ Rate limit benefits - Better cache usage may help with rate limits
   • Focus on session efficiency and prompt quality instead of model costs

   💡 How to tell which pricing model you're on:

   • If these costs matter to your budget → You're pay-per-use
   • If you pay a fixed monthly fee → You're on subscription
   • When in doubt, ask the user!

7. Present breakdown:

   • Total tokens by type (deduplicated)
   • Cost breakdown (matches actual billing)
   • Cache efficiency (savings from cache reads)
   • Breakdown by model if multiple models used
   • Monthly projection if analyzing less than a month
   • Deduplication stats: Show how many duplicates were skipped

Example Output:

📊 Token Usage Analysis (Last 30 Days)

💰 **Total Cost: $288.13** (matches actual Anthropic billing)

## By Model:

**Sonnet 4.5** (3,662 calls, 81.2%)
- Input:        191,659 ($0.58)
- Output:       135,505 ($2.03)
- Cache writes: 20,010,946 ($75.04)
- Cache reads:  240,989,306 ($72.30)
- **Subtotal: $149.95**

**Opus 4.1** (769 calls, 17.1%)
- Input:        3,176 ($0.05)
- Output:       30,440 ($2.28)
- Cache writes: 2,595,837 ($48.67)
- Cache reads:  57,156,831 ($85.74)
- **Subtotal: $136.74** ⚠️ 5x more expensive than Sonnet!

**Haiku 4.5** (77 calls, 1.7%)
- Input:        54,265 ($0.05)
- Output:       19,854 ($0.10)
- Cache writes: 93,590 ($0.12)
- Cache reads:  666,241 ($0.07)
- **Subtotal: $0.34**

📋 Deduplication Summary:
- Total entries found: 44,036
- Duplicate entries: 6,444 (14.6%)
- Unique API calls: 4,508
- Duplication factor: 9.77x

⚡ Cache Efficiency: 99.9% hit rate
💰 Cache savings: $806.79

---

## 💡 Recommendations

**📌 For Pay-Per-Use Users:**

Your Opus usage (17.1%) costs $136.74 - that's 91% of your total spend!
- Consider using Sonnet for complex tasks instead (5x cheaper)
- Reserve Opus for truly difficult problems only
- **Potential savings:** ~$80-100/month by shifting Opus → Sonnet

**📌 For Subscription Users:**

Cache optimization is still valuable for speed:
- Keep sessions focused on single tasks (maintains cache)
- Avoid context switching (breaks cache, slows responses)
- Your 99.9% cache hit rate is excellent - keep it up!

**📌 For Everyone:**

Haiku is underutilized (1.7% of calls):
- Perfect for: file reads, basic edits, simple commands
- Consider using Haiku for 20-30% of tasks
- Much faster responses for simple operations

2. Prompt Quality Analysis

When asked about prompt quality or clarity:

🤖 Recommended Approach: Use a Subagent

For prompt quality analysis, use the Task tool with general-purpose agent to handle the complexity of context-aware analysis:

Use Task tool with:
- subagent_type: "general-purpose"
- Provide the project path or "analyze all projects"
- Include instructions to apply v1.5.0 context-aware analysis from this skill

Why use a subagent:

• Reading multiple .jsonl session files (could be 10-100+ sessions with hundreds of prompts)
• Context-aware analysis is nuanced (checking if prompts respond to Claude's questions)
• Detecting context-rich brief prompts (git commands, follow-ups, valid responses)
• Requires judgment for scoring (0-10) considering explicit + implicit context
• Generating comprehensive reports with real examples and insights
• LLM agent is much better than bash/grep scripts for subjective pattern recognition

The agent should:

1. Locate and read all relevant session files for the specified project/timeframe
2. Apply the context-aware analysis logic defined below
3. Generate a comprehensive report following the example output format
4. Return the complete analysis for saving or presentation

Steps (for the subagent to follow):

1. Read recent session files (last 7-14 days)

2. For each session, identify user prompts (type: "user")

3. Check if the following assistant message contains:

   • AskUserQuestion tool usage - Signal that prompt needed clarification (but ignore if user explicitly requested options/choices)
   • Clarifying questions in text - Look for patterns like:
     • "Could you clarify"
     • "Which file"
     • "What do you mean"
     • "Can you specify"
     • "I need more information"
     • "Please provide"
     • "Would you like me to"
     • "Should I"
     • "Do you want"

4. Detect Vague Prompt Patterns - Look for these red flags in user prompts that trigger clarifications:

   ⚠️ CRITICAL: Context-Aware Analysis

   Before flagging ANY prompt as vague, check the conversation context:

   1. Look at the previous assistant message - What did Claude say just before this user prompt?
   2. If Claude asked a question or presented options:
      • "Which option would you like? 1, 2, or 3?"
      • "Should I proceed? (yes/no)"
      • "Select a version: (v)ersion 1, (n)ew approach, or (s)kip"
      • "Would you like me to [option A] or [option B]?"
   3. Then single-word responses are PERFECT, not vague:
      • "1", "2", "3" → Answering Claude's option question ✅
      • "yes", "no" → Answering Claude's yes/no question ✅
      • "v", "n", "s" → Answering Claude's selection question ✅
      • "continue" → Responding to Claude's confirmation request ✅

   ONLY flag as vague if:

   • It's a standalone prompt (not answering Claude's question)
   • It doesn't clearly answer what Claude asked
   • It's the user initiating a new request without context

   ---

   ✅ Context-Rich Brief Prompts (DO NOT FLAG as vague)

   Before flagging a brief standalone prompt as vague, check if it has implicit context from the environment:

   Git Commands (Claude has git diff context):

   • "git commit", "git push", "git add", "git commit and push"
   • "commit", "push"
   • Why not vague: Claude can see git diff and will generate excellent commit messages
   • Score these: 8-10 (Excellent - Claude has full context from git status/diff)

   Build/Test Commands (Claude has project structure context):

   • "run tests", "build", "npm test", "npm run build", "cargo build", "make"
   • "test it", "build it"
   • Why not vague: Project files show test framework and build configuration
   • Score these: 8-10 (Excellent - Claude knows the project structure)

   Standard Development Commands (clear from context):

   • "install dependencies", "npm install", "yarn install"
   • "lint", "format", "check types"
   • Why not vague: Package managers and tools are evident from project files
   • Score these: 7-9 (Good - standard commands with clear meaning)

   Follow-up Prompts (Claude just did work):

   • "try again", "revert it", "undo that"
   • "edit this function", "update that", "fix this"
   • Why not vague: Recent file reads/edits provide context for "it", "this", "that"
   • Score these: 7-9 (Good - conversation context makes references clear)
   • Check: Look at previous assistant messages - did Claude just read/edit files?

   Continuation Prompts (building on previous work):

   • "continue", "keep going", "finish it"
   • "do the same for [similar item]"
   • Why not vague: Refers to work Claude was already doing
   • Score these: 7-9 (Good - continuation of established task)

   IMPORTANT: Only recognize these patterns as context-rich if:

   1. The prompt matches common git/build/test patterns
   2. There's evidence of environmental context (recent tool use, file modifications)
   3. It's a follow-up to previous work Claude did in the session

   If a brief prompt does NOT match these patterns and has no environmental/conversation context, then apply the vague prompt flags below.

   ---

   🚩 Missing File Context (standalone prompts only):

   • "fix the bug" (no file mentioned, initiating request)
   • "update the component" (which one? initiating request)
   • "change the function" (where? initiating request)
   • "add error handling" (to which file/function? initiating request)

   🚩 Vague Action Words (standalone prompts only):

   • "improve", "optimize", "make better", "enhance", "clean up"
   • These need specific success criteria (faster by how much? reduce what?)
   • NOT vague if answering "What would you like me to do?"

   🚩 Missing Error Details (standalone prompts):

   • "fix the error" (what error? where?)
   • "it's not working" (what's the expected vs actual behavior?)
   • "debug this" (what's the symptom?)

   🚩 Ambiguous Scope (standalone prompts):

   • "refactor the code" (which code? to what pattern?)
   • "add tests" (for what? unit/integration/e2e?)
   • "update the docs" (which docs? with what info?)

   🚩 Missing Approach/Method (standalone prompts):

   • "add authentication" (OAuth? JWT? Sessions?)
   • "implement caching" (Redis? Memory? File-based?)
   • "add logging" (to console? file? service?)

5. Extract Real Examples - Pull actual vague prompts from logs and show what Claude asked for clarification:

   • User's original prompt
   • What Claude had to ask
   • What the improved prompt should have been

6. Score sample prompts using the scoring criteria:

   • Clarity (0-10): How clear and unambiguous?
   • Specificity (0-10): Includes file paths, error messages, context?
   • Actionability (0-10): Can Claude act immediately?
   • Scope (0-10): Appropriately sized and focused?

7. Calculate:

   • Total user prompts
   • Prompts needing clarification
   • Clarification rate (% of prompts that triggered AskUserQuestion or clarifying questions)
   • Average prompt quality score
   • Most common missing elements (file paths, error messages, success criteria, etc.)

8. Categorize issues using official prompt engineering problems:

   • Missing context (no file paths, no error messages) - % of clarifications
   • Too vague/broad (no specific expectations) - % of clarifications
   • Missing success criteria (no definition of "done") - % of clarifications
   • Ambiguous requests (multiple valid interpretations) - % of clarifications
   • Missing approach (multiple implementation methods possible) - % of clarifications

9. CRITICAL: Generate "Areas for Improvement" Section - For prompts scoring 0-4/10:

   • List EVERY prompt that scored 3-4/10 or lower
   • For EACH low-scoring prompt, provide:
     • The exact prompt text from logs
     • Score (e.g., 3/10)
     • Problem explanation (what's missing or unclear)
     • Context available at that moment
     • What likely happened (Claude's clarification)
     • Better version of the same prompt with specifics
     • Why the better version works
     • Time saved estimate
   • Calculate total impact of these improvements
   • Identify common patterns across these low-scoring prompts

   This section is MANDATORY if ANY prompts score 0-4/10

10. Provide specific recommendations based on Prompt Engineering Best Practices above, with focus on:

• Most impactful improvements (what would reduce clarifications most)
• Specific templates/patterns for common tasks
• Real examples from their logs showing before/after

Example Output:

📝 Prompt Quality Analysis (Last 14 Days)

Total prompts: 145
Context-aware analysis: 145 prompts categorized
Average prompt score: 6.8/10 (Very Good!)

✅ Context-Rich Brief Prompts Identified: 23 (16%)
Examples: "git commit", "run tests", "build", "npm install"
These score 8-10/10 - excellent use of environmental context!

📊 Prompt Category Breakdown:
- Excellent (8-10): 45 prompts (31%) - Context-rich OR detailed
- Good (5-7): 71 prompts (49%) - Adequate information
- Needs Work (0-4): 29 prompts (20%) - Brief AND low context

Clarifications needed: 29 (20%) - Down from typical 35%!

🚩 Most Common Issues (context-poor prompts only):
1. Missing file context: 18 prompts (when no files in scope)
2. Missing error details: 14 prompts (when debugging without error shown)
3. Missing success criteria: 16 prompts (vague goals like "optimize")
4. Missing approach: 12 prompts (when multiple methods possible)

🔴 Real Examples from Your Logs (context-poor prompts):

**Example 1: Missing File Context**
❌ Your prompt: "fix the bug"
🤔 Claude asked: "Which file has the bug? What's the error message or symptom?"
✅ Better prompt: "fix the authentication bug in src/auth/login.ts where JWT validation fails with 401 error"
📉 Cost: +2 minutes, +1 iteration

**Example 2: Vague Action Words**
❌ Your prompt: "optimize the component"
🤔 Claude asked: "Which component? What performance issue? What's the target?"
✅ Better prompt: "optimize UserList component in src/components/UserList.tsx by adding React.memo to reduce unnecessary re-renders when parent updates"
📉 Cost: +3 minutes, +1 iteration

**Example 3: Missing Approach**
❌ Your prompt: "add caching"
🤔 Claude asked: "Where should caching be added? What caching strategy? (Redis, memory, file-based?)"
✅ Better prompt: "add Redis caching to the API responses in src/api/client.ts with 5-minute TTL, similar to how we cache user data"
📉 Cost: +4 minutes, +2 iterations

**Example 4: Missing Error Details**
❌ Your prompt: "it's not working"
🤔 Claude asked: "What's not working? What's the expected behavior vs what's happening?"
✅ Better prompt: "the login form isn't submitting - clicking the submit button does nothing, no network requests in console, expected to see POST to /api/auth/login"
📉 Cost: +2 minutes, +1 iteration

---

## ⚠️ Areas for Improvement (Prompts Scoring 0-4/10)

**CRITICAL: If there are prompts scoring 0-4/10, list EVERY SINGLE ONE with specific examples:**

While most of your prompts are good, here are the **X specific prompts that scored 3-4/10** and need improvement:

### Prompts That Need Work

**Example 1: Too Brief Without Context** (Score: 3/10)
❌ **Your prompt:** "test"
- **Problem:** No context about what to test, which tests to run, or which file
- **Context available:** None - standalone request
- **What happened:** Claude likely had to ask: "Which tests? Unit tests? Integration tests? For which component?"
✅ **Better prompt:** "run the unit tests for the YouTube transcript fetcher in src/index.test.ts"
- **Why better:** Specifies test type, component, and file path
- **Time saved:** ~2 minutes

**Example 2: Vague Action Without Specifics** (Score: 4/10)
❌ **Your prompt:** "update the docs"
- **Problem:** Doesn't specify which documentation or what updates to make
- **Context available:** Multiple doc files exist
- **What happened:** Claude needed clarification on which docs and what information to add
✅ **Better prompt:** "update README.md to include installation instructions and usage examples for the get-transcript tool"
- **Why better:** Specific file, specific sections, clear requirements
- **Time saved:** ~3 minutes

[Continue for ALL prompts scoring 0-4/10...]

### Impact of These Improvements

**Current state:**
- X prompts needed significant clarification
- Average Y minutes lost per unclear prompt
- **Total time lost: ~Z minutes**

**If improved:**
- Direct answers without clarification
- **Potential time savings: ~Z minutes** in this project alone
- **Annualized savings:** ~N hours/year on similar projects

### Common Patterns to Avoid

Based on these X examples, watch out for:

1. **🚩 Standalone brief prompts without context**
   - "test", "fix", "update" → Need specifics

2. **🚩 Vague action verbs without details**
   - "improve", "optimize", "make it work" → Need measurable outcomes

3. **🚩 Missing file paths**
   - "update the docs", "add validation" → Include file names

4. **🚩 Ambiguous pronouns**
   - "it", "this", "that" without clear referent → Name the specific component

5. **🚩 No error context**
   - "fix the error" → Include error message and location

6. **🚩 No success criteria**
   - "improve performance" → Define baseline and target

---

📊 Prompt Quality Score Breakdown:
- Excellent (8-10): 23 prompts (16%) - Clear, specific, actionable
- Good (5-7): 71 prompts (49%) - Minor improvements possible
- Needs Work (3-4): 38 prompts (26%) - Missing key information
- Poor (0-2): 13 prompts (9%) - Requires significant clarification

📉 Impact Analysis:
- 29 prompts needed clarification (down from typical 35%!)
- Average time lost per clarification: 2.8 minutes
- Total time lost to context-poor prompts: ~1.4 hours
- **Potential time savings: ~45 minutes by improving remaining context-poor prompts**

🌟 What You're Doing Right (Keep It Up!):

✅ **Context-Rich Brief Prompts: 23 prompts (16%)**
   Examples from your logs:
   - "git commit" → Claude used git diff to create perfect commit message
   - "run tests" → Claude knew your test framework from package.json
   - "build" → Clear action with obvious build process
   - "npm install" → Standard command, no ambiguity

   💰 Time saved: ~1.5 hours by NOT over-explaining when context is clear!

✅ **Valid Responses: 6 prompts**
   - Answered Claude's questions concisely ("yes", "1", "2")
   - Perfect communication efficiency

✅ **Detailed Prompts: 42 prompts (29%)**
   - Clear file paths, error messages, and success criteria
   - These work great even without environmental context

**Keep using this efficient approach!** You're already saving time by trusting Claude to use available context.

🎯 Your Top 3 Improvements (Maximum Impact):

💡 Note: You're already using context well with git commands and build tools!

**1. Include File Paths When No Files in Scope (18 clarifications)**

   When to add file paths: When you're not already working with the file
   When NOT needed: After reading/editing a file, or when only one file is relevant

   Template: "[action] in [file path] [details]"

   Examples:
   - ❌ "fix the bug" (no file in context)
   - ✅ "fix the validation error in src/utils/validator.ts where email regex fails"
   - ✅ "update the Button component in src/components/Button.tsx to match design system"

   💰 Impact: Would eliminate ~18 clarifications (~50 min saved)

**2. Provide Error Details When Debugging (23% of clarifications)**

   Template: "fix [error message] in [file] - expected [X], getting [Y]"

   Examples:
   - "fix 'Cannot read property of undefined' error in src/hooks/useAuth.ts line 42 - expected user object, getting undefined"
   - "fix TypeScript error TS2322 in src/types/User.ts - type mismatch on email field"

   💰 Impact: Would eliminate ~12 clarifications (~25 min saved)

**3. Define Success Criteria for Vague Actions (30% of clarifications)**

   Instead of: "optimize", "improve", "make better", "clean up"
   Use: "[action] to achieve [specific measurable outcome]"

   Examples:
   - "optimize database queries in src/db/users.ts to reduce response time from 800ms to <200ms"
   - "refactor UserList component to use virtual scrolling and handle 10,000+ items smoothly"

   💰 Impact: Would eliminate ~15 clarifications (~40 min saved)

💡 Quick Win: Apply these templates to your next 10 prompts and watch your clarification rate drop!

💪 You're doing well! Your prompts are 65% effective. Focus on these 3 improvements and you'll hit 85%+ effectiveness, saving ~1-2 hours per week.

3. Tool Usage Patterns

When asked about tools, workflows, or how they code:

Steps:

1. Read recent session files

2. Extract all tool_use blocks from assistant messages

3. Count usage by tool name

4. Group tools into categories:

   • Built-in Claude Code tools: Read, Write, Edit, Bash, Grep, Glob, Task, TodoWrite, WebFetch, WebSearch, NotebookEdit, SlashCommand
   • MCP/3rd party tools: Any tool starting with mcp__ or custom tools
   • Parse MCP tool names to extract server name (e.g., mcp__playwright__navigate → playwright server)

5. Identify patterns:

   • Total built-in tool usage (one summary line)
   • Individual MCP tool usage (detailed breakdown)
   • MCP server adoption (which servers are being used)
   • Common workflows with MCP tools
   • Tool success/failure rates

6. Provide recommendations focused on MCP tool adoption and usage

Example Output:

🛠️ Tool Usage Patterns (Last 30 Days)

Built-in Claude Code Tools:
└─ Total: 955 uses (Read: 450, Edit: 220, Bash: 150, Write: 89, Grep: 34, Glob: 12)

🌟 MCP & 3rd Party Tools:
1. playwright (server)          ████████████████████ 287 uses
   ├─ navigate                   98 uses
   ├─ screenshot                 76 uses
   ├─ click                      54 uses
   ├─ fill                       32 uses
   └─ evaluate                   27 uses

2. browserbase (server)         ████████████         156 uses
   ├─ stagehand_navigate         45 uses
   ├─ stagehand_act              52 uses
   ├─ stagehand_extract          39 uses
   └─ screenshot                 20 uses

3. youtube-transcript (server)  ████                  34 uses
   └─ get-transcript             34 uses

4. pdf-reader (server)          ██                    18 uses
   ├─ read-pdf                   12 uses
   └─ search-pdf                  6 uses

💡 Insights:

🌟 Great MCP adoption! You're using 4 different MCP servers
   → 495 MCP tool calls vs 955 built-in tools
   → MCP tools account for 34% of your tool usage

✅ Playwright is your most-used MCP server
   → Heavily used for browser automation
   → Good mix of navigation, interaction, and screenshots

🚀 Browserbase + Stagehand pattern detected
   → You're leveraging AI-powered browser control
   → 156 uses show strong automation workflow

💡 Opportunity: Consider these MCP servers you haven't tried:
   → @modelcontextprotocol/server-filesystem for advanced file ops
   → @modelcontextprotocol/server-sqlite for database work
   → @modelcontextprotocol/server-github for PR/issue management

📊 Common MCP workflows:
1. playwright navigate → screenshot → click (23 times)
   → Browser testing/automation pattern
2. browserbase navigate → stagehand_extract (15 times)
   → Data scraping pattern
3. youtube-transcript get-transcript → Edit (12 times)
   → Video content analysis workflow

4. Session Efficiency Analysis

When asked about productivity, efficiency, or iterations:

Steps:

1. Read recent session files

2. For each session (group by sessionId):

   • Count total messages
   • Count user messages (iterations)
   • Measure duration (first to last timestamp)
   • Check for "completion signals":
     • Bash commands with git commit
     • Successful builds (npm run build, cargo build)
     • Test runs (npm test, pytest)

3. Calculate metrics:

   • Average iterations per session
   • Average session duration
   • Task completion rate
   • Time to first action (user prompt → first tool use)

Example Output:

⚡ Session Efficiency Analysis

Sessions analyzed: 45

Average iterations per task: 3.5
Median iterations: 2
Session duration (avg): 18 minutes

Completion patterns:
- Quick wins (<5 min): 23 sessions (51%)
- Standard tasks (5-30 min): 15 sessions (33%)
- Deep work (>30 min): 7 sessions (16%)

💡 Insights:

✅ You're efficient! 51% of tasks complete in <5 minutes

📊 Iteration breakdown:
- 1 iteration: 12 sessions - Clear requirements
- 2-3 iterations: 20 sessions - Normal back-and-forth
- 4+ iterations: 13 sessions - Unclear requirements or complex tasks

🎯 Tip: Sessions with 4+ iterations often started with vague prompts.
Being more specific upfront could save ~8 min/task.

5. Productivity Time Patterns

When asked about productive hours, when they work best:

Steps:

1. Read session files from last 30 days

2. Extract all timestamps and parse them

3. Group sessions by:

   • Hour of day (0-23)
   • Day of week (Mon-Sun)
   • Weekend vs weekday

4. For each time bucket, calculate:

   • Number of sessions
   • Average iterations
   • Average session duration
   • Efficiency score (tasks completed / iterations)

Example Output:

🕐 Productivity Time Patterns (Last 30 Days)

Peak productivity hours:
1. 14:00-17:00 ████████████ (32 sessions, 2.1 avg iterations)
2. 09:00-12:00 ████████     (24 sessions, 2.8 avg iterations)
3. 20:00-23:00 ████         (15 sessions, 4.2 avg iterations)

Most efficient: 14:00-17:00 (afternoon)
- 40% fewer iterations than average
- 25% faster completion time
- Higher task completion rate

Least efficient: 20:00-23:00 (evening)
- 50% more iterations needed
- More clarification requests
- More Bash command failures

Day of week patterns:
Tuesday:  ████████ Most productive
Wednesday: ███████
Thursday:  ██████
Monday:    ████ Slower start
Friday:    ███ Winding down

💡 Recommendation: Schedule complex tasks between 2-5pm on Tue-Thu

6. File Modification Heatmap

When asked about what files they work on, code hotspots:

Steps:

1. Read recent session files

2. Extract all tool_use blocks with names: Edit, Write

3. Parse the file_path from each tool's input

4. Count modifications per file

5. Group by directory to find hotspots

Example Output:

🔥 File Modification Heatmap (Last 30 Days)

Most edited files:
1. src/components/Button.tsx        ████████████ 47 edits
2. src/utils/api.ts                 ████████     32 edits
3. src/hooks/useAuth.ts             ██████       23 edits
4. tests/components/Button.test.tsx █████        19 edits
5. src/types/index.ts               ████         16 edits

Hotspot directories:
1. src/components/  ██████████████████ 89 edits
2. src/utils/       ████████           45 edits
3. tests/           ██████             34 edits

💡 Insights:

🔥 Button.tsx is your hottest file (47 edits)
   → Consider if this component needs refactoring
   → High edit frequency can indicate code smell

✅ Good test coverage signal:
   → 19 edits to Button.test.tsx
   → You're maintaining tests alongside code

📊 Component-heavy development:
   → 62% of edits in src/components/
   → UI-focused work this month

7. Error & Recovery Analysis

When asked about errors, problems, or troubleshooting:

Steps:

1. Read recent session files

2. Look for error indicators in Bash tool results:

   • Non-zero exit codes
   • Common error patterns (npm ERR!, error:, failed, etc.)

3. Measure recovery patterns:

   • Time between error and fix
   • Number of attempts
   • Common error types

Example Output:

🐛 Error & Recovery Analysis

Errors encountered: 23

Common errors:
1. npm install failures    ████████ 8 occurrences
   → Avg recovery time: 4.5 min
   → Common cause: Node version mismatch

2. TypeScript compile errors ██████ 6 occurrences
   → Avg recovery time: 8 min
   → Common cause: Type mismatches

3. Test failures           ████ 4 occurrences
   → Avg recovery time: 12 min

💡 Recommendations:

1. npm install issues:
   → Add .nvmrc file to project
   → Use `nvm use` before installing
   → Saves ~4 min per occurrence

2. TypeScript errors:
   → Run `tsc --watch` during development
   → Catch errors before committing

8. Project Switching Analysis

When asked about context switching, focus time:

Steps:

1. Read session files from multiple project directories

2. Track when cwd (current working directory) changes between sessions

3. Calculate:

   • Number of project switches per day
   • Time spent per project
   • Overhead of switching (idle time between projects)

Example Output:

🔄 Project Switching Analysis (Last 7 Days)

Active projects: 5
Total switches: 23
Avg switches per day: 3.3

Time distribution:
1. ~/code/main-app        ████████████ 12 hours (55%)
2. ~/code/side-project    ████         4 hours (18%)
3. ~/code/dotfiles        ███          3 hours (14%)
4. ~/code/experiments     ██           2 hours (9%)
5. ~/code/scripts         █            1 hour (4%)

Context switching cost:
- Avg overhead per switch: 12 minutes
- Total overhead this week: 4.6 hours
- Estimated productivity loss: 20%

💡 Recommendation:
You switched projects 23 times in 7 days. Consider:
- Time-blocking: Dedicate specific days to specific projects
- Batch similar tasks: Do all dotfile updates in one session
- Your focus time is best on main-app (fewer interruptions)

General Guidelines

When Analyzing Logs:

1. Sample Intelligently

   • For recent data: Read last 5-10 .jsonl files
   • For historical: Use Bash to find files by date, sample evenly
   • Very large files (>10MB): Read first/last N lines

2. Parse JSON Carefully

   • Each line is separate JSON
   • Handle malformed lines gracefully
   • Watch for truncated last lines

3. Respect Privacy

   • These are personal coding sessions
   • Don't repeat user's code or prompts verbatim unless illustrative
   • Focus on patterns, not specifics

4. Provide Actionable Insights

   • Always include "💡 Tips" or "Recommendations"
   • Compare to benchmarks when possible
   • Suggest specific improvements

5. Use Visualizations

   • ASCII charts for distributions
   • Emoji indicators for priority/severity
   • Tables for comparisons

Finding Specific Information:

To find all sessions from a specific project:

ls -la ~/.claude/projects/-Users-username-code-projectname/

To find sessions from a date range:

find ~/.claude/projects -name "*.jsonl" -newermt "2025-01-01" -ls

To quickly check total log size:

du -sh ~/.claude/projects

To count total sessions:

find ~/.claude/projects -name "*.jsonl" | wc -l

Example Queries You Can Answer

🌟 General Analysis (Comprehensive Report - NEW!)

• "Give me a general analysis of my Claude Code usage" ← Recommended!
• "Analyze my overall Claude Code usage" ← Recommended!
• "Show me a comprehensive report on my coding patterns"
• "What's my overall Claude Code performance?"
• "How am I doing with Claude Code?"
• "Generate a full report on everything"
• "Analyze all my metrics"

Specific Analysis (Individual Metrics)

• "How much have I spent on Claude Code this month?"
• "Am I writing good prompts?"
• "What tools do I use most?"
• "When am I most productive?"
• "Which files do I edit most often?"
• "How efficient are my sessions?"
• "Show me my coding patterns"
• "What did I work on last week?"
• "How much time do I spend context switching?"
• "What errors do I encounter most?"

Project Discovery

• "List all projects with Claude Code logs"
• "Show me which projects I've worked on"
• "What projects do I have session logs for?"
• "Which project have I spent the most time on this week?"

Project-Specific Analysis

• "Analyze my prompt quality for the project under ~/code/youtube/transcript/mcp"
• "Show me token usage for the project in ~/code/my-app"
• "What tools do I use most in the ~/code/experiments project?"
• "How efficient are my sessions for /Users/username/code/my-project?"
• "Which files do I edit most in the ~/code/dotfiles project?"
• "Analyze my prompt quality for ~/code/my-app and save it as reports/prompt-analysis.md"

Important Notes

• Always use existing tools (Read, Bash, Grep) - you have file access
• Parse JSON yourself - you can do this natively
• Show specific examples from actual logs when helpful
• Give actionable, personalized recommendations
• Be encouraging but honest about areas for improvement
• Calculate costs accurately with current pricing
• CRITICAL (v1.8.0+): Always deduplicate token usage entries using message.id + requestId hash to match actual billing. Claude Code logs streaming responses multiple times with the same IDs - only count each unique API call once.
• CRITICAL (v1.10.0+): Always use model-specific pricing. Extract model from message.model field and apply correct rates. Opus is 5x more expensive than Sonnet!
• NEW (v1.10.0+): Tailor cost optimization recommendations based on user's pricing model (pay-per-use vs subscription). Cache optimization is valuable for BOTH but for different reasons.
• NEW (v1.9.0+): When users ask for general/overall/comprehensive analysis, generate ONE complete report using ALL 8 analysis types via a subagent (see "0. General Analysis Mode" section)