prompt-consultant

name	prompt-consultant
description	Extract all user prompts from Claude Code history and deliver a harsh critique of your prompting patterns based on best practices. Get roasted to get better.

Prompt Consultant

Extracts all user prompts from Claude Code conversation history, then delivers a brutally honest critique of your prompting patterns based on best practices.

What This Skill Does

1. Copies conversation transcripts from ~/.claude/projects/
2. Parses JSONL files to extract only user messages
3. Filters out:
   • Tool results
   • System reminders
   • Task notifications
   • Command outputs
   • Session continuation summaries
   • Slash command invocations
4. Merges all prompts into a single file
5. Deduplicates
6. Analyzes prompts against best practices and delivers harsh critique

Execution Steps

Step 1: Run the extraction script

Create and run this Python script:

import json
import re
from pathlib import Path

# Configuration
projects_dir = Path.home() / ".claude" / "projects"
output_file = Path.cwd() / "all-prompts.txt"

skip_prefixes = [
    "<command-name>",
    "<local-command",
    "<task-notification>",
    "<system-reminder>",
    "This session is being continued from a previous conversation",
    "Caveat: The messages below were generated",
    "Unknown skill:",
    "Unknown slash command:",
    "<command-message>",
    "/ralph-wiggum:",
    "/ralph-loop:",
]

clean_patterns = [
    r'<system-reminder>.*?</system-reminder>',
    r'<task-notification>.*?</task-notification>',
    r'Read the output file to retrieve the result:.*?\n',
    r'<local-command-caveat>.*?</local-command-caveat>',
    r'<local-command-stdout>.*?</local-command-stdout>',
]

def is_real_prompt(content):
    """Filter out tool results, commands, and system messages"""
    if not content or not isinstance(content, str):
        return False

    content = content.strip()
    if not content:
        return False

    # Skip tool results
    if content.startswith("[{") and ("tool_use_id" in content or "tool_result" in content):
        return False
    if content.startswith("[{'tool_use_id'"):
        return False

    # Skip system prefixes
    for prefix in skip_prefixes:
        if content.startswith(prefix):
            return False

    # Skip short system content
    if "<local-command-stdout>" in content and len(content) < 500:
        return False
    if "<local-command-caveat>" in content and len(content) < 500:
        return False

    return True

def clean_prompt(content):
    """Remove system tags but keep actual user text"""
    for pattern in clean_patterns:
        content = re.sub(pattern, '', content, flags=re.DOTALL)
    return content.strip()

# Collect all prompts by session
sessions = []
prompt_count = 0
skipped_count = 0

for project_dir in projects_dir.iterdir():
    if not project_dir.is_dir():
        continue

    for jsonl_file in project_dir.glob("*.jsonl"):
        session_prompts = []
        try:
            with open(jsonl_file, 'r') as f:
                for line in f:
                    try:
                        entry = json.loads(line.strip())
                        if entry.get("type") == "user" and "message" in entry:
                            content = entry["message"].get("content", "")
                            if is_real_prompt(content):
                                cleaned = clean_prompt(content)
                                if cleaned and len(cleaned) > 1:
                                    session_prompts.append(cleaned)
                                    prompt_count += 1
                            else:
                                skipped_count += 1
                    except json.JSONDecodeError:
                        continue
        except Exception:
            continue
        if session_prompts:
            sessions.append(session_prompts)

# Deduplicate while preserving order
seen = set()
unique_sessions = []
for session in sessions:
    unique_session = []
    for prompt in session:
        if prompt not in seen:
            seen.add(prompt)
            unique_session.append(prompt)
    if unique_session:
        unique_sessions.append(unique_session)

# Write output (token-efficient format with session markers)
with open(output_file, 'w') as f:
    session_strs = ["\n---\n".join(s) for s in unique_sessions]
    f.write("\n=== new session ===\n".join(session_strs))

print(f"Extracted {len(unique_prompts)} unique prompts to {output_file}")
print(f"(Total: {prompt_count}, Duplicates removed: {prompt_count - len(unique_prompts)}, Skipped: {skipped_count})")

Step 2: Read the best practices

Read @best_practices.md to understand what good prompting looks like.

Step 3: Read and analyze the extracted prompts

Read the generated all-prompts.txt file (sample ~50-100 prompts if there are many).

Step 4: Deliver harsh critique

Analyze the user's prompting patterns against the best practices and deliver a brutally honest critique. Be harsh but constructive. The goal is to help them improve, not to be nice.

Critique Framework

When analyzing prompts, evaluate against these criteria from best_practices.md:

1. Verification Criteria

• Did the user provide ways for Claude to verify its work?
• Did they include test cases, expected outputs, or success criteria?
• Bad: "implement email validation"
• Good: "implement email validation. test cases: [e-mail]=true, invalid=false"

2. Specificity

• Are prompts vague or specific?
• Do they reference files, constraints, examples?
• Bad: "fix the bug"
• Good: "users report login fails after session timeout. check src/auth/, especially token refresh"

3. Context Provision

• Did they use @ references to files?
• Did they paste errors, screenshots, or relevant data?
• Or did they make Claude guess?

4. Planning vs. Jumping In

• Did they explore/plan before implementing?
• Or did they let Claude jump straight to coding?

5. Course Correction

• Did they correct early when things went wrong?
• Or did they let Claude spiral with repeated failed attempts?

6. Anti-patterns to Call Out

• Kitchen sink sessions: Mixing unrelated tasks without /clear
• Over-correction loops: Same mistake corrected 3+ times
• Vague delegation: "make it better", "fix this", "do the thing"
• Missing verification: No tests, no expected outputs, no way to check
• Infinite exploration: "investigate X" with no scope

Critique Output Format

Structure your critique as:

# Prompt Audit Report

## Overall Grade: [A/B/C/D/F]

## Executive Summary
[2-3 sentences on the biggest issues]

## What You're Doing Wrong

### 1. [Issue Name]
**Severity**: High/Medium/Low
**Frequency**: X% of prompts
**Examples**:
- "[quote from their prompt]" - [why this is bad]
- "[another example]" - [why this is bad]
**How to fix**: [specific actionable advice]

### 2. [Next Issue]
...

## What You're Doing Right
[Be brief - focus on problems]

## Top 5 Prompts That Made Me Cringe
1. "[prompt]" - [why it's terrible]
2. ...

## Specific Rewrites
Take 3-5 of their worst prompts and show the improved version:

| Original | Improved |
|----------|----------|
| "fix the bug" | "The login fails with error X. Check src/auth/. Write a failing test, then fix it." |

## Action Items
1. [Most important thing to change]
2. [Second most important]
3. [Third]

Tone Guidelines

• Be direct and blunt. No sugar-coating.
• Use phrases like: "This is lazy.", "You're making Claude guess.", "This wastes context.", "You have no way to verify this worked."
• Point out patterns, not just individual mistakes
• Quantify issues when possible ("40% of your prompts lack verification")
• The goal is improvement through honest feedback, not validation

Optional Arguments

If the user specifies:

• --output <path> - Change output file location
• --project <name> - Only extract from a specific project
• --raw - Don't deduplicate, keep all prompts
• --no-critique - Skip the critique, just extract