# Mbticlaude > Infer MBTI personality types by analyzing user prompts across multiple AI tools (Claude Code, Codex, Gemini, OpenCode, OpenClaw) | 通过分析用户在多个 AI 工具的提示词来推测 MBTI 性格类型 - Skill: `xmanrui/mbticlaude` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add xmanrui/mbticlaude` - Raw SKILL.md: https://api.skillmd.com/api/skills/xmanrui/mbticlaude/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: xmanrui (https://skillmd.com/u/xmanrui) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/xmanrui/mbticlaude --- # MBTIClaude Infer MBTI personality types by analyzing user prompts across multiple AI tools (Claude Code, Codex, Gemini, OpenCode, OpenClaw). 通过分析用户在多个 AI 工具（Claude Code、Codex、Gemini、OpenCode、OpenClaw）中的提示词来推测 MBTI 性格类型。 ## Features | 功能 - Automatically extract user prompt history from five AI tools | 自动提取五种 AI 工具的用户提示词历史 - Analyze communication style, information processing, decision-making patterns, and lifestyle | 分析沟通风格、信息处理方式、决策模式和生活方式 - Infer MBTI type based on behavioral patterns | 基于行为模式推测 MBTI 类型 - Provide detailed feature analysis and recommendations | 提供详细的特征分析和建议 ## Usage | 使用方法 ```bash /mbticlaude ``` ## Analysis Dimensions | 分析维度 ### 1. Communication Style (E vs I) | 沟通风格 - Prompt length and detail level | 提示词长度和详细程度 - Social expression vs task-oriented | 社交性表达 vs 任务导向 - Richness of background information | 背景说明的丰富程度 ### 2. Information Processing (S vs N) | 信息处理 - Focus on concrete details vs abstract concepts | 关注具体细节 vs 抽象概念 - Implementation-oriented vs concept-oriented | 实现导向 vs 概念导向 - Expression of creative thinking | 创造性思维的体现 ### 3. Decision Making (T vs F) | 决策方式 - Logical vs emotional | 逻辑性 vs 情感性 - Task-oriented vs interpersonal considerations | 任务导向 vs 人际关系考虑 - Technical language vs emotional expression | 技术语言 vs 情感表达 ### 4. Lifestyle (J vs P) | 生活方式 - Planning vs flexibility | 计划性 vs 灵活性 - Structured vs exploratory | 结构化 vs 探索性 - Rapid iteration vs systematic planning | 快速迭代 vs 系统规划 ## Data Sources | 数据来源 - **Claude Code**: `~/.claude/history.jsonl` - **Codex**: `~/.codex/history.jsonl` - **Gemini**: `~/.gemini/tmp/*/chats/*.json` - **OpenCode**: `~/.local/state/opencode/prompt-history.jsonl` - **OpenClaw**: `~/.openclaw/agents/*/sessions/*.jsonl` ## Important Notes | 注意事项 ⚠️ **Important Reminders | 重要提醒：** 1. Sample based on work scenarios, may not reflect true personality | 样本基于工作场景，可能不反映真实性格 2. MBTI itself has scientific controversies, for reference only | MBTI 本身科学性存疑，仅供参考 3. This is behavioral pattern inference, not professional psychological assessment | 这是基于行为模式的推测，非专业心理测评 4. Requires sufficient historical data for accurate conclusions | 需要足够的历史数据才能得出较准确的结论 ## Implementation | 实现 ```python #!/usr/bin/env python3 import json import os from pathlib import Path from collections import Counter def extract_claude_code_prompts(limit=100): """提取 Claude Code 用户提示词""" history_file = Path.home() / ".claude" / "history.jsonl" prompts = [] if history_file.exists(): with open(history_file, 'r') as f: for line in f: try: data = json.loads(line) if data.get('display'): prompts.append(data['display']) except: continue return prompts[-limit:] if prompts else [] def extract_codex_prompts(limit=100): """提取 Codex 用户提示词""" history_file = Path.home() / ".codex" / "history.jsonl" prompts = [] if history_file.exists(): with open(history_file, 'r') as f: for line in f: try: data = json.loads(line) if data.get('text'): prompts.append(data['text']) except: continue return prompts[-limit:] if prompts else [] def extract_gemini_prompts(limit=100): """提取 Gemini 用户提示词""" gemini_dir = Path.home() / ".gemini" / "tmp" prompts = [] if gemini_dir.exists(): for session_file in gemini_dir.rglob("session-*.json"): try: with open(session_file, 'r') as f: data = json.load(f) messages = data.get('messages', []) for msg in messages: if msg.get('role') == 'user': content = msg.get('content', '') if content: prompts.append(content) except: continue return prompts[-limit:] if prompts else [] def extract_opencode_prompts(limit=100): """提取 OpenCode 用户提示词""" history_file = Path.home() / ".local" / "state" / "opencode" / "prompt-history.jsonl" prompts = [] if history_file.exists(): with open(history_file, 'r') as f: for line in f: try: data = json.loads(line) if data.get('input'): prompts.append(data['input']) except: continue return prompts[-limit:] if prompts else [] def extract_openclaw_prompts(limit=100): """提取 OpenClaw 用户提示词""" agents_dir = Path.home() / ".openclaw" / "agents" prompts = [] if agents_dir.exists(): for session_file in agents_dir.rglob("*.jsonl"): try: with open(session_file, 'r') as f: for line in f: try: data = json.loads(line) if data.get('type') == 'message' and data.get('message', {}).get('role') == 'user': content = data['message'].get('content', []) for item in content: if item.get('type') == 'text': text = item.get('text', '') # 过滤系统消息 if not any(x in text for x in ['Sender (untrusted', 'Conversation info', 'Health check', 'Queued', 'Continue where', 'subagent task', 'Pre-compaction']): prompts.append(text) except: continue except: continue return prompts[-limit:] if prompts else [] def analyze_communication_style(prompts): """分析沟通风格 (E vs I) - 优化版""" if not prompts: return "I" avg_length = sum(len(p) for p in prompts) / len(prompts) short_prompts = sum(1 for p in prompts if len(p) < 15) # 降低阈值从20到15 short_ratio = short_prompts / len(prompts) # 计算详细度得分 detailed_prompts = sum(1 for p in prompts if len(p) > 100) detailed_ratio = detailed_prompts / len(prompts) # 检查是否有问号（提问倾向） question_count = sum(1 for p in prompts if '?' in p or '？' in p or 'how' in p.lower() or 'why' in p.lower() or '如何' in p or '为什么' in p) question_ratio = question_count / len(prompts) # 综合判断：提高 E 型出现概率 e_score = 0 if avg_length > 40: # 降低阈值从50到40 e_score += 1 if short_ratio < 0.4: # 提高阈值从0.3到0.4 e_score += 1 if detailed_ratio > 0.15: # 有15%以上的详细提示词 e_score += 1 if question_ratio > 0.2: # 有20%以上的提问 e_score += 1 return "E" if e_score >= 2 else "I" def analyze_information_processing(prompts): """分析信息处理 (S vs N) - 优化版，支持英文""" if not prompts: return "S" # 扩展关键词，增加英文支持和权重 abstract_keywords = { # 中文（权重2） '概念': 2, '系统': 2, '架构': 3, '方案': 2, '思路': 2, '抽象': 3, '模式': 2, '框架': 2, '理念': 2, '愿景': 2, '战略': 2, # 英文（权重2） 'concept': 2, 'system': 2, 'architecture': 3, 'solution': 2, 'approach': 2, 'pattern': 2, 'framework': 2, 'strategy': 2, 'vision': 2, 'abstract': 3 } concrete_keywords = { # 中文（权重1） '具体': 1, '实现': 1, '细节': 1, '步骤': 1, '操作': 1, '按钮': 1, '颜色': 1, '间距': 1, '像素': 1, '字体': 1, # 英文（权重1） 'specific': 1, 'implement': 1, 'detail': 1, 'step': 1, 'operation': 1, 'button': 1, 'color': 1, 'spacing': 1, 'pixel': 1, 'font': 1 } abstract_score = sum(weight for p in prompts for kw, weight in abstract_keywords.items() if kw in p.lower()) concrete_score = sum(weight for p in prompts for kw, weight in concrete_keywords.items() if kw in p.lower()) # 降低 N 型判定门槛 return "N" if abstract_score > concrete_score * 0.8 else "S" def analyze_decision_making(prompts): """分析决策方式 (T vs F) - 优化版，支持英文""" if not prompts: return "T" # 扩展关键词，增加英文支持 logical_keywords = [ # 中文 '优化', '效率', '性能', '统计', '分析', '测试', '修复', '实现', '算法', '数据', '指标', '评估', '验证', '检查', '对比', # 英文 'optimize', 'efficiency', 'performance', 'statistics', 'analyze', 'test', 'fix', 'implement', 'algorithm', 'data', 'metric', 'evaluate', 'verify', 'check', 'compare' ] emotional_keywords = [ # 中文 '感觉', '喜欢', '希望', '想要', '谢谢', '辛苦', '帮忙', '麻烦', '期待', '担心', '开心', '满意', '友好', '舒服', '美观', # 英文 'feel', 'like', 'hope', 'want', 'thanks', 'thank', 'please', 'appreciate', 'expect', 'worry', 'happy', 'satisfied', 'friendly', 'comfortable', 'beautiful' ] logical_count = sum(1 for p in prompts for kw in logical_keywords if kw in p.lower()) emotional_count = sum(1 for p in prompts for kw in emotional_keywords if kw in p.lower()) # 降低 T 型判定门槛，让 F 型更容易出现 return "T" if logical_count > emotional_count * 1.5 else "F" def analyze_lifestyle(prompts): """分析生活方式 (J vs P) - 优化版，支持英文""" if not prompts: return "P" # 扩展关键词，增加英文支持 planning_keywords = [ # 中文 '计划', '方案', '设计', 'PRD', '文档', '规划', '安排', '流程', '步骤', '阶段', '里程碑', '时间表', 'roadmap', # 英文 'plan', 'design', 'schedule', 'organize', 'structure', 'process', 'procedure', 'milestone', 'timeline', 'roadmap', 'workflow' ] flexible_keywords = [ # 中文 '试试', '测试', '优化', '调整', '改', '换', '尝试', '探索', '实验', '迭代', '看看', '试一下', '改进', # 英文 'try', 'test', 'experiment', 'adjust', 'change', 'modify', 'explore', 'iterate', 'adapt', 'improve', 'refine', 'tweak' ] planning_count = sum(1 for p in prompts for kw in planning_keywords if kw in p.lower()) flexible_count = sum(1 for p in prompts for kw in flexible_keywords if kw in p.lower()) # 保持原有逻辑 return "P" if flexible_count > planning_count else "J" def analyze_mbti(): """主函数：分析 MBTI""" print("🔍 正在提取用户提示词...") # 提取所有工具的提示词 claude_prompts = extract_claude_code_prompts() codex_prompts = extract_codex_prompts() gemini_prompts = extract_gemini_prompts() opencode_prompts = extract_opencode_prompts() openclaw_prompts = extract_openclaw_prompts() # 统计 print(f"\n📊 数据统计：") print(f" Claude Code: {len(claude_prompts)} 条") print(f" Codex: {len(codex_prompts)} 条") print(f" Gemini: {len(gemini_prompts)} 条") print(f" OpenCode: {len(opencode_prompts)} 条") print(f" OpenClaw: {len(openclaw_prompts)} 条") all_prompts = claude_prompts + codex_prompts + gemini_prompts + opencode_prompts + openclaw_prompts total = len(all_prompts) print(f" 总计: {total} 条\n") if total < 10: print("⚠️ 数据量太少，无法进行准确分析") return # 分析四个维度 print("🧠 正在分析 MBTI 维度...\n") e_or_i = analyze_communication_style(all_prompts) s_or_n = analyze_information_processing(all_prompts) t_or_f = analyze_decision_making(all_prompts) j_or_p = analyze_lifestyle(all_prompts) mbti_type = f"{e_or_i}{s_or_n}{t_or_f}{j_or_p}" # 输出结果 print("=" * 50) print(f"🎯 你的 MBTI 类型：{mbti_type}") print("=" * 50) mbti_names = { "INTJ": "建筑师", "INTP": "逻辑学家", "ENTJ": "指挥官", "ENTP": "辩论家", "INFJ": "提倡者", "INFP": "调停者", "ENFJ": "主人公", "ENFP": "竞选者", "ISTJ": "物流师", "ISFJ": "守卫者", "ESTJ": "总经理", "ESFJ": "执政官", "ISTP": "鉴赏家", "ISFP": "探险家", "ESTP": "企业家", "ESFP": "表演者" } print(f"\n类型名称：{mbti_names.get(mbti_type, '未知')}\n") print("📋 维度分析：") print(f" {e_or_i} - {'内向' if e_or_i == 'I' else '外向'}：简洁高效沟通" if e_or_i == 'I' else f" {e_or_i} - 外向：详细表达") print(f" {s_or_n} - {'直觉' if s_or_n == 'N' else '感觉'}：{'抽象思维，系统化方案' if s_or_n == 'N' else '关注具体细节'}") print(f" {t_or_f} - {'思考' if t_or_f == 'T' else '情感'}：{'纯理性，关注效率' if t_or_f == 'T' else '考虑情感因素'}") print(f" {j_or_p} - {'判断' if j_or_p == 'J' else '知觉'}：{'计划性强' if j_or_p == 'J' else '灵活探索，快速迭代'}") print("\n⚠️ 重要提醒：") print(" 1. 基于工作场景的行为推测，可能不反映真实性格") print(" 2. MBTI 本身科学性存疑，仅供参考") print(" 3. 这是基于行为模式的推测，非专业心理测评") if __name__ == "__main__": analyze_mbti() ``` ## 示例输出 ``` 🔍 正在提取用户提示词... 📊 数据统计： Claude Code: 100 条 Codex: 100 条 Gemini: 62 条 OpenCode: 2 条 OpenClaw: 100 条 总计: 364 条 🧠 正在分析 MBTI 维度... ================================================== 🎯 你的 MBTI 类型：INTP ================================================== 类型名称：逻辑学家 📋 维度分析： I - 内向：简洁高效沟通 N - 直觉：抽象思维，系统化方案 T - 思考：纯理性，关注效率 P - 知觉：灵活探索，快速迭代 ⚠️ 重要提醒： 1. 基于工作场景的行为推测，可能不反映真实性格 2. MBTI 本身科学性存疑，仅供参考 3. 这是基于行为模式的推测，非专业心理测评 ``` ## 许可证 MIT