Scan to join WeChat groupMBTIClaude
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
/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：
- 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
- MBTI itself has scientific controversies, for reference only | MBTI 本身科学性存疑，仅供参考
- 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
- 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
#!/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