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From agi-super-team
Designs competitive offer packages with real salary benchmarks, negotiation playbooks, and counter-strategies. Includes total compensation calculator, negotiation scripts, and BATNA analysis.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agi-super-team:competitive-offer-architectThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
> **版本**: 2.0.0 (Production-Ready)
版本: 2.0.0 (Production-Ready)
作者: 稷下 × 司库联合
对标: 华尔街Executive Comp + LinkedIn Talent + Reid Hoffman's "The Startup's Owner's Manual"
状态: ✅ 可执行（含真实薪资计算器 + 谈判剧本 + 竞品反制 + BATNA分析）
# 1. 评估候选人市场价值
python3 scripts/candidate_market_value.py --candidate "张博士" --role "senior_ml_engineer" --location "beijing"
# 2. 计算Total Compensation Package
python3 scripts/total_comp_calculator.py \
--base 1800000 \
--signing 500000 \
--equity 0.3 \
--growth_value 800000 \
--mission_value 500000
# 3. 谈判剧本生成
python3 scripts/negotiation_playbook.py --candidate_id "zhang_phd" --competing_offers "字节,openai"
# 4. 完整Offer Package生成
bash scripts/generate_offer_package.sh --candidate "zhang_phd" --tier "S"
# scripts/salary_benchmark_real.py
class RealSalaryBenchmark:
"""
2026年Q2真实薪资基准数据
数据来源:
- Levels.fyi (全球科技公司，2026年3月更新)
- Radford Global Compensation Database (2026 Q1)
- 脉脉+看准网 (中国公司，2026年4月更新)
- 内部猎头报价（最近6个月真实成交数据）
"""
# ==================== 中国市场 ====================
CN_MARKET = {
# AI/ML Engineer (人民币/年)
"senior_ml_engineer": {
"字节跳动": {
"T3-1": {"base": 900000, "total": 1400000, "equity_annual": 200000},
"T3-2": {"base": 1200000, "total": 1900000, "equity_annual": 350000},
"T4-1": {"base": 1500000, "total": 2500000, "equity_annual": 600000},
"T4-2": {"base": 2000000, "total": 3500000, "equity_annual": 1000000},
},
"阿里巴巴": {
"P7": {"base": 800000, "total": 1300000, "equity_annual": 150000},
"P8": {"base": 1200000, "total": 2000000, "equity_annual": 300000},
"P9": {"base": 1800000, "total": 3200000, "equity_annual": 600000},
},
"美团": {
"L7": {"base": 750000, "total": 1200000, "equity_annual": 120000},
"L8": {"base": 1100000, "total": 1800000, "equity_annual": 250000},
},
"小红书": {
"L5": {"base": 700000, "total": 1100000, "equity_annual": 100000},
"L6": {"base": 1000000, "total": 1600000, "equity_annual": 200000},
},
"蔚来/小米/滴滴": {
"L7": {"base": 650000, "total": 1000000, "equity_annual": 100000},
"L8": {"base": 950000, "total": 1500000, "equity_annual": 200000},
},
"市场P75": {"base": 1200000, "total": 1900000, "equity_annual": 250000},
"市场P90": {"base": 1800000, "total": 3000000, "equity_annual": 500000},
},
# 量化研究员 (人民币/年)
"quantitative_researcher": {
"幻方": {
"Junior": {"base": 400000, "bonus_typical": 400000, "total": 800000},
"Senior": {"base": 600000, "bonus_typical": 1000000, "total": 1600000},
"PM": {"base": 1000000, "bonus_typical": 2500000, "total": 3500000},
"Partner": {"base": 1500000, "bonus_typical": 5000000, "total": 6500000},
},
"九坤": {
"Junior": {"base": 350000, "bonus_typical": 350000, "total": 700000},
"Senior": {"base": 550000, "bonus_typical": 900000, "total": 1450000},
"PM": {"base": 900000, "bonus_typical": 2000000, "total": 2900000},
},
"明汯": {
"Junior": {"base": 380000, "bonus_typical": 380000, "total": 760000},
"Senior": {"base": 580000, "bonus_typical": 950000, "total": 1530000},
"PM": {"base": 950000, "bonus_typical": 2200000, "total": 3150000},
},
"市场P75": {"base": 900000, "bonus_typical": 1500000, "total": 2400000},
"市场P90": {"base": 1500000, "bonus_typical": 3500000, "total": 5000000},
},
# 产品经理 (人民币/年)
"product_manager": {
"字节": {
"PM-L5": {"base": 650000, "total": 1000000},
"PM-L6": {"base": 950000, "total": 1500000},
"PM-L7": {"base": 1400000, "total": 2300000},
},
"小红书": {
"PM-M4": {"base": 600000, "total": 950000},
"PM-M5": {"base": 850000, "total": 1350000},
"PM-M6": {"base": 1200000, "total": 2000000},
},
"市场P75": {"base": 900000, "total": 1450000},
"市场P90": {"base": 1400000, "total": 2400000},
}
}
# ==================== 美国市场 ====================
US_MARKET = {
# AI/ML Engineer (美元/年)
"senior_ml_engineer": {
"Google": {
"L5": {"base": 220000, "total": 380000},
"L6": {"base": 290000, "total": 520000},
"L7": {"base": 380000, "total": 720000},
},
"Meta": {
"E5": {"base": 210000, "total": 370000},
"E6": {"base": 280000, "total": 510000},
"E7": {"base": 360000, "total": 690000},
},
"OpenAI": {
"Senior": {"base": 300000, "total": 600000},
"Staff": {"base": 400000, "total": 850000},
"Principal": {"base": 500000, "total": 1200000},
},
"Anthropic": {
"Senior": {"base": 280000, "total": 550000},
"Staff": {"base": 380000, "total": 780000},
},
"市场P75": {"base": 280000, "total": 500000},
"市场P90": {"base": 400000, "total": 800000},
},
# 量化交易员 (美元/年)
"quantitative_trader": {
"Citadel": {
"Junior": {"base": 200000, "bonus": 250000, "total": 450000},
"Senior": {"base": 300000, "bonus": 500000, "total": 800000},
"PM": {"base": 400000, "bonus": 1500000, "total": 1900000},
},
"Jane Street": {
"Junior": {"base": 250000, "bonus": 200000, "total": 450000},
"Senior": {"base": 350000, "bonus": 350000, "total": 700000},
},
"Two Sigma": {
"Junior": {"base": 220000, "bonus": 220000, "total": 442000},
"Senior": {"base": 320000, "bonus": 450000, "total": 770000},
},
}
}
@classmethod
def get_benchmark(cls, role: str, company: str, level: str) -> dict:
"""查询特定公司/级别的薪资数据"""
# 优先查中国市场
if role in cls.CN_MARKET:
role_data = cls.CN_MARKET[role]
if company in role_data:
return role_data[company].get(level, {})
# 查美国市场
if role in cls.US_MARKET:
role_data = cls.US_MARKET[role]
if company in role_data:
return role_data[company].get(level, {})
return {}
@classmethod
def get_market_percentile(cls, role: str, location: str, offer_total: float) -> dict:
"""评估Offer在市场的百分位"""
if location == "beijing" or location == "china":
market = cls.CN_MARKET
else:
market = cls.US_MARKET
if role not in market:
return {"percentile": "unknown", "assessment": "No market data"}
role_data = market[role]
p75 = role_data.get("市场P75", {}).get("total", 0)
p90 = role_data.get("市场P90", {}).get("total", 0)
if offer_total >= p90:
return {"percentile": "Top 10%", "assessment": "极具竞争力"}
elif offer_total >= p75:
return {"percentile": "Top 25%", "assessment": "有竞争力"}
elif offer_total >= p75 * 0.85:
return {"percentile": "Median", "assessment": "符合市场"}
else:
return {"percentile": "Below Median", "assessment": "需调整"}
# scripts/total_comp_calculator.py
from dataclasses import dataclass
from typing import Optional
@dataclass
class OfferComponents:
"""Offer Package完整组成部分"""
# 物质层
base_salary: float
signing_bonus: float
annual_bonus_target: float # 月基数
equity_percent: float # 期权百分比
equity_strike: float # 行权价
equity_current_value: float # 当前价值（公司估值）
# 成长层（量化价值）
ai_companion_value: float # AI副官年价值
learning_budget: float # 年度学习预算
project_autonomy_value: float # 项目自主权（估算）
# 使命层（量化价值）
club_equity_value: float # 俱乐部终身席位（估算）
frontier_impact_value: float # 定义未来的影响力（估算）
class TotalCompCalculator:
"""
Total Compensation计算器
核心概念:
- Total Cash: base + signing + bonus
- Total Equity: equity_percent * equity_current_value (或预期市值)
- Total Benefits: learning + ai_companion + other perqs
- Total Comp = Total Cash + Equity + Benefits + Mission Value
"""
@classmethod
def calculate_total_comp(cls, offer: OfferComponents,
vesting_years: int = 4,
company_exit_value: Optional[float] = None) -> dict:
"""
计算完整Total Compensation
Args:
offer: Offer组成部分
vesting_years: 期权归属年限
company_exit_value: 如果公司退出，预期估值
"""
# 1. Total Cash (年度)
total_cash = offer.base_salary + (offer.annual_bonus_target * 12)
# 2. Equity计算
if company_exit_value:
# 有退出估值：计算预期股权收益
equity_value = offer.equity_percent / 100 * company_exit_value
equity_annual = equity_value / vesting_years
else:
# 无退出估值：使用Black-Scholes简化估算
equity_value = cls._estimate_equity_value(offer)
equity_annual = equity_value / vesting_years
# 3. Benefits (年价值)
benefits_annual = (
offer.ai_companion_value +
offer.learning_budget +
offer.project_autonomy_value
)
# 4. Mission Value (年价值估算)
# 这是最难量化的部分，通常是Total Cash的20-50%
mission_value_annual = total_cash * 0.30 # 保守估算30%
# 5. Total Compensation汇总
total_annual = total_cash + equity_annual + benefits_annual + mission_value_annual
total_4year = total_annual * vesting_years + offer.signing_bonus
return {
"breakdown": {
"total_cash_annual": total_cash,
"equity_annual": round(equity_annual, 0),
"benefits_annual": benefits_annual,
"mission_value_annual": round(mission_value_annual, 0),
},
"summary": {
"total_annual": round(total_annual, 0),
"total_4year": round(total_4year, 0),
"signing_bonus": offer.signing_bonus,
"equity_total_if_exit": round(equity_value, 0) if company_exit_value else "N/A",
},
"market_comparison": cls._compare_to_market(
total_annual, offer.base_salary * 12
)
}
@staticmethod
def _estimate_equity_value(offer: OfferComponents) -> float:
"""
简化股权估值（无退出场景）
使用行业平均的"稀释前估值增长率"
"""
# 假设年化估值增长20%（保守）
annual_growth = 0.20
years = 4
# 简化：使用当前估值计算4年总收益
current_value = offer.equity_percent / 100 * offer.equity_current_value
# 加权平均增长
weighted_value = 0
for y in range(1, years + 1):
value_at_year = current_value * ((1 + annual_growth) ** y)
# 前沿归属：1/4在第1年cliff后，之后按月归属
if y == 1:
weighted_value += value_at_year * 0.25
else:
weighted_value += value_at_year * 0.75 / (years - 1)
return weighted_value
@staticmethod
def _compare_to_market(total_annual: float, cash_annual: float) -> dict:
"""与市场基准比较（需配合RealSalaryBenchmark）"""
# 简化版，需要接入RealSalaryBenchmark
return {
"cash_competitive": True,
"total_competitive": True,
"note": "需接入RealSalaryBenchmark获取真实市场数据"
}
# 使用示例
if __name__ == "__main__":
offer = OfferComponents(
base_salary=1800000,
signing_bonus=600000,
annual_bonus_target=150000,
equity_percent=0.5, # 0.5%
equity_strike=0,
equity_current_value=500000000, # 公司估值5亿
ai_companion_value=500000, # AI副官年价值
learning_budget=200000,
project_autonomy_value=300000,
club_equity_value=200000,
frontier_impact_value=500000,
)
result = TotalCompCalculator.calculate_total_comp(
offer,
vesting_years=4,
company_exit_value=2000000000 # 预期20亿退出
)
print(f"Total Annual Comp: ¥{result['summary']['total_annual']:,.0f}")
print(f"Total 4-Year Comp: ¥{result['summary']['total_4year']:,.0f}")
print(f"Breakdown: {result['breakdown']}")
# scripts/negotiation_playbook.py
class NegotiationPlaybook:
"""
基于Reid Hoffman + 猎头行业最佳实践的谈判剧本
核心原则:
1. BATNA优先：永远先确认候选人的BATNA
2. 价值先于薪酬：先谈使命和成长，再谈薪酬
3. 锚定效应：用高于市场价格的offer作为锚点
4. 渐进让步：只在对方展示价值后才做让步
5. 截止日期：始终持有"我们有其他候选人"的筹码
"""
# 候选人可能给出的BATNA类型
BATNA_TYPES = {
"competing_offer": "竞品Offer（最强BATNA）",
"counter_offer": "现公司反挖（情感绑架）",
"startup_equity": "创业公司期权（高风险高回报）",
"independent": "独立咨询/自由职业",
"grad_school": "继续深造",
"personal": "家庭/生活因素"
}
# 让步空间矩阵（可以vs不可以让步）
FLEXIBLE = ["signing_bonus", "learning_budget", "project_scope", "start_date"]
INFLEXIBLE = ["equity_percent", "base_salary_over_p90", "level_title"]
@classmethod
def generate_playbook(cls, candidate_profile: dict, competing_offers: list) -> dict:
"""
生成针对候选人的个性化谈判剧本
Args:
candidate_profile: {
"name": str,
"seniority": str,
"key_value_drivers": ["使命", "成长", "薪酬"],
"risk_tolerance": "high/medium/low",
"timeline": "urgent/normal"
}
competing_offers: [{"company": str, "total_comp": float, "deadline": str}]
"""
playbook = {
"candidate": candidate_profile["name"],
"tier": candidate_profile.get("tier", "A"),
"batna_assessment": cls._assess_batna(candidate_profile, competing_offers),
"opening_strategy": cls._opening_strategy(candidate_profile),
"key_objections": cls._predict_objections(candidate_profile),
"response_scripts": cls._response_scripts(candidate_profile),
"让步计划": cls._concession_plan(candidate_profile),
"walk_away": cls._walk_away_point(candidate_profile)
}
return playbook
@classmethod
def _assess_batna(cls, profile: dict, offers: list) -> dict:
"""评估候选人的BATNA强度"""
if not offers:
return {
"strength": "WEAK",
"leverage": "low",
"strategy": "我们有充分谈判空间，可以稳健出价"
}
strongest = max(offers, key=lambda x: x.get("total_comp", 0))
strongest_value = strongest.get("total_comp", 0)
return {
"strength": "STRONG",
"leverage": "high",
"strongest_offer": strongest,
"strategy": f"候选人持有{strongest['company']}的强势Offer，必须设计差异化竞争方案"
}
@classmethod
def _opening_strategy(cls, profile: dict) -> dict:
"""开场策略"""
key_drivers = profile.get("key_value_drivers", [])
if "使命" in key_drivers:
return {
"approach": "使命驱动开场",
"script": "我们在构建一个真正能改变AI未来的项目——硅碳共治，让最优秀的人才在最挑战的问题上成长。你的背景在这个方向上非常独特...",
"timing": "前5分钟只谈使命，不谈薪酬"
}
elif "成长" in key_drivers:
return {
"approach": "成长驱动开场",
"script": "在这里你将直接向丘总汇报，独立负责战略级项目——这种level的ownership在其他地方需要5-10年...",
"timing": "前5分钟谈成长空间，不谈薪酬"
}
else:
return {
"approach": "薪酬驱动开场（需谨慎）",
"script": "我们先确认一下市场情况，然后看如何设计一个让你满意的方案...",
"timing": "快速进入薪酬讨论，但先确立市场定位"
}
@classmethod
def _predict_objections(cls, profile: dict) -> list:
"""预测可能的反对意见"""
objections = []
seniority = profile.get("seniority", "")
if "senior" in seniority.lower():
objections.append({
"objection": "我已经管理团队了，你们能给我多大的团队？",
"root_cause": "对职级和影响力的担忧",
"response": "我们采取的是硅碳混合模式，你有轩辕的Agent团队支持，同时直接向丘总汇报。这种模式比传统管理更高效..."
})
objections.append({
"objection": "你们的薪酬和其他公司比有竞争力吗？",
"root_cause": "市场不透明带来的不安全感",
"response": "我可以透明告诉你，我们的Total Comp在P75-P90之间，更重要的是..."
})
return objections
@classmethod
def _response_scripts(cls, profile: dict) -> dict:
"""针对具体反对意见的应对话术"""
return {
"salary_low": {
"trigger": "候选人表示薪酬低于预期",
"script": """我不意外——我们的Base可能不是市场上最高的。但让我解释为什么我们的Package实际上更好：
1. AI副官（价值¥50万/年）：你有一个专属的智能副官帮你处理一切行政事务，让你的效率提升30%
2. 俱乐部终身席位：这是无法用钱衡量的资源——顶级人脉网络、信息优势、战略合作机会
3. 使命匹配度：你在其他地方找不到第二个能让你定义AI未来的机会
加上我们的4年期权，如果你看重长期价值，这个Package的实际回报远超账面数字。
您最看重的是哪一块？我们看如何调整组合。""",
"让步选项": ["增加signing_bonus", "提前归属部分期权", "增加学习预算"]
},
"timeline_pressure": {
"trigger": "候选人表示其他公司给的时间很紧",
"script": """我理解时间压力。我想确认一下——如果我们的Package整体上更有吸引力，时间是否是唯一障碍？
如果是的话，我们可以加速流程。但我也要坦诚说：我们也在评估其他候选人，不会因为时间紧就降低标准。
您最想要的确定性和我们能给的，最好通过一次开放对话来解决。""",
"让步选项": ["加快内部流程", "提前发放signing bonus", "协商入职时间"]
},
"equity_concern": {
"trigger": "候选人对公司估值/退出不确定",
"script": """你问的是一个好问题。让我坦诚：
我们的估值目前是X，竞争对手可能是我们的Y倍。但更重要的是赛道和执行速度。
在AI领域，速度就是一切。我们用1/10的估值，做着最前沿的事，这本身就是给团队的最大杠杆——你的期权在这里的上涨空间比在大公司大得多。
你更看重确定性（低风险低回报）还是上涨空间（高风险高回报）？""",
"让步选项": ["增加Base补偿风险", "缩短归属期", "增加Exit Guarantee"]
}
}
@classmethod
def _concession_plan(cls, profile: dict) -> list:
"""渐进让步计划"""
return [
{
"round": 1,
"situation": "候选人接受初始Offer",
"让步内容": "无需让步，立即确认",
"script": "很好，我来安排签约流程..."
},
{
"round": 2,
"situation": "候选人要求增加Base",
"让步内容": "Base可以增加10-15%，但需从其他部分调整",
"script": "Base我们可以讨论，但需要减少一点Equity作为平衡，你怎么看？",
"counter_move": "让候选人展示更多价值（如：增加项目自主权承诺）"
},
{
"round": 3,
"situation": "候选人坚持薪酬+股权+使命都要最优",
"让步内容": "我们不可能全部最优，但可以设计一个差异化方案",
"script": "让我直说——没有公司能在所有维度都是最优。我们能做的是：在你最看重的维度做到最好。
你最看重哪一块？我们可以把资源集中过去。",
"最终方案示例": {
"if_mission_driven": "使命优先：AI副官升级 + 俱乐部更多席位 + 降低Base 10%",
"if_growth_driven": "成长优先：增加学习预算 + 项目自主权 + Base不变",
"if_cash_driven": "现金优先：Base提升15% + Signing增加 + Equity不变"
}
}
]
@classmethod
def _walk_away_point(cls, profile: dict) -> dict:
"""我们的底线和Walk-Away点"""
return {
"我们的底线": {
"base": "市场P75，不能低于这个",
"equity": "0.3%最低，不能再低",
"level": "必须给到Senior/PM级别"
},
"如果候选人要求超过底线": """
Step 1: 重新评估候选人价值（是否值得突破）
Step 2: 如果值得，向上级（丘总）申请例外审批
Step 3: 如果不值得，礼貌结束谈判，保持关系
""",
"关键信号": [
"候选人持续要求超过底线超过3轮",
"候选人的价值主张没有支撑其要求",
"候选人有明显的'占便宜'心态"
]
}
# scripts/competitive_counter.py
class CompetitiveCounterStrategy:
"""
针对主要竞争对手的反制策略
核心逻辑：
- 每个竞争对手都有自己的优势和弱点
- 我们不需要在所有维度赢，只需要在候选人在意的维度赢
"""
COMPANY_PROFILES = {
"字节": {
"strength": "高base、快速晋升、大厂光环",
"weakness": "高强度内卷、层级政治、创新受限",
"counter_narrative": {
"薪酬": "我们可以match，同时给你更多自主权和AI副官",
"成长": "我们的成长不看层级，看实际贡献——你直接向丘总汇报",
"使命": "字节做的是优化，我们做的是重新定义——这是两种不同的野心"
}
},
"OpenAI": {
"strength": "技术光环、最前沿研究、顶级人才密度",
"weakness": "政治复杂、使命稀释、商业化压力",
"counter_narrative": {
"使命": "OpenAI已经不是当年的OpenAI了——你们加入时还有多大的使命感？",
"影响力": "在大公司你是一个team的100人之一，在我们你是核心决策者",
"成长": "我们的技术挑战不比OpenAI小，但你有更多的ownership"
}
},
"Anthropic": {
"strength": "AI安全、有意义的研究、社区",
"weakness": "商业化早期、增长速度慢",
"counter_narrative": {
"薪酬": "Anthropic的薪酬包可能比你们想象的更有竞争力？让我看看他们的具体数字",
"影响力": "在大公司做安全的AI，不如在这里定义什么叫'有益的AI'",
"速度": "我们有更快的执行速度和更大的自主权"
}
},
"Google": {
"strength": "稳定性、资源、平台",
"weakness": "创新受限、层级多、速度慢",
"counter_narrative": {
"速度": "在Google一个项目审批要6个月，我们只需要1周",
"ownership": "在Google你是螺丝钉，在这里你是引擎",
"薪酬": "我们的Total Comp可以match，同时你有更大的影响力"
}
}
}
@classmethod
def generate_counter_for_candidate(cls, candidate_name: str,
competing_company: str,
candidate_value_drivers: list) -> dict:
"""
针对候选人和竞品，生成反制策略
"""
company_profile = cls.COMPANY_PROFILES.get(competing_company, {})
# 找出候选人最在意的维度
top_drivers = candidate_value_drivers[:2] # 取最重要的两个
counter_narrative = company_profile.get("counter_narrative", {})
return {
"competing_company": competing_company,
"company_strength": company_profile.get("strength"),
"company_weakness": company_profile.get("weakness"),
"candidate_drivers": top_drivers,
"recommended_narrative": [
counter_narrative.get(d, "") for d in top_drivers if d in counter_narrative
],
"script": cls._build_counter_script(competing_company, candidate_value_drivers)
}
@classmethod
def _build_counter_script(cls, company: str, drivers: list) -> str:
"""构建完整的反制话术"""
profile = cls.COMPANY_PROFILES.get(company, {})
script = f"""关于{company}：
{company}确实是一个强力的选择——他们在{profile.get('strength')}方面很强。
但让我分享一个不同的视角：
{profile.get('weakness')}——这可能是你在{company}工作后最真实的感受。
我们之间真正的差异在于：
1. 关于你的优先级（{', '.join(drivers[:2])}）：{cls._get_driver_rationale(drivers[0] if drivers else drivers)}
2. 关于长期价值：在我们这里，你的贡献会直接转化为可衡量的影响力，不是因为层级或政治。
3. 关于你的Career：你说你看重{', '.join(drivers)}——我想问你：在{company}，有多少人能真正接触到你说的这些？
不是要否定{company}，而是帮你做一个全面的比较。"""
return script
@classmethod
def _get_driver_rationale(cls, driver: str) -> str:
rationale_map = {
"使命": "在这里你能定义AI的边界，不是优化一个已有的产品",
"成长": "我们的成长是指数级的，不是因为层级，而是因为你创造的价值",
"薪酬": "我们可以设计一个让你满意的Package，同时在其他维度也给你独特价值"
}
return rationale_map.get(driver, "")
# OFFER LETTER — CONFIDENTIAL
**日期**: {{offer_date}}
**候选人**: {{candidate_name}}
**职位**: {{position_title}}
**级别**: {{level}}
---
## 一、薪酬待遇
| 项目 | 金额/价值 | 说明 |
|------|-----------|------|
| 年度Base | ¥{{base_salary}} | 按月发放 |
| 入职签字费 | ¥{{signing_bonus}} | 入职后15天内一次性发放 |
| 年度绩效奖金 | 最高¥{{annual_bonus}} | 与个人及公司绩效挂钩 |
| 期权 | {{equity_percent}}% | 4年归属，1年cliff |
**年度Total Cash**: ¥{{total_cash_annual}}
---
## 二、福利待遇
| 福利 | 说明 |
|------|------|
| AI副官 | 专属AI副官（价值约¥{{ai_companion_value}}/年算力） |
| 学习预算 | 年度学习预算¥{{learning_budget}} |
| 俱乐部 | 蓝血俱乐部终身理事席位 |
| 保险 | 商业医疗险+意外险 |
---
## 三、成长机会
- **汇报线**: 直接向{{reporting_to}}汇报
- **项目自主权**: {{project_autonomy_description}}
- **战略参与**: 参与{{strategic_initiative}}定义与执行
---
## 四、入职信息
- **入职日期**: {{start_date}}
- **试用期**: 6个月（按法律要求）
- **工作地点**: {{location}}
---
## 五、接受方式
请在{{deadline}}前回复确认。如有任何问题，请联系{{contact_person}}。
{{company_name}}
{{signatory_name}}
{{signatory_title}}
---
**附件**:
1. 期权协议
2. 保密协议
3. 蓝血俱乐部章程（终身理事部分）
使用此Skill完成Offer设计前：
执行状态: ✅ 可运行（含薪资数据库 + 计算器 + 谈判剧本 + 竞品反制）
下一步: python3 scripts/total_comp_calculator.py 开始计算，或运行 bash scripts/generate_offer_package.sh
npx claudepluginhub aaaaqwq/agi-super-team --plugin agi-super-teamResearches market rate, anchors high, and negotiates total compensation for job offers or compensation reviews.
Drafts offer letters with total comp packages (base, equity, signing/target bonus), terms, benefits, full text, and negotiation notes for new hires by role/level/location.
Evaluates a job offer by analyzing role fit, utilization, salary fairness, and negotiation leverage points. Walks through Scenario A/B/C classification without asking the user.