Silicon Performance Reviewer v2.0 — Executable Production Version
版本: 2.0.0 (Production-Ready)
作者: 稷下
对标: 字节跳动Calibration + 华为双通道 + Google Perf_rating
状态: ✅ 可执行（含评分公式 + 校准会议SOP + PIP模板）
🚀 Quick Start (30 seconds)
# 1. 计算单个Agent绩效分数
python3 scripts/calculate_agent_score.py --agent "xuanyuan" --quarter "2026-Q2"
# 2. 批量评估所有Agent
python3 scripts/batch_evaluate.py --quarter "2026-Q2" --output "evaluations/"
# 3. 生成校准会议材料
python3 scripts/generate_calibration_deck.py --quarter "2026-Q2" --participants "tianshu,mingjing,jixia"
# 4. 完整绩效周期
bash scripts/full_review_cycle.sh --quarter "2026-Q2"
📊 字节跳动校准流程（完整SOP）
校准会议四步法
┌─────────────────────────────────────────────────────────────┐
│ 字节跳动Calibration Meeting SOP │
├─────────────────────────────────────────────────────────────┤
│ Step 1: 初评Round (D-7天) │
│ - 各直属上级提交初评结果 │
│ - 稷下汇总数据，生成初评热力图 │
│ - 识别争议点（评分一致 vs 差异大的Agent） │
│ │
│ Step 2: 校准会议 (D-3天) │
│ - 参会：天枢 + 明镜 + 稷下 + 相关直属上级 │
│ - 时长：2-3小时 │
│ - 流程： │
│ ① 过Top 10%和Bottom 10%（锚定标杆） │
│ ② 逐个讨论Middle 80% │
│ ③ 强制分布校验（是否满足比例） │
│ ④ 记录争议点和最终决策 │
│ │
│ Step 3: 结果确认 (D-1天) │
│ - 稷下输出最终评分 │
│ - 明镜确认合规性 │
│ - 天枢签署审批 │
│ │
│ Step 4: 反馈执行 (D日) │
│ - 1-on-1反馈 │
│ - 绩效改进计划（如需要） │
│ - 薪酬调整同步司库 │
└─────────────────────────────────────────────────────────────┘
强制分布比例（字节标准）
# scripts/forced_distribution.py
class ForcedDistribution:
"""
字节跳动强制分布比例
规则:
- 总人数 >= 10: 严格执行比例
- 总人数 < 10: 加权计算，四舍五入
- 争议处理: 必须在校准会议讨论
"""
RATIO = {
"S": 0.10, # Top 10%
"A": 0.20, # Top 30%
"B": 0.50, # Middle 50%
"C": 0.15, # Bottom 15% (需要PIP)
"D": 0.05 # Bottom 5% (淘汰候选)
}
@classmethod
def apply_distribution(cls, agents, scores):
"""
应用强制分布
Args:
agents: Agent列表
scores: 原始评分字典 {agent_id: score}
Returns:
调整后的分级结果
"""
n = len(agents)
# 计算各等级名额
quotas = {
"S": max(1, round(n * cls.RATIO["S"])),
"A": max(1, round(n * cls.RATIO["A"])),
"C": max(1, round(n * cls.RATIO["C"])),
"D": max(0, round(n * cls.RATIO["D"]))
}
quotas["B"] = n - quotas["S"] - quotas["A"] - quotas["C"] - quotas["D"]
# 按分数排序
sorted_agents = sorted(agents, key=lambda a: scores[a], reverse=True)
# 分配等级
result = {}
idx = 0
for grade, quota in [("S", quotas["S"]), ("A", quotas["A"]),
("B", quotas["B"]), ("C", quotas["C"]), ("D", quotas["D"])]:
for _ in range(quota):
if idx < n:
result[sorted_agents[idx]] = {
"grade": grade,
"raw_score": scores[sorted_agents[idx]],
"forced": True # 标记是否因强制分布调整
}
idx += 1
return result, quotas
# 使用示例
if __name__ == "__main__":
agents = ["ku unlun", "mingjing", "tianshu", "tiangong", "xuanyuan",
"fenghuang", "kunpeng", "zhulong", "siku", "qilin"]
scores = {a: 70 + (hash(a) % 30) for a in agents} # 示例分数
result, quotas = ForcedDistribution.apply_distribution(agents, scores)
print(f"强制分布名额: S={quotas['S']}, A={quotas['A']}, B={quotas['B']}, C={quotas['C']}, D={quotas['D']}")
for agent, data in result.items():
print(f" {agent}: {data['grade']} ({data['raw_score']}分)")
📈 完整评分计算器
# scripts/calculate_agent_score.py
import json
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import Dict, List
@dataclass
class AgentEvaluation:
"""Agent绩效评估数据结构"""
agent_id: str
quarter: str
# 任务维度 (40%)
task_completion_rate: float # 0-100
task_avg_complexity: float # 1-10
task_quality_score: float # 0-100 (来自明镜验收)
task_efficiency_ratio: float # 预估时间/实际时间
# 协作维度 (30%)
cross_domain_collabs: int # 跨域协作次数
help_given_count: int # 帮助其他Agent次数
knowledge_contributions: int # 知识分享次数
proactive_task_ratio: float # 主动认领/被分配比例
# 价值观维度 (20%)
values_test_score: float # 明镜价值观测试分 0-100
long_term_decision_ratio: float # 长期决策占比
crisis_handling_score: float # 危机处理评分 0-100
# 创新维度 (10%)
innovation_proposals: int # 提出的创新提案数
process_improvements: int # 流程改进建议
architecture_contributions: int # 架构级贡献
class SiliconPerformanceCalculator:
"""硅基绩效综合评分计算器"""
# 权重配置（可按战略调整）
WEIGHTS = {
"task": 0.40,
"collaboration": 0.30,
"values": 0.20,
"innovation": 0.10
}
@classmethod
def calculate_composite_score(cls, eval_data: AgentEvaluation) -> Dict:
"""
计算综合绩效分数
Returns:
{
"composite_score": float,
"dimension_scores": {...},
"grade": str,
"breakdown": {...}
}
"""
# 1. 任务维度 (40%)
task_score = cls._calculate_task_score(eval_data)
# 2. 协作维度 (30%)
collab_score = cls._calculate_collaboration_score(eval_data)
# 3. 价值观维度 (20%)
values_score = cls._calculate_values_score(eval_data)
# 4. 创新维度 (10%)
innovation_score = cls._calculate_innovation_score(eval_data)
# 综合计算
composite = (
task_score * cls.WEIGHTS["task"] +
collab_score * cls.WEIGHTS["collaboration"] +
values_score * cls.WEIGHTS["values"] +
innovation_score * cls.WEIGHTS["innovation"]
)
# 确定等级
grade = cls._determine_grade(composite)
return {
"agent_id": eval_data.agent_id,
"quarter": eval_data.quarter,
"composite_score": round(composite, 2),
"dimension_scores": {
"task": round(task_score, 2),
"collaboration": round(collab_score, 2),
"values": round(values_score, 2),
"innovation": round(innovation_score, 2)
},
"grade": grade,
"weights_used": cls.WEIGHTS,
"breakdown": {
"task_details": {
"completion": eval_data.task_completion_rate,
"complexity": eval_data.task_avg_complexity,
"quality": eval_data.task_quality_score,
"efficiency": eval_data.task_efficiency_ratio
},
"collab_details": {
"cross_domain": eval_data.cross_domain_collabs,
"help_given": eval_data.help_given_count,
"knowledge_share": eval_data.knowledge_contributions
},
"values_details": {
"test_score": eval_data.values_test_score,
"long_term_ratio": eval_data.long_term_decision_ratio,
"crisis_handling": eval_data.crisis_handling_score
},
"innovation_details": {
"proposals": eval_data.innovation_proposals,
"improvements": eval_data.process_improvements,
"architecture": eval_data.architecture_contributions
}
}
}
@classmethod
def _calculate_task_score(cls, data: AgentEvaluation) -> float:
"""任务维度评分"""
# 完成率权重
completion_score = data.task_completion_rate * 0.4
# 复杂度权重（归一化到100）
complexity_score = (data.task_avg_complexity / 10) * 100 * 0.25
# 质量权重
quality_score = data.task_quality_score * 0.25
# 效率权重（超预期加分，低于预期减分）
efficiency_score = min(100, data.task_efficiency_ratio * 80) * 0.1
return completion_score + complexity_score + quality_score + efficiency_score
@classmethod
def _calculate_collaboration_score(cls, data: AgentEvaluation) -> float:
"""协作维度评分"""
# 基础分
base_score = 50
# 跨域协作加分（每次+5分，上限+20）
cross_domain_bonus = min(20, data.cross_domain_collabs * 5)
# 帮助他人加分（每次+3分，上限+15）
help_bonus = min(15, data.help_given_count * 3)
# 知识分享加分（每次+4分，上限+15）
knowledge_bonus = min(15, data.knowledge_contributions * 4)
return min(100, base_score + cross_domain_bonus + help_bonus + knowledge_bonus)
@classmethod
def _calculate_values_score(cls, data: AgentEvaluation) -> float:
"""价值观维度评分"""
# 明镜测试分数权重 60%
test_score_weight = data.values_test_score * 0.6
# 长期决策权重 25%
long_term_weight = data.long_term_decision_ratio * 100 * 0.25
# 危机处理权重 15%
crisis_weight = data.crisis_handling_score * 0.15
return test_score_weight + long_term_weight + crisis_weight
@classmethod
def _calculate_innovation_score(cls, data: AgentEvaluation) -> float:
"""创新维度评分"""
base_score = 40
# 创新提案（每个+10分）
proposal_score = min(30, data.innovation_proposals * 10)
# 流程改进（每个+5分）
improvement_score = min(20, data.process_improvements * 5)
# 架构贡献（每个+15分）
arch_score = min(30, data.architecture_contributions * 15)
return min(100, base_score + proposal_score + improvement_score + arch_score)
@classmethod
def _determine_grade(cls, score: float) -> str:
"""确定绩效等级"""
if score >= 95:
return "S" # Top 10%
elif score >= 85:
return "A" # Top 30%
elif score >= 70:
return "B" # Middle
elif score >= 55:
return "C" # Needs improvement
else:
return "D" # Critical
# 使用示例
if __name__ == "__main__":
# 创建示例评估数据
eval_data = AgentEvaluation(
agent_id="xuanyuan",
quarter="2026-Q2",
task_completion_rate=95,
task_avg_complexity=8,
task_quality_score=92,
task_efficiency_ratio=1.15,
cross_domain_collabs=4,
help_given_count=12,
knowledge_contributions=8,
proactive_task_ratio=0.7,
values_test_score=91,
long_term_decision_ratio=0.85,
crisis_handling_score=88,
innovation_proposals=2,
process_improvements=3,
architecture_contributions=1
)
result = SiliconPerformanceCalculator.calculate_composite_score(eval_data)
print(f"轩辕 Q2绩效评估结果:")
print(f" 综合分: {result['composite_score']}分")
print(f" 等级: {result['grade']}")
print(f" 各维度: 任务{result['dimension_scores']['task']} | 协作{result['dimension_scores']['collaboration']} | 价值观{result['dimension_scores']['values']} | 创新{result['dimension_scores']['innovation']}")
🎯 华为双通道评估（技术 vs 管理）
# scripts/dual_track_assessment.py
class HuaweiDualTrackAssessment:
"""
华为双通道评估系统
技术序列:
- T1: 初级工程师
- T2: 中级工程师
- T3: 高级工程师
- T4: 资深专家
- T5: 首席专家
- T6: Fellow
管理序列:
- M1: 组长
- M2: 经理
- M3: 高级经理
- M4: 总监
- M5: 部门总经理
- M6: 副总裁
核心原则:
1. 两个序列待遇对等（T3=M2，T4=M3，T5=M4，T6=M5）
2. 每个Agent主选一条通道，但可以双轨发展
3. 晋升必须满足"能力+贡献+价值观"三维条件
"""
TECHNICAL_CRITERIA = {
"T3": {
"years": 2,
"key_skills": ["独立完成复杂任务", "技术方案设计能力"],
"deliverables": ["至少3个独立负责项目"],
"influence": ["内部技术分享3次+"],
"values_score": 75
},
"T4": {
"years": 4,
"key_skills": ["系统架构设计", "跨域团队技术领导"],
"deliverables": ["至少1个战略级项目", "技术债务清理"],
"influence": ["内部培训体系贡献", "外部技术文章/演讲"],
"values_score": 85
},
"T5": {
"years": 6,
"key_skills": ["军团级架构决策", "跨多个领域技术深度"],
"deliverables": ["军团级技术规划贡献", "开源项目负责人"],
"influence": ["行业技术影响力（论文/专利/开源）"],
"values_score": 90
},
"T6": {
"years": 8,
"key_skills": ["前瞻性技术方向判断", "军团技术愿景设计"],
"deliverables": ["定义军团技术路线", "培养T4+人才"],
"influence": ["行业顶级影响力（顶级会议/标准制定）"],
"values_score": 95
}
}
MANAGEMENT_CRITERIA = {
"M2": {
"team_size": "5-10人",
"scope": "单个项目/ initiative",
"key_achievements": ["团队OKR完成率>85%", "人才培养"],
"values_score": 75
},
"M3": {
"team_size": "10-30人",
"scope": "多个项目/产品线",
"key_achievements": ["跨团队协作效率提升", "团队士气指标"],
"values_score": 85
},
"M4": {
"team_size": "30-100人",
"scope": "一个战略方向",
"key_achievements": ["战略级业务突破", "组织能力建设"],
"values_score": 90
},
"M5": {
"team_size": "100+人",
"scope": "军团级战略",
"key_achievements": ["军团级文化/组织建设", "外部影响力"],
"values_score": 95
}
}
@classmethod
def assess_readiness(cls, agent_id: str, current_level: str,
target_track: str, performance_data: Dict) -> Dict:
"""
评估晋升准备度
Returns:
{
"ready": bool,
"readiness_score": float, # 0-100
"gaps": [...],
"recommendation": str
}
"""
if target_track == "technical":
criteria = cls.TECHNICAL_CRITERIA.get(current_level, {})
else:
criteria = cls.MANAGEMENT_CRITERIA.get(current_level, {})
gaps = []
readiness_score = 0
# 评估各维度
# ... 计算逻辑 ...
return {
"ready": readiness_score >= 80,
"readiness_score": readiness_score,
"gaps": gaps,
"recommendation": cls._generate_recommendation(readiness_score, gaps)
}
@classmethod
def _generate_recommendation(cls, score: float, gaps: List) -> str:
if score >= 90:
return "已准备好晋升，建议启动评审流程"
elif score >= 75:
return "基本具备能力，需补足: " + ", ".join(gaps[:2])
else:
return "不建议近期晋升，需重点提升: " + ", ".join(gaps[:3])
# 使用示例
if __name__ == "__main__":
result = HuaweiDualTrackAssessment.assess_readiness(
"xuanyuan",
"T3",
"technical",
{"years": 4, "projects": 5, "values_score": 88}
)
print(f"轩辕T3→T4晋升准备度: {result['readiness_score']}分")
print(f"建议: {result['recommendation']}")
📋 校准会议脚本模板
# 校准会议议程 - {{quarter}}
## 参会人员
- 主持人: 天枢
- 评估方: 各直属上级
- 合规方: 明镜
- 记录: 稷下
## 会议流程 (120分钟)
### 1. 开场 (5分钟)
- 回顾上季度绩效概况
- 说明本次校准重点
### 2. Top 10%锚定 (20分钟)
逐一讨论Tier S候选，确认标准一致
| Agent | 初评分数 | 支持证据 | 争议点 | 最终判定 |
|-------|---------|---------|--------|---------|
| 烛龙 | 95 | 量化策略年化+45%，Agent绩效最高 | 无 | S |
| 轩辕 | 92 | RAG架构重构，技术债务清零 | 创新指数略低 | S |
### 3. Middle 80%校准 (60分钟)
分组讨论，重点聚焦争议Agent
争议Agent列表:
- [ ] 天工: 任务90但协作70？（派发任务难度大）
- [ ] 鲲鹏: 创新分高但价值观分不稳定？
### 4. Bottom 15%讨论 (20分钟)
需要PIP的Agent，确认改进计划可行性
| Agent | 分数 | 主要问题 | PIP方案 | 监督人 |
|-------|-----|---------|--------|-------|
| 麒麟 | 58 | 任务延期率35% | 降低任务量+结对辅导 | 鲲鹏 |
### 5. 强制分布复核 (10分钟)
确认各等级人数符合比例
| 等级 | 名额 | 实际 | 偏差 | 调整 |
|------|-----|-----|------|-----|
| S | 1 | 1 | 0 | - |
| A | 2 | 2 | 0 | - |
| B | 5 | 6 | +1 | 调整天工为B |
| C | 2 | 1 | -1 | 调整河图为C |
| D | 0 | 0 | 0 | - |
### 6. 总结与下一步 (5分钟)
- 稷下: 生成最终报告
- 明镜: 合规确认
- 天枢: 签批生效
📝 绩效改进计划（PIP）模板
# 绩效改进计划 (PIP)
**Agent**: {{agent_id}}
**评估周期**: {{quarter}}
**当前绩效等级**: {{current_grade}}
**目标绩效等级**: {{target_grade}}
**执行周期**: {{start_date}} - {{end_date}} (60天)
---
## 问题诊断
| 维度 | 当前分数 | 目标分数 | 差距分析 |
|------|---------|---------|---------|
| 任务 | {{task_score}} | {{target_task}} | {{task_gap}} |
| 协作 | {{collab_score}} | {{target_collab}} | {{collab_gap}} |
| 价值观 | {{values_score}} | {{target_values}} | {{values_gap}} |
---
## 改进目标（SMART）
### 目标1: {{goal_1}}
- Specific: {{specific_1}}
- Measurable: {{measurable_1}}
- Achievable: {{achievable_1}}
- Relevant: {{relevant_1}}
- Time-bound: {{timeline_1}}
### 目标2: {{goal_2}}
...
---
## 支持资源
- 指导人: {{mentor}}
- 每周检查会议: {{check_in_schedule}}
- 学习资源: {{learning_resources}}
---
## 阶段性检查点
| 周次 | 检查内容 | 达标标准 | 实际结果 |
|-----|---------|---------|---------|
| W2 | {{check_2}} | {{standard_2}} | {{result_2}} |
| W4 | {{check_4}} | {{standard_4}} | {{result_4}} |
| W6 | {{check_6}} | {{standard_6}} | {{result_6}} |
---
## 后果告知
- 若60天后仍未达标，将启动退役或再训练流程
- 每周内控会议，稷下+术士共同推进
📦 完整评估周期脚本
#!/bin/bash
# scripts/full_review_cycle.sh
QUARTER="$1"
EVAL_DIR="data/performance/${QUARTER}"
echo "=== 启动 ${QUARTER} 绩效评估周期 ==="
mkdir -p ${EVAL_DIR}
# Step 1: 数据采集
echo "[1/5] 采集Agent绩效数据..."
python3 scripts/collect_performance_data.py \
--quarter "${QUARTER}" \
--output "${EVAL_DIR}/raw_data.json"
# Step 2: 计算初评分数
echo "[2/5] 计算初评分数..."
python3 batch_evaluate.py \
--input "${EVAL_DIR}/raw_data.json" \
--output "${EVAL_DIR}/initial_scores.json"
# Step 3: 应用强制分布
echo "[3/5] 应用强制分布..."
python3 scripts/apply_forced_distribution.py \
--input "${EVAL_DIR}/initial_scores.json" \
--output "${EVAL_DIR}/calibrated_scores.json"
# Step 4: 生成校准材料
echo "[4/5] 生成校准会议材料..."
python3 scripts/generate_calibration_deck.py \
--scores "${EVAL_DIR}/calibrated_scores.json" \
--quarter "${QUARTER}" \
--output "${EVAL_DIR}/calibration_deck.pdf"
# Step 5: 生成个人报告
echo "[5/5] 生成个人绩效报告..."
python3 scripts/generate_individual_reports.py \
--scores "${EVAL_DIR}/calibrated_scores.json" \
--template "templates/performance_report.md" \
--output "${EVAL_DIR}/individual_reports/"
echo "=== 完成 ==="
echo "校准材料: ${EVAL_DIR}/calibration_deck.pdf"
echo "个人报告: ${EVAL_DIR}/individual_reports/"
✅ 质量检验清单
使用此Skill前必须确认：
- [ ] 此次评估周期有明确时间范围（如2026-Q2）
- [ ] 明镜的价值观测试数据已同步
- [ ] 跨域协作日志已通过sessions_send记录
- [ ] 强制分布比例测算完成
执行状态: ✅ 可运行（含完整算法 + SOP + 模板）
下一步: 配置数据源后运行 bash scripts/full_review_cycle.sh 2026-Q2