Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/yuhao-corn/manufacturing-agents/managersgit clone --depth 1 https://github.com/YUHAO-corn/manufacturing-agentsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.04618 |
| Opus 5 | $0.00000 | $0.02309 |
| Sonnet 5 | $0.00000 | $0.00924 |
| Haiku 4.5 | $0.00000 | $0.00462 |
Grade A, and why
managers scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 571 lines — stays where its author put it; the contents beside it link to each section on GitHub.
管理层智能体
概述
管理层智能体是 TradingAgents 框架的协调和决策中枢,负责统筹各个专业团队的工作,确保整个投资决策流程的有序进行。管理层包括研究经理和风险经理,分别负责研究活动的协调和风险管理的统筹。
管理层架构
管理层体系设计
class ManagementLayer:
"""管理层系统 - 统筹协调各专业团队"""
def __init__(self, config):
self.config = config
self.research_manager = ResearchManager(config)
self.risk_manager = RiskManager(config)
self.decision_coordinator = DecisionCoordinator(config)
def orchestrate_decision_process(self, analysis_data: Dict) -> Dict:
"""协调整个决策流程"""
# 1. 研究阶段管理
research_results = self.research_manager.manage_research_process(analysis_data)
# 2. 风险评估管理
risk_assessment = self.risk_manager.manage_risk_assessment(
research_results, analysis_data
)
# 3. 决策协调
final_decision = self.decision_coordinator.coordinate_final_decision(
research_results, risk_assessment
)
# 4. 质量控制
quality_check = self._conduct_quality_control(final_decision)
return {
"research_results": research_results,
"risk_assessment": risk_assessment,
"final_decision": final_decision,
"quality_check": quality_check,
"management_approval": self._provide_management_approval(final_decision, quality_check)
}
1. 研究经理 (Research Manager)
职责与功能
class ResearchManager:
"""研究经理 - 协调和管理研究活动"""
def __init__(self, config):
self.config = config
self.research_standards = ResearchStandards()
self.quality_controller = ResearchQualityController()
self.debate_moderator = DebateModerator()
核心职责:
- 协调分析师和研究员工作
- 主持研究员辩论
- 确保研究质量
- 形成研究共识
- 管理研究流程
管理范围:
- 分析师团队协调
- 研究员辩论管理
- 研究质量控制
- 共识形成机制
- 研究效率优化
研究流程管理
def manage_research_process(self, analysis_data: Dict) -> Dict:
"""管理研究流程"""
# 1. 分析师工作协调
analyst_coordination = self._coordinate_analyst_work(analysis_data)
# 2. 分析质量检查
analysis_quality = self._check_analysis_quality(analyst_coordination)
# 3. 研究员辩论管理
debate_results = self._manage_researcher_debate(analyst_coordination)
# 4. 共识形成
research_consensus = self._facilitate_consensus_formation(debate_results)
# 5. 研究报告生成
research_report = self._generate_research_report(
analyst_coordination, debate_results, research_consensus
)
return {
"analyst_coordination": analyst_coordination,
"analysis_quality": analysis_quality,
"debate_results": debate_results,
"research_consensus": research_consensus,
"research_report": research_report,
"research_confidence": self._assess_research_confidence(research_consensus)
}
def _coordinate_analyst_work(self, analysis_data: Dict) -> Dict:
"""协调分析师工作"""
analyst_reports = analysis_data.get("analyst_reports", {})
coordination_results = {
"coverage_completeness": self._assess_coverage_completeness(analyst_reports),
"analysis_consistency": self._check_analysis_consistency(analyst_reports),
"data_quality": self._evaluate_data_quality(analyst_reports),
"methodology_alignment": self._check_methodology_alignment(analyst_reports)
}
# 识别需要补充的分析
missing_analysis = self._identify_missing_analysis(analyst_reports)
# 协调补充分析
if missing_analysis:
coordination_results["supplementary_analysis"] = self._request_supplementary_analysis(
missing_analysis
)
# 分析师工作评估
coordination_results["analyst_performance"] = self._evaluate_analyst_performance(
analyst_reports
)
return coordination_results
def _manage_researcher_debate(self, analyst_coordination: Dict) -> Dict:
"""管理研究员辩论"""
# 设置辩论议题
debate_agenda = self._set_debate_agenda(analyst_coordination)
# 分配辩论角色
debate_roles = self._assign_debate_roles()
# 主持辩论过程
debate_process = self._moderate_debate_process(debate_agenda, debate_roles)
# 评估辩论质量
debate_quality = self._assess_debate_quality(debate_process)
# 提取关键争议点
key_disagreements = self._extract_key_disagreements(debate_process)
return {
"debate_agenda": debate_agenda,
"debate_roles": debate_roles,
"debate_process": debate_process,
"debate_quality": debate_quality,
"key_disagreements": key_disagreements,
"debate_effectiveness": self._measure_debate_effectiveness(debate_process)
}
def _facilitate_consensus_formation(self, debate_results: Dict) -> Dict:
"""促进共识形成"""
# 分析辩论结果
debate_analysis = self._analyze_debate_outcomes(debate_results)
# 识别共同点
common_ground = self._identify_common_ground(debate_analysis)
# 调解分歧
mediated_disagreements = self._mediate_disagreements(
debate_results["key_disagreements"]
)
# 权衡不同观点
balanced_perspective = self._create_balanced_perspective(
common_ground, mediated_disagreements
)
# 形成最终共识
final_consensus = self._formulate_final_consensus(balanced_perspective)
return {
"common_ground": common_ground,
"mediated_disagreements": mediated_disagreements,
"balanced_perspective": balanced_perspective,
"final_consensus": final_consensus,
"consensus_strength": self._measure_consensus_strength(final_consensus),
"remaining_uncertainties": self._identify_remaining_uncertainties(final_consensus)
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 571 lines · 0 tokens per session scan A d67335964093
managers is an agent published in the GitHub repository YUHAO-corn/manufacturing-agents (171 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 4,618 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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