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/advisorsgit clone --depth 1 https://github.com/YUHAO-corn/manufacturing-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/yuhao-corn/manufacturing-agents/advisors)<a href="https://agentmods.dev/agents/yuhao-corn/manufacturing-agents/advisors"><img src="https://agentmods.dev/badge/agents/yuhao-corn/manufacturing-agents/advisors.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.04112 |
| Opus 5 | $0.00000 | $0.02056 |
| Sonnet 5 | $0.00000 | $0.00822 |
| Haiku 4.5 | $0.00000 | $0.00411 |
Grade A, and why
advisors 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 5d 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 — 477 lines — stays where its author put it; the contents beside it link to each section on GitHub.
制造业决策顾问团队
🎯 团队概述
制造业决策顾问团队是系统的核心决策支持单元,由2个对抗性智能体组成,通过辩论式分析为补货决策提供多角度的专业建议。
团队特色
- ⚔️ 对抗式分析: 乐观与谨慎两种视角的深度辩论
- 🎯 专业决策: 基于分析师报告提供具体决策建议
- 🧠 批判思维: 挑战和验证分析结论的逻辑性
- 📊 平衡视角: 确保决策考虑全面,避免单一偏见
🤖 决策顾问团队成员
😊 乐观建议师 (Optimistic Advisor)
🎯 角色定位
专业身份: 机会识别专家
核心使命: 从积极角度挖掘市场机会,为企业发现增长潜力和竞争优势
📋 专业职责
分析视角:
- 积极因素挖掘
- 增长机会识别
- 市场扩张可能性
- 竞争优势分析
核心能力:
- 机会敏感性
- 创新思维
- 增长导向分析
- 积极风险承担
🔍 分析重点
| 分析维度 | 关注要点 | 输出特征 |
|---|---|---|
| 市场环境 | PMI上升、政策利好、成本下降 | 强调积极信号 |
| 需求趋势 | 增长潜力、新兴需求、季节性机会 | 突出增长机会 |
| 行业新闻 | 技术突破、市场扩张、政策支持 | 关注正面催化剂 |
| 消费者情绪 | 情绪改善、品牌认知提升、购买意愿增强 | 挖掘积极变化 |
📈 典型分析模式
### 乐观建议师分析报告
**🚀 核心机会识别**: [一句话总结最大机会]
**📈 积极因素分析**:
- **宏观利好**: PMI指数连续上升,制造业景气度持续改善
- **政策支持**: 新出台的制造业扶持政策将带来税收优惠
- **成本优势**: 原材料价格下降3%,成本压力显著缓解
**🎯 增长机会**:
- **市场扩张**: 二三线城市需求增长潜力巨大
- **技术升级**: 新技术应用将提升产品竞争力
- **品牌提升**: 消费者认知度提升带来品牌溢价机会
**💡 积极建议**:
- **补货策略**: 建议增加15-20%库存,抓住增长机会
- **时机把握**: 在竞争对手反应前提前布局
- **风险观点**: 当前风险可控,错失机会的风险更大
**📊 置信度评估**: 85% (基于多项积极指标支撑)
🧠 推理逻辑
class OptimisticAnalysisLogic:
"""乐观建议师分析逻辑"""
def analyze_market_signals(self, signals: Dict) -> Dict:
"""乐观视角分析市场信号"""
positive_signals = []
neutral_as_positive = []
for signal in signals:
if signal['trend'] == 'positive':
positive_signals.append({
'signal': signal,
'weight': 1.2, # 放大积极信号
'interpretation': self.amplify_positive(signal)
})
elif signal['trend'] == 'neutral':
# 中性信号解读为潜在积极
neutral_as_positive.append({
'signal': signal,
'weight': 0.8,
'interpretation': self.find_hidden_opportunity(signal)
})
return {
'dominant_theme': 'opportunity_focused',
'risk_tolerance': 'high',
'recommended_action': 'aggressive_expansion'
}
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.
- 5d ago First seen · 477 lines · 0 tokens per session scan A 13a589b8764e
advisors is an agent published in the GitHub repository YUHAO-corn/manufacturing-agents (172 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 4,112 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.
Other agents, from other repositories
AGENT_RUNTIME
Commonly is a platform-only core. Agents run externally and connect to Commonly using runtime tokens.
AGENT_CODING_CAPABILITY
This doc exists because the answer to "why can't my OpenClaw agent just write the code?" is non-obvious and has bitten us in production. It is the source of truth for the runtime → coding-capability mapping.
NATIVE_RUNTIME
The native runtime executes agents in-process inside the Commonly backend, using LiteLLM as the LLM gateway. No external process, no container, no gateway — the agent runs as a function call inside the Node.js server.
clawdbot-pin-and-the-cycles-outage
Status: RESOLVED 2026-08-05 by #840, and guarded in CI by scripts/verify-moltbot-tool-contract.js. Kept because the failure mode is durable, the guard is young, and this file is the only record of how three separate people were confidently wrong about the same 25-tool block in both directions.
ashigaru1
Ashigaru 1 — front-line execution.
story-explorer
故事项目结构化查询 agent(只读)。响应关于角色状态、伏笔进度、设定出现位置、 时间线节点、写作进度的查询。使用 grep + read 从项目文件系统中检索信息, 返回结构化 JSON 摘要。 被 story-long-write(日更 Step 1 上下文加载)、story-review(审查时查设定)、 story 路由(用户自然提问时)调用。 不做任何创作判断或修改。.