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 skills add duolongworld/AI_Renaissance --skill industry_sentiment_trackergit clone --depth 1 https://github.com/duolongworld/AI_RenaissanceWrote 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/skills/duolongworld/ai_renaissance/industry_sentiment_tracker)<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/industry_sentiment_tracker"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/industry_sentiment_tracker/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/industry_sentiment_tracker"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/industry_sentiment_tracker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00058 | $0.02113 |
| Opus 5 | $0.00029 | $0.01056 |
| Sonnet 5 | $0.00012 | $0.00423 |
| Haiku 4.5 | $0.00006 | $0.00211 |
Grade A, and why
industry-sentiment-tracker 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 12d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
行业景气热度计 Skill
1. 适用范围
所属小组：专家6组（舆情）
适用任务：
- 判断个股所属行业板块的景气冷热程度，输出 0-100 行业温度
- 识别行业所处的景气阶段（冰点→沸点 7 档），给出行业仓位参考
- 为个股舆情分析提供行业背景参照（"中气候"）
- 检测行业特殊信号（龙头异动、资金大幅流入/流出、板块轮动等）
- 适用于东方财富行业板块分类下的所有 A 股行业板块
边界说明：
- 本 Skill 产出的是行业情绪面辅助信号，不单独构成交易建议
- 行业景气不等于个股景气，个股可能与行业走势背离
- 行业板块划分依据东方财富分类，可能与申万/中信分类有差异
- 数据源存在延迟，需在
meta.uncertainties说明
2. 输入材料
数据来源
本 Skill 为分析层，消费由数据源提供的结构化行业数据：
| 数据接口 | 执行数据源 | 提供内容 |
|---|---|---|
| AKShare 行业板块接口 | data_sources/industry_sentiment.py |
板块涨跌比、资金流、换手率、涨跌停、涨幅排名等 |
必填输入（自动采集）
- 行业板块归属：通过个股代码查询其所属行业板块名称
- 板块内涨跌数据：板块内上涨/下跌家数占比
- 板块资金流数据：板块当日主力资金净流入额
- 板块换手率数据：板块当日换手率
- 板块涨幅排名：板块在全行业中的涨跌幅排名百分位
可选输入
- 板块内涨跌停数据
- 板块龙头股走势
- 板块近 5 日/20 日资金流趋势
缺失处理
- 如果无法解析个股所属行业板块（如退市股、新股未入板块），输出
status: "error"，meta.industry = None，不阻塞后续个股分析 - 如果部分指标缺失，
confidence降低，权重自动归一化到可用指标 - 如果板块成分股数据获取超时，使用简化方案（仅用板块行情数据推算）
3. 分析步骤
-
解析行业归属：通过
data_sources/industry_sentiment.py将 stock_code 映射到行业板块名称 -
采集行业数据：获取板块成分股、资金流、行情数据
-
逐指标标准化：将每个原始指标映射到 0-100 分
- 板块涨跌比（breadth）：板块内上涨家数占比 × 100
- 板块资金流（fund_flow）：(50 + 净流入/标准化因子) 限制在 [0, 100]
- 板块换手率异动（turnover）：(50 + Z-score×16.7) 限制在 [0, 100]
- 板块涨跌停比（limit_ratio）：板块内涨停数/(涨停+跌停) × 100
- 板块涨幅排名（rank）：百分位排名 × 100
-
加权综合评分：按权重计算行业景气温度
指标 权重 板块涨跌比 0.25 板块资金流 0.25 板块换手率异动 0.20 板块涨跌停比 0.15 板块涨幅排名 0.15 - 可用指标权重自动归一化
-
判断景气阶段：根据综合温度匹配 7 档阶段
-
检测特殊信号：龙头异动、资金大幅流入/流出、板块轮动等
-
输出标准 JSON
4. 判断规则
4.1 景气阶段判定
| 温度区间 | 阶段 | direction | 说明 |
|---|---|---|---|
| 0-20 | 行业冰点 | bullish |
行业极度低迷，逆向关注低估值龙头 |
| 21-35 | 行业偏冷 | bullish |
行业景气偏低，关注政策催化 |
| 36-45 | 偏冷中性 | neutral |
行业景气一般，等待信号 |
| 46-54 | 中性均衡 | neutral |
行业景气中性，精选个股 |
| 55-65 | 偏热中性 | neutral |
行业景气偏暖，注意追高风险 |
| 66-80 | 行业偏热 | bearish |
行业过热，控制仓位 |
| 81-100 | 行业沸点 | bearish |
行业极度亢奋，警惕回调 |
4.2 置信度规则
- 0.7-1.0：行业景气极端明确（温度<20或>80），且特殊信号支持
- 0.5-0.7：行业景气有倾向（温度<35或>65），或有 1 个特殊信号
- 0.3-0.5：行业景气有倾向但不强烈
- <0.3：数据不足或指标矛盾
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.
- 12d ago First seen · 186 lines · 58 tokens per session scan A f9bc2e196689
industry-sentiment-tracker is a skill published in the GitHub repository duolongworld/AI_Renaissance (59 stars, last pushed 15d ago), licensed Apache-2.0. It adds 58 tokens to every session and 2,113 once invoked, about $0.0003 per session on Opus 5. 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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