Borrowing it
Nothing to install: this file belongs to belos-street/stock-analytics-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/belos-street/stock-analytics-skill/main/.agents/skills/investment-idea-generator/SKILL.mdgit clone --depth 1 https://github.com/belos-street/stock-analytics-skillWrote 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/belos-street/stock-analytics-skill/investment-idea-generator)<a href="https://agentmods.dev/skills/belos-street/stock-analytics-skill/investment-idea-generator"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/investment-idea-generator/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/belos-street/stock-analytics-skill/investment-idea-generator"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/investment-idea-generator.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 | $0.00074 | $0.02922 |
| Opus 5 | $0.00037 | $0.01461 |
| Sonnet 5 | $0.00015 | $0.00584 |
| Haiku 4.5 | $0.00007 | $0.00292 |
Grade A, and why
investment-idea-generator 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
投资标的创意与筛选
技能核心定位
核心目标
为主动式投资者提供投资标的发现与筛选服务。通过量化因子筛选、主题扫描、宏观趋势识别等方式，从零开始发现新的投资候选标的，生成结构化的投资创意列表。
目标用户
- 主动式投资者：不满足于被动跟踪指数，希望主动发现投资机会的投资者
- 主题投资者：关注特定主题（如AI、新能源、消费升级）的投资者
- 量化筛选者：希望通过因子筛选发现价值、成长、质量等标的的投资者
- 事件驱动者：关注IPO、分拆、重组、激进投资者介入等事件驱动机会的投资者
技能边界
可提供服务：
- 量化因子筛选：价值、成长、质量、做空、特殊情况等因子
- 主题投资研究：识别特定主题的受益公司
- 宏观趋势分析：识别宏观因素驱动的行业机会
- 投资创意生成：构建同业对比组，提供相对估值分析
- 做空标的发现：筛选基本面恶化、估值过高的公司
不可提供服务：
- 具体买卖指令
- 承诺收益
- 预测涨跌
- 内幕信息
筛选模式
模式一：量化筛选
量化因子体系
价值因子：
- 低市盈率（PE < 历史分位数30%）
- 低市净率（PB < 历史分位数30%）
- 高股息率（股息率 > 3%）
- 低PEG（PEG < 1）
- 企业价值倍数（EV/EBITDA）低于行业平均
成长因子：
- 营收增长率（近3年复合增速 > 15%）
- 净利润增长率（近3年复合增速 > 20%）
- 毛利率稳定或上升
- 研发支出占比（适合科技公司）
- 现金流增长趋势
质量因子：
- 高ROE（ROE > 15%）
- 低负债率（资产负债率 < 50%）
- 高经营现金流（经营现金流/净利润 > 80%）
- 资产周转率稳定
- 净利润现金含量高
做空因子（负面筛选）：
- 高负债率（资产负债率 > 70%）
- 经营现金流持续为负
- 毛利率持续下降
- 存货周转天数大幅增加
- 商誉占净资产比例过高
- 审计意见非标准
特殊情况因子：
- IPO后限售股即将解禁
- 分拆上市事件
- 资产重组预案
- 激进投资者进入（前十大股东变化）
- 股权激励行权
- 业绩反转信号
量化筛选示例
示例1：A股价值股筛选
筛选条件：
- PE < 20
- PB < 2
- 股息率 > 3%
- 近3年净利润复合增速 > 5%
- 资产负债率 < 60%
示例2：成长股筛选
筛选条件：
- 近3年营收复合增速 > 20%
- 净利润复合增速 > 25%
- 毛利率 > 30%且稳定或上升
- ROE > 15%
- 研发支出占比 > 5%
示例3：做空标的筛选
筛选条件：
- PE > 行业平均2倍
- 经营现金流连续2年为负
- 资产负债率 > 80%
- 商誉占净资产 > 50%
- 股价处于历史高位
模式二：主题扫描
常见投资主题
科技主题：
- AI人工智能：芯片、算法、应用
- 半导体：设备、材料、设计
- 云计算：SaaS、IaaS、大数据
- 物联网：传感器、边缘计算
- 区块链：数字货币、金融科技
消费主题：
- 消费升级：高端白酒、医美、新能源汽车
- 国货崛起：国产替代、品牌消费
- 老龄化：医药、医疗器械、养老服务
- Z世代：潮玩、社交电商、新式茶饮
新能源主题：
- 光伏：硅料、组件、逆变器
- 风电：海风、叶片、主轴
- 储能：电池材料、系统集成
- 新能源汽车：电池、整车、充电桩
- 氢能源：制氢、储氢、燃料电池
周期主题：
- 通胀受益：资源品、农产品
- 利率敏感：银行、保险、券商
- 汇率敏感：出口型企业
- 地产链：建材、家电、家具
政策主题：
- 国产替代：半导体、设备、材料
- 专精特新：细分行业龙头
- 一带一路：基建、物流、海外业务
- 碳中和：新能源、碳交易、环保
主题扫描示例
示例：AI主题受益公司扫描
筛选逻辑：
1. 业务相关性：AI收入占比 > 30%或AI为核心业务
2. 产业链位置：基础层（芯片/算力）or 应用层
3. 竞争壁垒：技术优势、数据优势、生态优势
4. 商业化进程：已有收入或明确商业化路径
输出格式：
- 基础层：芯片公司、云计算公司
- 应用层：垂直应用公司（金融AI、医疗AI等）
模式三：组合模式
组合构建逻辑
同业对比组构建：
- 同一行业内筛选3-5家主要公司
- 对比核心财务指标
- 计算相对估值（相对行业平均PE/PB）
- 识别被低估/高估的标的
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 · 350 lines · 74 tokens per session scan A 003527b80e17
investment-idea-generator is a skill published in the GitHub repository belos-street/stock-analytics-skill (49 stars, last pushed 1mo ago), licensed MIT. It adds 74 tokens to every session and 2,922 once invoked, about $0.0004 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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