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/fund-screening/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/fund-screening)<a href="https://agentmods.dev/skills/belos-street/stock-analytics-skill/fund-screening"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/fund-screening/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/fund-screening"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/fund-screening.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 | $0.00060 | $0.02168 |
| Opus 5 | $0.00030 | $0.01084 |
| Sonnet 5 | $0.00012 | $0.00434 |
| Haiku 4.5 | $0.00006 | $0.00217 |
Grade A, and why
fund-screening 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
基金筛选与投资建议
技能核心定位
核心目标
为投资顾问生成专业的基金筛选、对比分析和个性化投资建议。支持根据风险偏好、投资目标、投资期限等多维度筛选基金，对比业绩、风险、持仓、基金经理等指标，生成结构化分析报告，提供配置建议和投资者画像匹配。
目标用户
- 新手投资者：选择首只基金，不知如何下手
- 有经验投资者：优化基金组合，寻找替代标的
- 主题投资者：特定行业主题投资需求
- 风险规避者：市场不确定性期间寻找稳健标的
技能边界
可提供服务：
- 多维度基金筛选（风险偏好、收益目标、期限）
- 基金业绩与风险指标分析
- 基金经理能力评估
- 基金持仓结构分析
- 投资者画像匹配
- 基金组合配置建议
不可提供服务：
- 具体买卖指令
- 承诺收益
- 预测基金净值
- 内幕信息
筛选维度
1. 风险偏好匹配
| 风险偏好 | 基金类型 | 配置比例建议 |
|---|---|---|
| 保守型 | 货币基金、纯债基金 | 固收100% |
| 稳健型 | 债券基金、混合债基、宽基ETF | 固收70%+权益30% |
| 平衡型 | 混合基金、宽基指数基金 | 固收50%+权益50% |
| 积极型 | 股票基金、行业主题基金 | 权益70%+固收30% |
| 激进型 | 行业主题基金、成长股基金 | 权益100% |
2. 投资期限匹配
| 期限 | 风险承受建议 | 基金类型 |
|---|---|---|
| 1年以内 | 低风险 | 货币基金、短债基金 |
| 1-3年 | 中低风险 | 纯债基金、混合债基 |
| 3-5年 | 中等风险 | 宽基指数、平衡混合 |
| 5年以上 | 中高风险 | 股票基金、行业主题 |
3. 收益目标匹配
| 收益目标 | 年化收益区间 | 基金类型 |
|---|---|---|
| 保本 | 2-3% | 货币基金 |
| 稳健增值 | 4-8% | 纯债基金、混合债基 |
| 均衡收益 | 8-12% | 宽基指数、平衡混合 |
| 较高收益 | 12-20% | 行业主题、成长股基 |
| 高收益 | 20%+ | 激进成长、行业集中 |
筛选指标体系
业绩指标
- 年化收益率（近1/3/5年）
- 累计收益率
- 相对业绩基准的超额收益
- 排名百分位
风险指标
- 最大回撤
- 年化波动率
- 夏普比率
- 卡玛比率
- 下行标准差
风险调整收益
- 夏普比率 > 1 为优秀
- 卡玛比率 > 2 为优秀
- 最大回撤 < 15% 为稳健
基金规模
- 规模太小心得流动性风险
- 规模太大难以灵活配置
- 建议规模：2亿-50亿
基金经理
- 从业年限
- 管理期间业绩
- 风格稳定性
- 离职率
筛选流程
第一步：确定筛选条件
输入示例：
- 风险偏好：平衡型
- 投资期限：3年
- 收益目标：年化8-12%
- 可承受最大回撤：20%
第二步：执行筛选
筛选条件组合：
- 基金类型：混合型、股票型
- 近3年年化收益 > 10%
- 最大回撤 < 20%
- 基金规模 > 2亿
- 基金经理从业 > 3年
第三步：业绩归因
- 分析收益来源：Alpha、贝塔、行业配置、个股选择
- 识别超额收益来源
- 评估收益可持续性
第四步：风险评估
- 分析回撤控制能力
- 评估波动率特征
- 压力测试
第五步：给出建议
- 推荐优先级排序
- 配置建议
- 买入时机建议
投资者画像匹配
画像维度
保守型投资者：
- 无法承受本金亏损
- 投资期限短（1-3年）
- 收益预期：跑赢通胀即可
- 建议配置：货币基金+纯债基金
稳健型投资者：
- 可承受5-10%短期亏损
- 投资期限中等（3-5年）
- 收益预期：年化5-10%
- 建议配置：固收+宽基ETF
平衡型投资者：
- 可承受10-20%短期亏损
- 投资期限较长（5年以上）
- 收益预期：年化10-15%
- 建议配置：宽基+行业ETF
积极型投资者：
- 可承受20%+短期亏损
- 投资期限长（7年以上）
- 收益预期：年化15%+
- 建议配置：行业主题+成长股基
报告输出格式
基金筛选报告
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 · 272 lines · 60 tokens per session scan A 8002c22966ec
fund-screening is a skill published in the GitHub repository belos-street/stock-analytics-skill (49 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 2,168 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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