Stock Deep Analysis Workflow
wbh604/UZI-Skill
Runs a staged deep analysis of a single stock on China A-share, Hong Kong and US markets, ending in an HTML report with valuation models and investor-panel scores.
Evaluates mutual funds, private funds and ETFs by return, risk and risk-adjusted metrics, style box and drift, and manager quality, then builds FOF portfolios; Chinese text.
SKILL.md written in Chinese; this summary is our English description.
$ npx skills add HKUDS/Vibe-Trading --skill fund-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install HKUDS/Vibe-Trading fund-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent/src/skills/fund-analysis .claude/skills/fund-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "fund-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/fund-analysis into .claude/skills/fund-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fund-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/fund-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add HKUDS/Vibe-Trading --skill fund-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install HKUDS/Vibe-Trading fund-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .agents/skills && cp -r skills-src/agent/src/skills/fund-analysis .agents/skills/fund-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "fund-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/fund-analysis into .agents/skills/fund-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fund-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add HKUDS/Vibe-Trading --skill fund-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install HKUDS/Vibe-Trading fund-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/agent/src/skills/fund-analysis .cursor/skills/fund-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "fund-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/fund-analysis into .cursor/skills/fund-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fund-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/HKUDS/Vibe-Trading.git --path agent/src/skills/fund-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add HKUDS/Vibe-Trading --skill fund-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install HKUDS/Vibe-Trading fund-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/agent/src/skills/fund-analysis .gemini/skills/fund-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "fund-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/fund-analysis into .gemini/skills/fund-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fund-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install HKUDS/Vibe-Trading fund-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add HKUDS/Vibe-Trading --skill fund-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .github/skills && cp -r skills-src/agent/src/skills/fund-analysis .github/skills/fund-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "fund-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/fund-analysis into .github/skills/fund-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fund-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add HKUDS/Vibe-Trading --skill fund-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install HKUDS/Vibe-Trading fund-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/agent/src/skills/fund-analysis .opencode/skills/fund-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "fund-analysis" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/fund-analysis into .opencode/skills/fund-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fund-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
fund-analysisEvaluates mutual funds, private funds and ETFs by return, risk and risk-adjusted metrics, style box and drift, and manager quality, then builds FOF portfolios; Chinese text.
The aim is to find a sustainable source of excess return rather than simply the fund with the best past performance. The skill lists return metrics, risk metrics and risk-adjusted ratios such as Sharpe, Sortino, Treynor and Calmar with thresholds for an excellent fund, sets out benchmarks for equity and hybrid funds, and recommends an evaluation period of at least three years. Style analysis uses a nine-cell Sharpe style box, and drift is detected by rolling-window regression. The instructions are written in Chinese.
A five-step screening framework starts from hard filters such as fund age and size, followed by criteria for judging fund managers, an ETF selection framework and FOF construction with sample allocations for conservative and balanced profiles. Caveats include survivorship bias, scale effects, quarter-end window dressing, subscription and redemption impact, fee drag and so-called index enhancement funds that are really active. The code needs pandas, numpy and scipy.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 14cabaf. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
pipFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Fund Analysis and FOF Screening loads about 1.2k tokens when it runs. Until then it costs about 19 tokens; SKILL.md has 107 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from HKUDS/Vibe-Trading at commit 14cabaf, republished under its MIT licence (© HKUDS). 107 words, ~1,234 tokens.
.claude/skills/fund-analysis/SKILL.md (or your agent's skills folder).系统化评估公募基金/私募基金/ETF的业绩表现、投资风格和管理能力,并构建FOF(基金中的基金)组合。核心目标:找到"可持续的超额收益来源"而非"过去业绩最好的基金"。
适用场景:
收益类指标:
| 指标 | 公式 | 优秀阈值 | 说明 |
|---|---|---|---|
| 年化收益率 | (1+总收益)^(1/年数)-1 | > 15% (股基) | 绝对收益 |
| 超额收益(Alpha) | 基金收益-基准收益 | > 5%/年 | 相对基准 |
| 信息比率(IR) | Alpha / 跟踪误差 | > 0.5 | Alpha稳定性 |
| 胜率 | 跑赢基准的月份占比 | > 55% | 一致性 |
风险类指标:
| 指标 | 公式 | 优秀阈值 | 说明 |
|---|---|---|---|
| 最大回撤 | max(peak-trough)/peak | < 20% (股基) | 极端风险 |
| 年化波动率 | std(日收益)*√252 | < 20% (股基) | 总风险 |
| 下行标准差 | std(负收益)*√252 | < 13% | 下行风险 |
| Calmar比率 | 年化收益/最大回撤 | > 1.0 | 收益/极端风险 |
风险调整指标:
| 指标 | 公式 | 优秀阈值 | 说明 |
|---|---|---|---|
| 夏普比率 | (Rp-Rf)/σp | > 1.0 | 每单位风险收益 |
| Sortino比率 | (Rp-Rf)/下行σ | > 1.5 | 更关注下行风险 |
| Treynor比率 | (Rp-Rf)/β | > 10% | 每单位系统风险收益 |
无风险利率(Rf): A股通常用1年期国债收益率, 约2.0-2.5%
基准: 股票型→沪深300; 混合型→沪深300×60%+中证全债×40%
评估周期: 至少3年,推荐5年(覆盖完整牛熊周期)九宫格风格分类:
价值 平衡 成长
大盘 大盘价值 大盘平衡 大盘成长
中盘 中盘价值 中盘平衡 中盘成长
小盘 小盘价值 小盘平衡 小盘成长
判定方法(回归法):
Ri = α + β1×大盘价值 + β2×大盘成长 + β3×小盘价值 + β4×小盘成长 + ε
风格指数选择(A股):
大盘价值: 沪深300价值 (399346)
大盘成长: 沪深300成长 (399370)
小盘价值: 中证500价值 (930782)
小盘成长: 中证500成长 (930783)
β权重最大的方向 = 基金主风格
R² > 0.85 → 风格明确; R² < 0.70 → 风格模糊/择时型方法: 滚动窗口回归 (窗口=60个交易日, 步长=20日)
漂移判定:
1. 计算每个窗口的风格权重β
2. 相邻窗口β变化:
|Δβ| > 0.2 → 显著漂移
最大β对应的风格变了 → 风格切换
3. R²时序:
R²持续下降 → 基金经理在做择时/偏离基准
R²忽高忽低 → 风格不稳定
漂移类型:
- 渐进漂移: 大盘→中盘→小盘 (通常是规模增长后被迫下沉)
- 突变漂移: 价值突然切换成长 (可能换了基金经理)
- 周期漂移: 牛市追成长、熊市转价值 (择时型)
A股常见漂移:
2020-2021: 大量"价值型"基金实际持仓转向新能源/半导体(成长)
检测: 申报风格=大盘价值, 实际回归风格=大盘成长 → 名不副实Step 1: 硬指标过滤
□ 成立 ≥ 3年
□ 规模 2-100亿(太小清盘风险, 太大船大难掉头)
□ 同一基金经理管理 ≥ 2年
□ 机构持有比例 > 20%(机构认可)
Step 2: 绩效排序
□ 近3年年化收益 > 同类中位数
□ 近3年夏普比率 > 同类前30%
□ 最大回撤 < 同类中位数
□ 信息比率 > 0.3
Step 3: 风格验证
□ 实际风格与申报风格一致(R² > 0.8)
□ 风格漂移得分 < 0.3(稳定)
□ 近1年风格与近3年一致
Step 4: 基金经理评价
□ 管理同类基金 ≥ 3年
□ 历史任职基金收益均为正超额
□ 换手率合理(年化200-400%为正常, >600%过高)
□ 持股集中度适中(前10大持仓40-70%)
Step 5: 费用检查
□ 管理费 ≤ 1.5%(主动股基)
□ 无惩罚性赎回费(持有>1年免赎回费)
□ 托管费 ≤ 0.25%核心维度:
1. 超额收益能力:
任职年化Alpha (相对基准)
牛市Alpha vs 熊市Alpha (优秀经理熊市也有超额)
2. 风险控制:
最大回撤 vs 基准最大回撤
下行捕获比率 < 0.8 → 善于控制下行风险
上行捕获比率 > 1.0 → 上涨行情不掉队
3. 选股能力 vs 择时能力 (T-M模型):
Ri-Rf = α + β(Rm-Rf) + γ(Rm-Rf)² + ε
α > 0 → 有选股能力
γ > 0 → 有择时能力
A股实证: 大部分基金经理有选股能力, 少有择时能力
4. 持仓特征:
换手率: <200%=长期持有; 200-400%=适中; >600%=频繁交易
持股集中度: 前10大占比, >70%=集中, <40%=分散
行业偏离度: 相对基准的行业超/低配幅度
经理更换信号:
基金经理变更公告日起:
- 新经理来自同一公司、风格相近 → 影响小
- 新经理风格截然不同 → 重新评估, 观察1-2个季度再决定
- 明星经理离职 → 考虑赎回, 跟踪新经理的其他产品核心标准:
1. 跟踪误差: 年化 < 2% (被动) / < 4% (增强)
计算: std(ETF日收益 - 指数日收益) × √252
2. 费率比较:
管理费: 0.15%(最低) ~ 0.50%(普通)
托管费: 0.05% ~ 0.10%
综合费率差 0.2%/年,10年累积差异显著
3. 流动性:
日均成交额 > 1亿 → 流动性充足
买卖价差 < 0.1% → 交易成本低
折溢价率 < 0.3% → 定价准确
4. 规模:
> 10亿 → 清盘风险极低
2-10亿 → 可接受
< 2亿 → 需关注是否有清盘风险
A股主流宽基ETF对比(示例):
| ETF | 代码 | 费率 | 规模 | 跟踪误差 |
|-----|------|------|------|----------|
| 华泰柏瑞沪深300ETF | 510300 | 0.20% | 800亿+ | 0.5% |
| 易方达沪深300ETF | 510310 | 0.20% | 200亿+ | 0.6% |
| 华夏上证50ETF | 510050 | 0.50% | 500亿+ | 0.4% |
| 南方中证500ETF | 510500 | 0.20% | 400亿+ | 0.8% |Step 1: 大类资产配置
保守型: 股基30% + 债基50% + 货基20%
均衡型: 股基50% + 债基30% + 商品10% + 货基10%
激进型: 股基70% + 债基20% + 商品10%
Step 2: 细分资产选基
每个资产类别选 2-3 只基金(分散管理人风险)
股票部分:
大盘价值 1只 + 大盘成长 1只 + 中小盘 1只
风格互补, 降低单一风格暴露
债券部分:
纯债 1只 + 转债增强 1只
控制信用风险, 不追高收益债
Step 3: 再平衡规则
定期: 每季度检查一次偏离度
触发: 任一资产偏离目标权重 > 5% → 再平衡
再平衡方法:
a. 卖出超配、买入低配 → 交易成本高
b. 增量资金买入低配 → 减少交易频率
c. 分红再投资到低配 → 最优方案
Step 4: 监控预警
□ 季度绩效回顾: 任一基金连续2个季度排名后30% → 观察
□ 基金经理变更 → 重新评估
□ 风格漂移 → 替换为风格稳定的同类基金
□ 规模异常(暴增/暴降) → 关注流动性冲击基金分析报告:
=== 基金概况 ===
名称: 易方达蓝筹精选混合 (005827)
经理: 张坤 任职: 2018-01-05 (8年)
规模: 450亿 风格: 大盘成长
=== 绩效评估 (近3年) ===
年化收益: 12.5% (同类前25%)
夏普比率: 0.85 (同类前20%)
最大回撤: -28.3% (同类中位-25.6%)
信息比率: 0.62
Calmar比率: 0.44
胜率: 58% (月度跑赢基准)
=== 风格分析 ===
回归风格: 大盘成长 (R²=0.91)
风格漂移: 低 (近1年与近3年一致)
持股集中度: 前10大持仓68%
换手率: 年化150% (低换手, 长期持有)
=== 评价 ===
优势: 选股能力强(Alpha显著), 风格稳定
劣势: 规模过大可能影响操作灵活性, 回撤控制一般
建议: 适合作为FOF组合中的大盘成长配置, 仓位15-20%pip install pandas numpy scipy© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in agent/src/skills/fund-analysis of HKUDS/Vibe-Trading.
Open the folder on GitHubat commit 14cabaf
Fund Analysis and FOF Screening next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Fund Analysis and FOF Screening this skillHKUDS/Vibe-Trading | 35k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Stock Deep Analysis Workflowwbh604/UZI-Skill | 7.1k | — | ~9.1k | Automated safety check: Notes | MIT | |
| Tushare Datazillionare/zillionare | 319 | 2 repos | ~2.3k | Automated safety check: Pass | None | |
| Three-Statement Model Builderginlix-ai/LangAlpha | 1.8k | — | ~5.4k | Automated safety check: Pass | Apache-2.0 | |
| Quant Blog Writingzillionare/zillionare | 319 | — | ~895 | Automated safety check: Pass | None | |
| Insurance Operations Analysiszj-unicom-ai/UniEmployee | 358 | — | ~646 | Automated safety check: Pass | MIT |
wbh604/UZI-Skill
Runs a staged deep analysis of a single stock on China A-share, Hong Kong and US markets, ending in an HTML report with valuation models and investor-panel scores.
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求,转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
ginlix-ai/LangAlpha
Builds or repairs an integrated income statement, balance sheet and cash flow model in Excel with live formulas, supporting schedules, scenarios and a Checks sheet.
zillionare/zillionare
撰写文笔精炼、富有深度的量化交易博文,论点清晰、证据确凿、叙事层次更加丰富。适用于量化交易博文、因子研究、回测复盘、数据源排查、市场微观结构、策略原理、风险控制、职业观察、量化人物故事等选题。文章将聚焦具体角度,提供详实的大纲、证据规划及成稿,力求内容兼具思想深度与诚实性,而非单纯口号式宣传;同时,通过人物经历、引言、贡献及行业背景的融入,让文章更具可读性和吸引力。
zj-unicom-ai/UniEmployee
Analyzes insurance operating data such as premium, loss ratio, renewal rate and expense ratio by branch, product and channel, flags anomalies and builds an HTML dashboard.
majiayu000/claude-skill-registry
Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.
HKUDS/Vibe-Trading
Index of Eastmoney's free, no-token market data interfaces for China A-shares and Hong Kong stocks: fund flows, dragon-tiger lists, margin trading, reports and news.
HKUDS/Vibe-Trading
Retrieves public OKX cryptocurrency market data such as spot prices, candlesticks, funding rates and open interest through the OKX V5 REST API, with no authentication.
HKUDS/Vibe-Trading
Fetches U.S. SEC EDGAR data: resolves tickers to CIK numbers, lists recent 10-K, 10-Q and 8-K filings with document URLs, and pulls XBRL financial series.
HKUDS/Vibe-Trading
Predicts whether a mainland China A-share company risks an ST or *ST warning after its next annual report, using financial thresholds and Sina penalty records.
HKUDS/Vibe-Trading
Breaks a structural trend such as AI infrastructure into its physical supply chain and ranks lesser-known listed companies sitting on each bottleneck.
HKUDS/Vibe-Trading
Plans and drafts an eight-part, roughly 120k-word investigative series on one company, built around a strict fact-check pass rather than fast drafting.
Categories
Evaluates mutual funds, private funds and ETFs by return, risk and risk-adjusted metrics, style box and drift, and manager quality, then builds FOF portfolios; Chinese text. The aim is to find a sustainable source of excess return rather than simply the fund with the best past performance. The skill lists return metrics, risk metrics and risk-adjusted ratios such as Sharpe, Sortino, Treynor and Calmar with thresholds for an excellent fund, sets out benchmarks for equity and hybrid funds, and recommends an evaluation period of at least three years.
Fund Analysis and FOF Screening fits situations like: screening equity or hybrid funds across return, risk and risk-adjusted metrics; detecting style drift in a fund manager's portfolio; evaluating a fund manager's tenure and excess return; building an FOF portfolio from several funds and ETFs.
Run `npx skills add HKUDS/Vibe-Trading --skill fund-analysis -a claude-code`. Or copy the skill folder (agent/src/skills/fund-analysis in HKUDS/Vibe-Trading) into .claude/skills/fund-analysis in your project. Claude Code loads it when a task matches its description.
Run `npx skills add HKUDS/Vibe-Trading --skill fund-analysis -a codex`. Or copy the skill folder (agent/src/skills/fund-analysis in HKUDS/Vibe-Trading) into .agents/skills/fund-analysis in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/Vibe-Trading --skill fund-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fund-analysis, .gemini/skills/fund-analysis, .github/skills/fund-analysis and .opencode/skills/fund-analysis in your project.
Going by SKILL.md and its folder, Fund Analysis and FOF Screening needs the command-line tools its instructions call (pip). Our summary lists: Python with pandas, numpy and scipy.
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Fund Analysis and FOF Screening is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Fund Analysis and FOF Screening: Stock Deep Analysis Workflow (wbh604/UZI-Skill, 7.1k stars), Tushare Data (zillionare/zillionare, 319 stars), Three-Statement Model Builder (ginlix-ai/LangAlpha, 1.8k stars) and Quant Blog Writing (zillionare/zillionare, 319 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 34,949 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 8, 2026.
Source: HKUDS/Vibe-Trading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.