No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by simonlin1212 · Agent Tool · ★ 111
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is astock-peg safe to install? View the security audit →
简体中文 | English astock-peg A 股 PEG 估值分析工具 — Next.js 全栈 · AI 生成估值报告 · 行业板块 PE 对比 · 零数据库依赖 为什么做这个工具 · 功能演示 · 技术架构 · 数据源 · 快速开始 · PEG 计算逻辑 本工具只做 PEG 相关的计算与数据呈现,不做估值定性、不给买卖建议、不预测涨跌,不构成任何投资建议,投资决策请咨询持牌专业机构。 为什么做这个工具 PE 只告诉你一只股票"贵不贵",但不告诉你"贵得值不值"。 PEG(市盈率相对盈利增长比率) 是彼得·林奇提出的核心估值指标,用一个公式把"估值"和"成长性"绑在一起: 本工具只输出计算结果(PEG 数值、前瞻 PE、CAGR、PE 消化年数、同行数值对比), 不给「低估 / 高估」这类估值定性,也不给买卖时机、目标价或操作倾向 —— 估值是否合适由你自己判断。 问题是:手动计算 PEG 需要翻财报、查一致预期、算 CAGR、对比同行 —— 每只股票重复一遍非常低效。 这个工具解决的问题:输入 6 位股票代码,自动完成数据采集 → PEG 计算 → AI 估值报告生成 → 同行业 PE 对比,全流程 < 30 秒。 功能演示 PEG 看板 输入股票代码,实时监控价格、涨跌幅、PE(TTM)、PB、市值。一键发起 AI 分析。 AI PEG 估值报告 自动采集财务数据 → 送入大模型 →
| Stars | 111 |
| Forks | 39 |
| Language | TypeScript |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 65.5891072074702/100 |
| Last Updated | 2026-09-16 |
| Created | 2026-05-15 |
| Platforms | claude-code, node |
| Est. Tokens | ~15k |
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astock-peg is A股 PEG 估值分析工具 — 彼得·林奇 PEG 投资法本地化实践 | A-share PEG valuation tool with AI-powered analysis. It is categorized as a Agent Tool with 111 GitHub stars.
astock-peg is primarily written in TypeScript. It covers topics such as a-share, ai-agent, china-stocks.
You can find installation instructions and usage details in the astock-peg GitHub repository at github.com/simonlin1212/astock-peg. The project has 111 stars and 39 forks, indicating an active community.
astock-peg is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to astock-peg on Agent Skills Hub include ashare-lake, chanlun-trading-system, ai-workspace-hub. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: