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 sun-btc/biga --skill biga-value-skillgit clone --depth 1 https://github.com/sun-btc/bigaWrote 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/sun-btc/biga/biga-value-skill)<a href="https://agentmods.dev/skills/sun-btc/biga/biga-value-skill"><img src="https://agentmods.dev/badge/skills/sun-btc/biga/biga-value-skill/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/sun-btc/biga/biga-value-skill"><img src="https://agentmods.dev/badge/skills/sun-btc/biga/biga-value-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00259 | $0.15647 |
| Opus 5.5 | $0.00104 | $0.06259 |
| Sonnet 5.5 | $0.00052 | $0.03129 |
| Haiku 4.5 | $0.00026 | $0.01565 |
Grade A, and why
biga-value-skill 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 1mo 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 — 2,781 lines — stays where its author put it; the contents beside it link to each section on GitHub.
biga-value-skill
目标
使用本 skill 时,你需要帮助用户构建或应用一套完整的 A 股价值投资分析体系。
核心思想:
价值投资 = 好行业 + 好生意 + 好公司 + 好价格 + 安全边际 + 长期纪律 + 风险控制
不要把价值投资简化为:
低 PE 就买
低 PB 就买
高股息就买
机构买入就跟
券商评级买入就买
股东户数减少就买
你需要始终把分析重心放在:
1. 企业长期赚钱能力
2. 自由现金流创造能力
3. 竞争优势与护城河
4. 财务质量
5. 合理估值
6. 安全边际
7. 风险控制
8. 市场预期与资金结构辅助验证
适用场景
当用户提出以下需求时使用本 skill:
解释或完善价值投资策略
分析 A 股上市公司是否适合价值投资
构建价值投资选股模型
设计价值投资评分表
分析 ROE、ROIC、现金流、分红、估值
判断低 PE、低 PB、高股息是否有陷阱
分析股东户数、散户数量、筹码集中度
分析机构持仓、基金持仓、社保、险资、QFII、北向资金
分析国内外机构评级、目标价、一致预期
制定买入、加仓、卖出和复盘规则
构建适合散户的长期投资框架
投资者教育声明
回答中应根据场景加入简短提示:
以下内容是投资方法论和投资者教育,不构成个股推荐或收益承诺。A 股波动较大,投资前应结合自身风险承受能力。
如果用户询问具体个股,可以分析框架、风险和数据维度,但不要用确定性语言承诺上涨或推荐满仓买入。
避免使用:
一定上涨
必买
稳赚
无风险
马上买
满仓买
短期必涨
建议使用:
可以进一步研究
需要结合估值和风险
属于辅助信号
不宜单独作为买卖依据
需要持续跟踪
核心分析框架
价值投资分析应优先使用以下 18 个维度:
1. 行业空间
2. 行业生命周期
3. 商业模式
4. 产业链地位
5. 竞争优势
6. 技术路线与替代风险
7. 财务质量
8. 盈利质量
9. 资产质量
10. 负债结构与偿债压力
11. 成长能力
12. 现金流质量
13. 估值水平
14. 股东回报与资本配置
15. 公司治理与大股东行为
16. 市场预期与资金结构
17. 宏观环境与流动性
18. 风险、安全边际与压力测试
也可以简化为三层:
核心价值层:判断公司值不值得研究
风险控制层:判断会不会踩雷
市场验证层:判断买入节奏和预期差
不要让资金流、股东户数、机构评级替代基本面判断。
一、行业空间
先看行业,再看公司。
需要分析:
行业未来 5-10 年需求是增长、稳定还是衰退
行业竞争格局是否清晰
行业是否有政策风险
行业利润率是否稳定
行业是否容易被技术替代
行业是否处于周期高点或低点
好行业通常具备:
需求长期存在
市场空间足够大
竞争格局稳定
龙头企业有定价权
现金流好
不需要持续巨额资本开支
政策风险较低
需要谨慎的行业通常具备:
重资产
高负债
强周期
价格战严重
技术迭代过快
政策不确定性强
现金流差
依赖补贴
依赖融资扩张
对于周期行业,提醒用户:
周期股在盈利高点时 PE 可能很低,但并不一定便宜。
周期行业更应关注:
供需关系
产品价格
库存周期
产能扩张
资本开支周期
成本曲线
二、商业模式
分析公司靠什么赚钱。
需要回答:
公司卖什么?
客户是谁?
客户为什么买它?
收入是否可重复?
是否有涨价能力?
是否需要大量资本开支?
是否依赖大客户?
是否需要赊销?
好商业模式通常表现为:
高毛利
高净利
轻资产
现金流好
客户粘性强
复购率高
应收账款少
库存风险低
有品牌、渠道、成本或技术壁垒
差商业模式通常表现为:
毛利低
价格战
赊销严重
库存积压
资本开支大
客户集中度高
回款周期长
靠融资扩张
利润增长但现金流差
核心判断:
好商业模式是收现金快、利润稳定、扩张不烧钱。
差商业模式是赚账面利润、占用大量资金、靠融资续命。
三、竞争优势
分析公司是否有护城河。
常见护城河包括:
品牌优势
成本优势
渠道优势
技术优势
资源优势
牌照优势
网络效应
客户迁移成本
规模优势
供应链优势
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.
- 1mo ago First seen · 2,781 lines · 259 tokens per session scan A 7c4188a971f8
biga-value-skill is a skill published in the GitHub repository sun-btc/biga (7 stars, last pushed 5mo ago), licensed MIT. It adds 259 tokens to every session and 15,647 once invoked, about $0.0010 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
stock-daily
A daily review workflow for the Chinese A-share stock market, covering market conditions, active themes, and the user's positions.
stock-buddy
A two-level assistant for analysing Chinese stocks, sectors, and investment themes. It can provide a normal analysis or, after confirmation, a deeper financial review that may consider holdings and risk-budget position suggestions.
stock-screener
A stock-screening tool that starts with an established investment theme and finds related exchange-traded funds and leading companies. It ranks the candidates by factors such as strength, money flow, liquidity, and potential price movement.
serenity-skill
Research technology and advanced-manufacturing investments using Serenity-inspired supply-chain bottleneck analysis. Use for theme scans, company thesis challenges, candidate comparisons, or learning this method. Prioritize A-shares unless another market is requested. Return research priorities, dated evidence, profit…
a-share-data
A data lookup skill for mainland Chinese A-shares, meaning stocks traded on China's Shanghai and Shenzhen exchanges. It covers prices, history, indicators, company events, sectors, concepts, market news, and macroeconomic data.
a-share-paper-trading
A simulated stock-trading and backtesting system for mainland Chinese A-shares. Simulated trading uses pretend orders and money, while backtesting evaluates a strategy against past data.