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/buffett-value-investing/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/buffett-value-investing)<a href="https://agentmods.dev/skills/belos-street/stock-analytics-skill/buffett-value-investing"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/buffett-value-investing/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/buffett-value-investing"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/buffett-value-investing.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 | $0.00055 | $0.04221 |
| Opus 5 | $0.00028 | $0.02110 |
| Sonnet 5 | $0.00011 | $0.00844 |
| Haiku 4.5 | $0.00006 | $0.00422 |
Grade A, and why
buffett-value-investing 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
巴菲特核心理念股票分析技能(通用版)
技能定位
本技能专为大模型设计,严格基于巴菲特价值投资四大核心原则(能力圈、护城河、安全边际、长期持有),对任意股票进行系统化、结构化分析,最终输出可落地的投资决策建议(含估值判断、建仓/持有策略、风险提示),适用于A股、港股等全球主流市场的个股分析场景。
核心分析框架(巴菲特四大原则拆解)
原则1:能力圈原则(巴菲特"只投资看得懂的公司")
分析维度需覆盖3个核心要点,大模型需逐一验证并输出结论:
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业务易懂性:判断股票对应公司的业务模式是否简单清晰、无复杂跨界/概念炒作,是否属于大模型可清晰拆解盈利逻辑的赛道(如制造业、消费品、金融等)。
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认知匹配度:评估公司所处行业的发展阶段、竞争格局是否可通过公开信息(财报、行业报告)准确预判,是否存在无法预测的技术代际更迭、政策突变等变量。
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管理层一致性:核查管理层是否专注主业(无频繁跨界、盲目多元化),是否践行股东利益至上(如分红政策稳定、无损害中小股东的关联交易)。
原则2:护城河原则(巴菲特"寻找拥有持久竞争优势的企业")
从定性+定量双维度分析,大模型需结合行业特性与公司数据输出结论:
| 分析维度 | 定性判断要点 | 定量验证指标 |
|---|---|---|
| 成本优势 | 是否拥有规模效应、供应链壁垒、资源独占等成本控制能力 | 毛利率/净利率(对比行业均值、历史中枢)、单位成本降幅、原材料自给率 |
| 品牌壁垒 | 是否具备全球/区域龙头品牌、高客户忠诚度、不可替代性 | 市占率(全球/国内)、品牌溢价率、客户复购率/留存率 |
| 技术/专利壁垒 | 是否拥有核心专利、独家技术、行业认证门槛 | 专利数量/质量、行业准入认证持有率、研发投入占比(对比同行) |
| 客户转换成本 | 客户更换供应商是否需付出高成本(如适配性、数据迁移) | 大客户合作年限、客户集中度(是否依赖单一车企/客户) |
| 渠道壁垒 | 是否拥有独家渠道、覆盖网络、供应链控制权 | 渠道数量/覆盖密度、渠道议价能力、供应链话语权指标 |
原则3:安全边际原则(巴菲特"以50美分买1美元的资产")
核心是估值测算+现金流验证,大模型需结合财报数据与行业估值中枢输出结论:
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估值合理性:对比3类核心估值指标(PE、PB、股息率)与历史区间、行业均值,计算估值溢价/折价幅度。
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内在价值测算:基于公司未来3-5年盈利增速(结合行业周期、公司增长逻辑),采用现金流折现(DCF) 或市盈率相对盈利增长比率(PEG) 测算内在价值。
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安全边际计算:公式=(内在价值-当前股价)/内在价值,需明确安全边际是否达到巴菲特认可的30%以上核心阈值。
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现金流/分红安全垫:核查经营现金流是否覆盖净利润、股息率是否稳定(对比无风险利率)、分红比例是否合理。
原则4:长期持有原则(巴菲特"持有优质公司穿越周期")
分析维度需聚焦长期价值可持续性:
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需求韧性:判断公司产品/服务是否为刚需、是否具备长期增长逻辑(如消费升级、全球化、技术迭代红利)。
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周期抗风险:评估公司在行业下行周期、宏观经济波动中的业绩抗跌能力(如历史周期业绩表现、毛利率稳定性)。
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复利潜力:结合公司历史盈利增速、分红增速,测算长期复利回报是否具备可持续性。
标准化分析流程(大模型执行步骤)
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输入信息接收:获取目标股票代码、市场、最新股价、核心业务、最新财报关键数据(营收、净利、毛利率、现金流、负债率)、行业背景。
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能力圈验证:按框架1的3个维度,输出"是否符合能力圈原则"的结论+依据。
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护城河评估:按框架2的5个维度,结合定性数据与定量指标,输出"护城河强度(强/中/弱)"结论+核心支撑点。
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安全边际测算:按框架3的4个维度,计算估值指标、内在价值、安全边际,输出"估值区间+安全边际等级(充足/一般/不足)"结论。
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长期持有可行性:按框架4的3个维度,输出"长期持有价值(高/中/低)"结论+风险对冲逻辑。
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投资决策建议:结合以上所有结论,输出具体建议:
- 若股价高于内在价值:明确"不建议建仓/持有,等待估值回归"。
- 若股价处于内在价值±10%:明确"可轻仓关注,分批建仓"。
- 若股价低于内在价值30%以上:明确"安全边际充足,建议分批建仓,明确仓位比例与持有周期"。
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 · 283 lines · 55 tokens per session scan A 66bd07c09358
buffett-value-investing is a skill published in the GitHub repository belos-street/stock-analytics-skill (49 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 4,221 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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