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 Travisun/Opptrix --skill investment-checklistgit clone --depth 1 https://github.com/Travisun/OpptrixWrote 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/travisun/opptrix/investment-checklist)<a href="https://agentmods.dev/skills/travisun/opptrix/investment-checklist"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/investment-checklist/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/travisun/opptrix/investment-checklist"><img src="https://agentmods.dev/badge/skills/travisun/opptrix/investment-checklist.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 | $0.00072 | $0.01080 |
| Opus 5 | $0.00036 | $0.00540 |
| Sonnet 5 | $0.00014 | $0.00216 |
| Haiku 4.5 | $0.00007 | $0.00108 |
Grade A, and why
investment-checklist 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 6d 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.
What it actually says
巴菲特买入前 Checklist
对一家或多家公司过六关。宁可错过，不可做错——目标是排除坏选择。
何时使用 / 非目标
| 使用 | 不要用 |
|---|---|
| 买入前快速过关 / 多公司对比 | 完整四大师深度长文 → @skill:investment-research |
| 镜子测试与快速否决 | 通用尽调叙事 → @skill:equity-deep-dive |
收益型分配专评 → @skill:income-investment |
研究质量（硬性）
- A/B/C：C 级不勉强填满六关；数据不足 = 灰色地带，≠ 通过/否决
- 镜子测试不可跳过：≤5 句话说不清 → 不买
- 快速否决一票否决；诚信污点不可被高分对冲
- 关键财务用
financial_rigor；署名 Opptrix · AI Berkshire 分析
六关
- 能力圈：一句话说清赚钱方式；10 年后还做什么
- 好生意：ROE/毛利/FCF/轻资产/负债（
verify-valuation） - 护城河：五类 +「给对手 100 亿能否复制」
- 管理层：诚信、资本配置、利益对齐
- 安全边际：倍数分位 +
three-scenario - 纪律：FOMO/停牌五年/200 字理由
每关 ★1–5（无半星）。
Opptrix 取数
精简 #1 栈：search_instruments / batch_instrument_snapshots / financials / indicators / cash_flow / dividend / profile / shareholders / news。多公司可并行子 Agent，须回收。
脚本
# Agent 对每关打星并写入 JSON 后：
python scripts/checklist_score.py --input checklist.json --output score.json
python scripts/financial_rigor.py verify-valuation --price ... --eps ...
python scripts/financial_rigor.py three-scenario --price ... --eps ... --shares ...
checklist_score 输出 pass / conditional_pass / reject / grey；data_mode 随 gates 完整度自适应。
步骤
- 解析公司列表；未上市 → N/A 简述
- A/B/C 标注
- 取数 → 六关评分 → 镜子测试 → 快速否决清单
checklist_score.py汇总；多公司总览表create_web：每家一章 + 明确结论
快速否决（触发即否决）
说不清赚钱方式；连续 3 年 FCF 为负无改善；诚信污点；护城河不可逆侵蚀；博傻；无法承受归零；纯跟风；写不清 200 字理由。
网页目录建议
- 总览对比表
- 各公司六关 + 数据表 + 镜子测试 + 结论
- 否决/灰色说明
- 免责声明
禁止
- 跳过镜子测试给「通过」
- C 级因填不满表格就否决
- 原仓库路径 / 脚本联网
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 91 lines · 72 tokens per session scan A a0ff16e0dee4
investment-checklist is a skill published in the GitHub repository Travisun/Opptrix (231 stars, last pushed 3d ago), licensed Apache-2.0. It adds 72 tokens to every session and 1,080 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
national-team-position
A Chinese-language analysis tool that estimates changes in China’s government-backed ETF holdings by tracking ETF share counts and related index prices. ETFs are funds traded on stock exchanges, and the “national team” refers here to Central Huijin, a state investment company.
caijing-ipo-hk
A Chinese-language adviser for Hong Kong stock initial public offerings, or IPOs—the first sale of a company's shares to the public. It covers how to apply, how much to apply for, and risks such as the share price falling below the offering price.
caijing-fundamental
A finance research skill for writing a detailed, forward-looking analysis of a listed company’s business, financials, valuation, risks, and investment arguments. It covers companies listed in mainland China and Hong Kong.
rodya-caijing-studio
A toolkit for researching Chinese A-share and Hong Kong-listed companies and producing financial content. It includes separate workflows for company fundamentals, earnings, valuation, risks, industries, and IPO checks.
caijing-earnings
A finance research skill for reviewing listed companies’ earnings reports, or preparing for an upcoming report. It focuses on Chinese A- and Hong Kong-listed companies.
caijing-valuation
A Chinese-language adviser that assesses whether a stock's current valuation looks high or low. It adapts the comparison to the industry and examines historical and peer-company valuation ranges.