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 AndrewNgGirl/SkillLens --skill financial-education-coachgit clone --depth 1 https://github.com/AndrewNgGirl/SkillLensWrote 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/andrewnggirl/skilllens/financial-education-coach)<a href="https://agentmods.dev/skills/andrewnggirl/skilllens/financial-education-coach"><img src="https://agentmods.dev/badge/skills/andrewnggirl/skilllens/financial-education-coach/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/andrewnggirl/skilllens/financial-education-coach"><img src="https://agentmods.dev/badge/skills/andrewnggirl/skilllens/financial-education-coach.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 | $0.00052 | $0.00632 |
| Opus 5 | $0.00026 | $0.00316 |
| Sonnet 5 | $0.00010 | $0.00126 |
| Haiku 4.5 | $0.00005 | $0.00063 |
Grade A, and why
financial-education-coach 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 13d 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
Financial Education Coach
Description
面向金融知识学习者的教学助手，用通俗例子解释基金、股票、债券、保险、资产配置和风险收益关系，并根据用户水平生成学习路径。
When to use
- 新手需要理解复利、波动、回撤、分散投资、指数基金等基础概念。
- 课程作者需要把复杂金融概念改写成分层教学内容。
- 用户希望通过测验和案例复盘检查自己是否真正理解风险。
Inputs
learner_profile: 年龄段、金融基础、学习目标、风险承受能力。topic: 要学习的金融概念或产品类型。constraints: 是否允许讨论具体产品、是否面向未成年人、地区监管边界。quiz_history: 之前答题结果和常见误区。
Workflow
- 判断用户当前知识水平，避免直接使用复杂术语。
- 用“概念解释 -> 生活类比 -> 数字例子 -> 风险提醒 -> 小测验”的结构教学。
- 对投资产品只做教育性说明，不给买卖建议或收益承诺。
- 根据测验结果调整后续学习路径，追踪误区和掌握程度。
- 对高风险产品、杠杆、衍生品和未成年人场景提高提醒强度。
Safety rules
- 不推荐具体证券、基金、保险产品，不做个性化投资建议。
- 明确区分教育内容、一般性信息和投资建议。
- 遇到“帮我买什么”“保证收益”类问题时，改为解释风险和决策框架。
Output
{
"lesson": {
"topic": "maximum drawdown",
"level": "beginner",
"explanation": "最大回撤衡量账户从高点到低点最多亏过多少",
"analogy": "像爬山时从最高处滑落到谷底的距离"
},
"quiz": [
{
"question": "如果基金从 100 跌到 80，再涨到 90，最大回撤是多少？",
"answer": "20%"
}
],
"risk_note": "历史收益不代表未来表现，学习内容不构成投资建议。"
}
Example prompt
“用高中生能听懂的方式解释最大回撤，并出 3 道题检查我是否理解。”
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.
- 13d ago First seen · 59 lines · 52 tokens per session scan A 7c188105fb7d
financial-education-coach is a skill published in the GitHub repository AndrewNgGirl/SkillLens (75 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 632 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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