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 FTShare-Lab/FTShare-skill --skill economic-china-lpr-monthlygit clone --depth 1 https://github.com/FTShare-Lab/FTShare-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/ftshare-lab/ftshare-skill/economic-china-lpr-monthly)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/economic-china-lpr-monthly"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/economic-china-lpr-monthly/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/ftshare-lab/ftshare-skill/economic-china-lpr-monthly"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/economic-china-lpr-monthly.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 | $0.00073 | $0.00580 |
| Opus 5 | $0.00036 | $0.00290 |
| Sonnet 5 | $0.00015 | $0.00116 |
| Haiku 4.5 | $0.00007 | $0.00058 |
Grade A, and why
economic-china-lpr-monthly 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 8d 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
中国经济 - LPR 贷款市场报价利率（月度）
1. 接口描述
| 项目 | 说明 |
|---|---|
| 接口名称 | LPR 贷款市场报价利率（月度汇总计算结果） |
| 外部接口 | GET /api/v1/market/data/economic/china-lpr |
| 请求方式 | GET |
| 适用场景 | 获取中国 LPR 1 年期与 5 年期利率按日期汇总数据 |
2. 请求参数
说明：该接口无需请求参数。
| 参数名 | 类型 | 是否必填 | 描述 | 取值示例 | 备注 |
|---|---|---|---|---|---|
| - | - | - | 无需参数 | - | - |
3. 用法
直接执行：
python script/handler.py
脚本输出 JSON 数组，按时间倒序，每项含 date（如 2025-12-22）、lpr_1y（1 年期 LPR %）、lpr_5y（5 年期 LPR %），以表格展示给用户。
4. 响应说明
返回值为 LPR 按日期计算结果列表，按时间倒序。
LprComputed 结构
| 字段名 | 类型 | 是否可为空 | 说明 | 单位 |
|---|---|---|---|---|
| date | String | 否 | 日期，格式如 2025-12-22 | - |
| lpr_1y | float | 是 | LPR 1 年期利率 | % |
| lpr_5y | float | 是 | LPR 5 年期利率 | % |
5. 请求示例
GET /api/v1/market/data/economic/china-lpr
6. 注意事项
- 返回按报价日期（通常为每月 20 日左右）汇总，列表已按时间倒序，最新日期在前。
lpr_1y、lpr_5y单位为 %，可为 null。
调用示例
python <RUN_PY> economic-china-lpr-monthly
What ships with it
1 file 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.
- 8d ago Changed · +6 lines 6c3bb443ba51
- 12d ago First seen · 57 lines · 73 tokens per session scan A 40d59f4d560b
economic-china-lpr-monthly is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (64 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 580 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 2026-08-30.
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