Get China LPR (Loan Prime Rate) monthly data (LPR 贷款市场报价利率 月度). Use when user asks about LPR, 贷款市场报价利率, 1年期LPR, 5年期LPR, 中国 LPR, 房贷利率, China LPR.
Scanned 9/2/2026
Install to Claude Code
npx -y skills add FTShare-Lab/FTShare-skill --skill economic-china-lpr-monthly --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Economic China Lpr Monthly?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/ftshare-lab-economic-china-lpr-monthly)More formats (shields.io, HTML) on the badges page.
---
name: economic-china-lpr-monthly
description: Get China LPR (Loan Prime Rate) monthly data (LPR 贷款市场报价利率 月度). Use when user asks about LPR, 贷款市场报价利率, 1年期LPR, 5年期LPR, 中国 LPR, 房贷利率, China LPR.
---
# 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:
```bash
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。
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Interview, source-challenge, verify, save, and ADR-gate fuzzy coding requests into Codex-ready implementation specs. Use when a feature, bugfix, refactor, migration, repo-wide change, or architecture task needs user-verified requirements, source-backed decisions, durable architecture decisions, acceptance criteria, validation commands, rollout notes, saved spec/ADR files, and a Codex execution prompt. Do not use when already fully specified or when the user wants direct implementation now.