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 etf-pricesgit 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/etf-prices)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/etf-prices"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/etf-prices.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.01347 |
| Opus 5 | $0.00020 | $0.00674 |
| Sonnet 5 | $0.00008 | $0.00269 |
| Haiku 4.5 | $0.00004 | $0.00135 |
Grade A, and why
etf-prices 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.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ETF 分时价格 - 查询单只 ETF 分钟级分时
1. 接口描述
| 项目 | 说明 |
|---|---|
| 接口名称 | 查询单只 ETF 分时价格（一分钟级别） |
| 外部接口 | GET /api/v1/market/data/daec/history/prices |
| 请求方式 | GET |
| 适用场景 | 获取 A 股指定 ETF 在指定时间范围内的分时数据（一分钟一根），用于分时图、当日/多日走势；每条含该分钟的价格、成交量、成交额、均价、时间戳；支持从今天起、从五日前起、从 N 个交易日前起或从指定毫秒时间戳起 |
已从 v2（
/app/api/v2/etfs/:etf/prices）迁移至 daec 统一标的接口（daec/history/prices?symbol=）。对外参数契约不变（--since/--since_ts_ms），脚本内部映射为 daec 的range/days/ts_ms。
2. 请求参数
说明：etf 为路径参数（必填）；时间范围由 since 或 since_ts_ms 二选一指定。脚本内部映射：TODAY→range=Today、FIVE_DAYS_AGO→range=FiveDays、TRADE_DAYS_AGO(n)→days=n、since_ts_ms→ts_ms=<毫秒>。
| 参数名 | 类型 | 是否必填 | 描述 | 取值示例 | 备注 |
|---|---|---|---|---|---|
| etf | string | 是 | ETF 标的键（路径参数，带市场后缀） | 510050.XSHG、159915.XSHE、920036.BJ | 沪 .XSHG、深 .XSHE、北交所 .BJ |
| since | string | 条件必填 | 时间范围起点（语义） | TODAY、FIVE_DAYS_AGO、TRADE_DAYS_AGO(10) | 见下方取值说明；与 since_ts_ms 二选一 |
| since_ts_ms | long | 条件必填 | 时间范围起点（毫秒时间戳） | 1735689600000 | 须属于“今天”或“最近一个交易日”；不传 since 时必传 |
since 取值说明：
| 取值 | 含义 |
|---|---|
| TODAY | 从今天（或最近一个交易日）的第一条分时数据开始 |
| FIVE_DAYS_AGO | 从五个交易日前的第一条分时数据开始（含今天） |
| TRADE_DAYS_AGO(n) | 从 n 个交易日前的第一条分时数据开始（含今天），n 为正整数 |
3. 响应说明
返回指定 ETF 的分时价格列表，包装为 {"prices": [...]}（daec 返回裸数组，脚本统一包装）：
{
"prices": [
{ "price": 3.005, "avg_price": 3.003, "volume": 12499680, "turnover": 37543462.22, "ts_ms": "2026-06-22T09:35:00" }
]
}
分时单条（prices 元素）
| 字段名 | 类型 | 是否可为空 | 说明 | 单位 |
|---|---|---|---|---|
| price | float | 否 | 该分钟价格 | 元 |
| avg_price | float | 是 | 该分钟均价 | 元 |
| volume | long | 否 | 该分钟成交量 | 份 |
| turnover | float | 否 | 该分钟成交额 | 元 |
| ts_ms | string | 否 | 该分钟时间（北京时间 ISO） | - |
注：daec 接口不再返回
prev_close（昨收）与today（当前交易日），如需请改用其他接口。
4. 用法
通过主目录 run.py 调用（必填 --etf，且必填 --since 或 --since_ts_ms 其一）：
python <RUN_PY> etf-prices --etf 510050.XSHG --since TODAY
python <RUN_PY> etf-prices --etf 159915.XSHE --since FIVE_DAYS_AGO
python <RUN_PY> etf-prices --etf 510050.XSHG --since "TRADE_DAYS_AGO(10)"
python <RUN_PY> etf-prices --etf 510050.XSHG --since_ts_ms 1735689600000
What ships with it
2 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.
- 8d ago First seen · 81 lines · 40 tokens per session scan A 2f320aa5c789
etf-prices is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (63 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,347 once invoked, about $0.0002 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.
Other skills, from other repositories
defeatbeta-earnings-analysis
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when…
sprr
Single PR reviewer for awesome-quant. Use when the user asks to review, validate, comment on, label, close, or merge one specific pull request that adds README.md entries. Triggers include "sprr", "review PR", "check PR", and "validate contribution".
bprr
Bulk PR reviewer for awesome-quant. Use when the user asks to review all open PRs, review unreviewed PRs, bulk review, or mentions "bprr". Reviews open PRs lacking the reviewed label and presents a summary before any merge/comment/label action.
update-pypi-dates
Refresh tracked PyPI last-updated dates in awesome-quant README.md. Use when the user asks to update PyPI dates, refresh PyPI metadata, or run update-pypi-dates.
defeatbeta-earnings-preview
Build pre-earnings analysis with normalized baselines, weighted decision models, company-specific veto gates, scenario frameworks, catalysts, historical reactions, and options-implied moves. Use before a company reports quarterly earnings to prepare positioning notes or bilingual three-page PDF reports.
defeatbeta-analyst
Professional financial analysis using 60+ market data APIs. Use for: company fundamentals (revenue, margins, EPS, balance sheet), valuation (P/E, P/B, P/S, PEG, DCF, intrinsic value), profitability (ROE, ROA, ROIC), growth trends (YoY revenue/earnings/FCF), earnings transcripts (key data, changes, guidance), industry…