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 stock-candlesticksgit 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/stock-candlesticks)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/stock-candlesticks"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-candlesticks/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/stock-candlesticks"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-candlesticks.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 | $0.00122 | $0.01276 |
| Opus 5 | $0.00061 | $0.00638 |
| Sonnet 5 | $0.00024 | $0.00255 |
| Haiku 4.5 | $0.00012 | $0.00128 |
Grade A, and why
stock-candlesticks 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 yesterday.
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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
股票 K 线 - 查询单只标的 K 线（stock-candlesticks）
1. 接口描述
| 项目 | 说明 |
|---|---|
| 接口名称 | 查询单只标的 K 线（通用） |
| 外部接口 | GET /api/v1/market/data/stock-candlesticks |
| 请求方式 | GET（query 参数） |
| 适用场景 | 获取股票 / ETF / 指数 / 可转债等标的的分/日/周/月/年 K 线，含开高低收、成交量、成交额；支持前复权 / 后复权 / 不复权。通用语义，不做证券类别校验 |
本接口统一使用毫秒时间戳和 GET query 参数，支持分钟级与年 K，并允许股票、ETF、指数、可转债等不同类别标的。
2. 请求参数
| 参数名 | 类型 | 是否必填 | 描述 | 取值示例 | 备注 |
|---|---|---|---|---|---|
| symbol | string | 是 | 标的代码（带市场后缀） | 600519.SH、000001.SZ、510300.XSHG | 长短后缀均支持；.XSHG/.SH、.XSHE/.SZ、.BJSE/.BJ |
| interval_unit | string | 是 | 周期单位 | Day | Minute/Day/Week/Month/Year |
| interval_value | int | 否 | 间隔数值 | 1 | 默认 1；Minute+5 表示 5 分钟 K 线 |
| adjust_kind | string | 否 | 复权类型 | Forward | None（默认）/Forward（前复权）/Backward（后复权） |
| since_ts_millis | int | 否 | 开始时间戳（毫秒） | 1756700000000 | 分钟 K 线与 until 跨度 ≤3 天，其余周期不受 3 天限制 |
| until_ts_millis | int | 是 | 结束时间戳（毫秒） | 1756791000000 | - |
| limit | int | 否 | 返回条数上限 | 5 | 未传 since 和 limit 时默认最多返回 50 根 |
3. 响应说明
返回裸数组，每根 K 线包含：
| 字段名 | 类型 | 说明 | 单位 |
|---|---|---|---|
| open | string | 开盘价 | 元 |
| high | string | 最高价 | 元 |
| low | string | 最低价 | 元 |
| close | string | 收盘价（或最新价） | 元 |
| ts_millis | int | 收盘时间戳 | 毫秒 |
| ts_millis_open | int | 开盘时间戳 | 毫秒 |
| turnover | string | 成交额 | 元 |
| volume | int64 | 成交量 | 股/份 |
| turnover_rate | number | 换手率 | % |
4. 调用方式
python <RUN_PY> stock-candlesticks --symbol 600519.SH --interval-unit Day --since-ts-millis 1756431000000 --until-ts-millis 1756710000000 --limit 5
python <RUN_PY> stock-candlesticks --symbol 000001.SZ --interval-unit Minute --interval-value 5 --adjust-kind Forward --since-ts-millis 1756700000000 --until-ts-millis 1756791000000
<RUN_PY> 为主 SKILL.md 同级 run.py 的绝对路径。输出 JSON，请求头已内置 X-Client-Name: ft-claw。
5. 注意事项
symbol、interval_unit、until_ts_millis必填。symbol必须带市场后缀；不带后缀或后缀无法识别时外部接口返回系统错误。- 分钟 K 线（
interval_unit=Minute）的since/until跨度硬限制 ≤3 天，超过需分段调用。 interval_value仅在interval_unit=Minute时生效：不传或传 1 为 1 分钟 K，传 5/15/30/60/120 为对应多分钟 K；其他周期忽略该字段。- 多分钟 K 按北京时间的每个交易日分别聚合，不跨交易日；以 5 分钟 K 为例，首根为 09:30—09:35，开高低收取区间首根开盘价、最高价、最低价、末根收盘价，成交量和成交额按区间求和。
- 默认不复权（None），
Forward前复权、Backward后复权。 - 该接口不校验证券类别；查 ETF 推荐用
etf-candlesticks、查指数用index-candlesticks、查可转债用convertible-bond-candlesticks。 - 价格字段 JSON 中为字符串以避免精度丢失。
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.
- yesterday Changed · +1 lines a5ce6ed92a80
- 8d ago First seen · 65 lines · 122 tokens per session scan A 2fd06a856658
stock-candlesticks is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (64 stars, last pushed yesterday), licensed MIT. It adds 122 tokens to every session and 1,276 once invoked, about $0.0006 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
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…