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-iposgit 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-ipos)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/stock-ipos"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-ipos/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-ipos"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-ipos.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 | $0.00050 | $0.00968 |
| Opus 5 | $0.00025 | $0.00484 |
| Sonnet 5 | $0.00010 | $0.00194 |
| Haiku 4.5 | $0.00005 | $0.00097 |
Grade A, and why
stock-ipos 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
获取股票 IPO 列表
接口说明
| 项目 | 说明 |
|---|---|
| 接口名称 | 获取股票 IPO 列表 |
| 外部接口 | /api/v1/market/data/stock-ipos |
| 请求方式 | GET |
| 适用场景 | 获取 A 股 IPO 列表，含发行价格、发行数量、申购日期、上市日期等，支持分页查询 |
请求参数
| 参数名 | 类型 | 是否必填 | 描述 | 取值示例 | 备注 |
|---|---|---|---|---|---|
page |
int | 是 | 页码，从 1 开始 | 1 |
必须大于等于 1 |
page_size |
int | 是 | 每页记录数 | 20 |
必须大于等于 1，建议不超过 100 |
执行方式
通过根目录的 run.py 调用（推荐）：
# 获取第 1 页，每页 20 条
python <RUN_PY> stock-ipos --page 1 --page_size 20
# 获取第 2 页，每页 50 条
python <RUN_PY> stock-ipos --page 2 --page_size 50
# 获取全量数据（自动翻页合并）
python <RUN_PY> stock-ipos --all
<RUN_PY>为主SKILL.md同级的run.py绝对路径，参见主 SKILL.md 的「调用方式」说明。
响应结构
{
"items": [
{
"symbol": "600989.SH",
"industry_pe": 15.8,
"listing_date": "2019-05-16",
"max_subscription_shares": 120000,
"online_shares": 600000000,
"pe": 22.5,
"price": "11.25",
"shares": 734000000,
"subscription_date": "2019-05-10",
"subscription_symbol_id": "730989"
}
],
"total_pages": 10,
"total_items": 200
}
顶层字段说明
| 字段名 | 类型 | 是否可为空 | 说明 |
|---|---|---|---|
items |
Array | 否 | 当前页 IPO 列表 |
total_pages |
int | 否 | 总页数 |
total_items |
int | 否 | 总记录数（未分页前） |
items 元素字段说明（StockIpo）
| 字段名 | 类型 | 是否可为空 | 说明 | 单位 |
|---|---|---|---|---|
symbol |
String | 否 | 标的代码，带 .SZ/.SH/.BJ 市场后缀 |
- |
industry_pe |
float | 是 | 行业市盈率 | - |
listing_date |
String | 是 | 上市日期，格式 YYYY-MM-DD |
- |
max_subscription_shares |
int | 是 | 申购上限 | 股 |
online_shares |
int | 否 | 网上发行数量 | 股 |
pe |
float | 是 | 发行市盈率 | - |
price |
String | 是 | 发行价格 | 元 |
shares |
int | 否 | 发行数量 | 股 |
subscription_date |
String | 是 | 申购日期，格式 YYYY-MM-DD |
- |
subscription_symbol_id |
String | 是 | 申购代码 | - |
注意事项
page和page_size为必填项- 如需全量数据，按
page递增循环请求至total_pages，合并所有items - 可为空字段（如
industry_pe、listing_date等）可能返回null，展示时需做空值处理 price为字符串类型，比较或计算前需转为数值
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 First seen · 93 lines · 50 tokens per session scan A 5e28ef5e61b7
stock-ipos is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (64 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 968 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 2026-09-03.
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…