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 pledge-detailgit 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/pledge-detail)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/pledge-detail"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/pledge-detail.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 19 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00062 | $0.01025 |
| Opus 5 | $0.00031 | $0.00513 |
| Sonnet 5 | $0.00012 | $0.00205 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
pledge-detail 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 4d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
查询单票股权质押个股详细信息
接口说明
| 项目 | 说明 |
|---|---|
| 接口名称 | 查询单票股权质押个股详细信息 |
| 外部接口 | /api/v1/market/data/pledge/pledge-detail |
| 请求方式 | GET |
| 适用场景 | 获取 A 股上市公司单只股票所有报告期的股权质押详细信息，支持沪深京股票，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 |
|---|---|---|---|---|---|
| stock_code | string | 是 | 单个股票代码 | 603323.SH | 支持沪深京股票，A股需为6位数字+后缀（SH=上交所，SZ=深交所，BJ=北交所），单次仅支持一个代码 |
| page | int | 否 | 页码，从 1 开始 | 1 | 默认值为 1，必须大于等于 1 |
| page_size | int | 否 | 每页记录数 | 50 | 默认值为 50，必须大于等于 1 |
执行方式
通过根目录的 run.py 调用（推荐）：
python <RUN_PY> pledge-detail --stock_code 603323.SH
python <RUN_PY> pledge-detail --stock_code 603323.SH --page 2 --page_size 20
<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
接口返回带分页信息的对象：
{
"items": [
{
"trade_code": "603323.SH",
"stock_name": "苏农银行",
"report_date": "2025-09-30",
"pledge_ratio": 12.34,
"pledge_n": 5,
"pledge_market_value": 98765432.0,
"pledge_unlimitn": 1234.56,
"pledge_limitn": 0.0,
"last_year_fluctuation": -5.67
}
],
"total_pages": 1,
"total_items": 21
}
PledgeStockHolder 字段说明
| 字段名 | 类型 | 是否可为空 | 说明 | 单位 |
|---|---|---|---|---|
| trade_code | String | 否 | 股票交易代码，固定携带 .SZ/.SH/.BJ 市场后缀 | - |
| stock_name | String | 否 | 上市公司对应股票名称 | - |
| report_date | String | 否 | 报告日期，固定格式为 YYYY-MM-DD | - |
| pledge_ratio | float | 否 | 质押比例 | % |
| pledge_n | int | 否 | 质押笔数 | 笔 |
| pledge_market_value | float | 否 | 质押市值（质押股数 × 报告日期收盘价） | 元 |
| pledge_unlimitn | float | 否 | 质押无限售股数 | 万股 |
| pledge_limitn | float | 否 | 质押限售股数 | 万股 |
| last_year_fluctuation | float | 是 | 较上年变动（报告日期收盘价与一年前收盘价的涨跌幅），无上年数据时返回 null | % |
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.
- 4d ago First seen · 80 lines · 62 tokens per session scan A f5f48850c881
pledge-detail is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (63 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 1,025 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…