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 major-contract-by-dategit 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/major-contract-by-date)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/major-contract-by-date"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/major-contract-by-date/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/major-contract-by-date"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/major-contract-by-date.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 | $0.00042 | $0.00797 |
| Opus 5 | $0.00021 | $0.00398 |
| Sonnet 5 | $0.00008 | $0.00159 |
| Haiku 4.5 | $0.00004 | $0.00080 |
Grade A, and why
major-contract-by-date 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 6d 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.
What it actually says
重大合同 - 按公告日期范围查询
1. 接口描述
| 项目 | 说明 |
|---|---|
| 接口名称 | 按公告日期范围查询重大合同 |
| 外部接口 | GET /api/v1/market/data/corporate/contract |
| 请求方式 | GET |
| 适用场景 | 按公告日期范围查询 A 股上市公司重大合同，包括合同方、金额、收入影响等 |
2. 请求参数
| 参数名 | 类型 | 是否必填 | 描述 | 取值示例 | 备注 |
|---|---|---|---|---|---|
| start_date | string | 是 | 开始日期 | 20260526 |
格式 YYYYMMDD |
| end_date | string | 是 | 结束日期 | 20260526 |
格式 YYYYMMDD |
3. 响应说明
返回 ApiResponse 包装的 JSON 对象。
{
"code": 0,
"message": "success",
"data": {
"total": 2,
"items": [
{
"seq": 0,
"security_code": "300209",
"security_short_name": "行云科技",
"signatory": "行云存算(深圳)科技有限公司",
"signatory_rel_name": "控股子公司",
"counter_party": "浙江甚湖科技有限公司",
"counter_party_rel_name": "无关联关系",
"contract_type_name": "销售合同",
"contract_name": "设备销售框架协议",
"amounts": "321750000.0000",
"snd_yysr": null,
"zsnd_yysr_bl": null,
"operate_reve": null,
"sign_date": null,
"dim_rdate": "2026-05-26"
}
]
}
}
data.items 元素结构
| 字段名 | 类型 | 说明 |
|---|---|---|
| seq | int | 序号 |
| security_code | string | 股票代码 |
| security_short_name | string | 股票简称 |
| signatory | string | 签约方 |
| signatory_rel_name | string | 签约方关联名称 |
| counter_party | string | 对方签约方 |
| counter_party_rel_name | string | 对方签约方关联名称 |
| contract_type_name | string | 合同类型名称 |
| contract_name | string | 合同名称 |
| amounts | string | 合同金额（元） |
| snd_yysr | string | 上年度营业收入 |
| zsnd_yysr_bl | string | 占上年度营业收入比例 |
| operate_reve | string | 最近营业收入 |
| sign_date | string | 签约日期 |
| dim_rdate | string | 公告日期 |
4. 用法
通过主目录 run.py 调用：
python <RUN_PY> major-contract-by-date --start_date 20260526 --end_date 20260526
5. 请求示例
GET /api/v1/market/data/corporate/contract?start_date=20260526&end_date=20260526
6. 注意事项
- 金额字段为 Decimal 类型，以字符串返回保持精度。
- 按
dim_rdate降序排列。
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.
- 6d ago First seen · 95 lines · 42 tokens per session scan A 9e0e28d9b191
major-contract-by-date is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (63 stars, last pushed 2d ago), licensed MIT. It adds 42 tokens to every session and 797 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 2026-09-03.
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…