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-goodwill-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/stock-goodwill-detail)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/stock-goodwill-detail"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-goodwill-detail/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-goodwill-detail"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-goodwill-detail.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 | $0.00048 | $0.00808 |
| Opus 5 | $0.00024 | $0.00404 |
| Sonnet 5 | $0.00010 | $0.00162 |
| Haiku 4.5 | $0.00005 | $0.00081 |
Grade A, and why
stock-goodwill-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 9d 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
查询个股商誉明细
接口说明
| 项目 | 说明 |
|---|---|
| 接口名称 | 查询个股商誉明细 |
| 外部接口 | GET /api/v1/market/data/goodwill/stock-detail |
| 请求方式 | GET |
| 适用场景 | 查询个股的商誉规模、商誉占净资产比例、净利润同比变化等明细数据 |
请求参数
说明：date 为必填项。
| 参数名 | 类型 | 是否必填 | 描述 | 取值示例 | 备注 |
|---|---|---|---|---|---|
| date | string | 是 | 报告期日期 | 20251231 | 格式 YYYYMMDD，按年份范围过滤，如 20251231 查询 2025 全年 |
执行方式
python scripts/handler.py --date 20251231
响应结构
{
"code": 0,
"message": "success",
"data": {
"pageNum": 1,
"pageSize": 50,
"total": 2676,
"pages": 54,
"records": [
{
"seq": 0,
"security_code": "300860",
"security_name": "锋尚文化",
"goodwill_scale": "28235811.5900",
"goodwill_to_net_assets_ratio": "0.00898123",
"net_profit_scale": "-16592410.5300",
"net_profit_yoy_ratio": "-1.39720928",
"goodwill_previous": "28235811.5900",
"notice_date": "2026-05-13 00:00:00",
"trade_board": "cyb"
}
]
}
}
字段说明（GoodwillStockDetailItem）
| 字段名 | 类型 | 是否可为空 | 说明 |
|---|---|---|---|
| seq | int | 否 | 序号 |
| security_code | string | 否 | 证券代码 |
| security_name | string | 是 | 证券简称 |
| goodwill_scale | string | 是 | 商誉规模（元） |
| goodwill_to_net_assets_ratio | string | 是 | 商誉占净资产比例 |
| net_profit_scale | string | 是 | 净利润规模（元） |
| net_profit_yoy_ratio | string | 是 | 净利润同比变化率 |
| goodwill_previous | string | 是 | 上期商誉（元） |
| notice_date | string | 是 | 公告日期，格式 YYYY-MM-DD HH:MM:SS |
| trade_board | string | 是 | 交易板块（sh/sz/cyb/star/bj/hk） |
注意事项
- date 格式为 YYYYMMDD，按年份范围过滤（如 20251231 查询 2025 全年数据）
- 金额字段均为 Decimal 类型，以字符串形式返回以保持精度
- notice_date 大部分常规商誉记录为 null，仅减值相关记录有公告日期
- trade_board 取值：sh（上交所主板）、sz（深交所主板）、cyb（创业板）、star（科创板）、bj（北交所）、hk（港股）
调用示例
python <RUN_PY> stock-goodwill-detail --date 20260828
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.
- 9d ago First seen · 87 lines · 48 tokens per session scan A 336dca19da36
stock-goodwill-detail is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (64 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 808 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.
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