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-predictgit 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-predict)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/stock-goodwill-predict"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-goodwill-predict/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-predict"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-goodwill-predict.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 | $0.00054 | $0.00901 |
| Opus 5 | $0.00027 | $0.00451 |
| Sonnet 5 | $0.00011 | $0.00180 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade A, and why
stock-goodwill-predict 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
查询商誉减值预期明细
接口说明
| 项目 | 说明 |
|---|---|
| 接口名称 | 查询商誉减值预期明细 |
| 外部接口 | GET /api/v1/market/data/goodwill/predict |
| 请求方式 | GET |
| 适用场景 | 查询商誉减值业绩预告数据，包含预测净利润上下限、业绩变动幅度、上年同期净利润等字段 |
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": 820,
"pages": 17,
"records": [
{
"seq": 0,
"security_code": "601211",
"security_name": "国泰海通",
"perform_change_explain": "业绩变动原因说明...",
"predict_period": "20251231",
"newest_goodwill": "10000000000.0000",
"goodwill_previous": "9500000000.0000",
"predict_netprofit_lower": "50000000000.0000",
"predict_netprofit_upper": "60000000000.0000",
"perform_change_lower": "10.50000000",
"perform_change_upper": "20.50000000",
"pe_samereport_netprofit": "45000000000.0000",
"notice_date": "2025-04-01 00:00:00",
"trade_market": "主板"
}
]
}
}
字段说明（GoodwillPredictItem）
| 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 |
| perform_change_explain | string | 是 | 业绩变动原因 |
| predict_period | string | 是 | 预告周期 |
| newest_goodwill | string | 是 | 最新商誉（元） |
| goodwill_previous | string | 是 | 上期商誉（元） |
| predict_netprofit_lower | string | 是 | 预测净利润下限（元） |
| predict_netprofit_upper | string | 是 | 预测净利润上限（元） |
| perform_change_lower | string | 是 | 业绩变动下限（%） |
| perform_change_upper | string | 是 | 业绩变动上限（%） |
| pe_samereport_netprofit | string | 是 | 上年同期净利润（元） |
| notice_date | string | 是 | 公告日期，格式 YYYY-MM-DD HH:MM:SS |
| trade_market | string | 是 | 交易市场 |
注意事项
- date 格式为 YYYYMMDD，按年份范围过滤
- 金额字段均为 Decimal 类型，以字符串形式返回以保持精度
- 业绩变动上/下限为百分比值（如 10.5 表示 10.5%）
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 · 94 lines · 54 tokens per session scan A 5c21a70600e4
stock-goodwill-predict is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (64 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 901 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.
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