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 report-announcement-listgit 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/report-announcement-list)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/report-announcement-list"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/report-announcement-list/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/report-announcement-list"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/report-announcement-list.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 | $0.00079 | $0.01057 |
| Opus 5 | $0.00039 | $0.00528 |
| Sonnet 5 | $0.00016 | $0.00211 |
| Haiku 4.5 | $0.00008 | $0.00106 |
Grade A, and why
report-announcement-list 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 7d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
查询报告公告列表
接口说明
| 项目 | 说明 |
|---|---|
| 接口名称 | 查询报告公告列表 |
| 外部接口 | GET /api/v1/market/data/report-announcements/list |
| 请求方式 | GET |
| 适用场景 | 按公告日期分页查询报告公告，可选按证券代码过滤；公告 ID 可用于查询公告摘要 |
同一处理逻辑的兼容入口还包括
/api/v1/market/data/report-announcement/list。
请求参数
| 参数名 | 类型 | 是否必填 | 描述 | 取值示例 | 备注 |
|---|---|---|---|---|---|
| date | string | 是 | 公告日期 | 20260714 |
YYYYMMDD 或 YYYY-MM-DD |
| sec_code | string | 否 | 证券代码 | 600000 |
不传返回当天全部证券公告 |
| page | int | 否 | 页码 | 1 |
从 1 开始，默认 1 |
| page_size | int | 否 | 每页数量 | 50 |
默认 50，最大 200 |
执行方式
# 某日某证券公告
python <RUN_PY> report-announcement-list --date 20260714 --sec_code 600000 --page 1 --page_size 20
# 某日全部公告并翻全量
python <RUN_PY> report-announcement-list --date 20260714 --all
<RUN_PY> 为主 SKILL.md 同级的 run.py 绝对路径。
响应结构
返回 code/message/data，分页数据位于 data.records。
{
"code": 200,
"message": "success",
"data": {
"pageNum": 1, "pageSize": 20, "total": 50, "pages": 3,
"records": [
{
"id": 12345,
"announcement_id": "AN202607140001",
"url_hash": "...",
"sec_code": "600000",
"sec_name": "浦发银行",
"announcement_title": "...",
"announcement_time": "2026-07-14 09:30:00",
"adjunct_type": "PDF", "adjunct_size": 102400,
"column_type": "monthly", "plate": "sh",
"status": "summarized", "retry_count": 0,
"created_at": "2026-07-14 09:30:00",
"updated_at": "2026-07-14 10:00:00",
"processed_at": "2026-07-14 10:01:00"
}
]
}
}
data 字段
| 字段 | 类型 | 说明 |
|---|---|---|
| pageNum | int | 当前页码 |
| pageSize | int | 当前每页条数 |
| total | int | 满足条件的总记录数 |
| pages | int | 总页数；无记录时为 0 |
| records | array | 当前页公告记录 |
records 元素字段
| 字段 | 类型 | 说明 |
|---|---|---|
| id | int64/null | 数据库记录 ID |
| announcement_id | string/null | 公告 ID |
| url_hash | string/null | 公告附件哈希 |
| sec_code / sec_name | string/null | 证券代码 / 证券名称 |
| announcement_title | string/null | 公告标题 |
| announcement_time | string/null | 公告时间 |
| adjunct_type | string/null | 附件类型 |
| adjunct_size | int64/null | 附件大小 |
| column_type | string/null | 公告栏目类型 |
| plate | string/null | 所属板块 |
| status | string/null | 处理状态 |
| retry_count | int64/null | 重试次数 |
| created_at / updated_at / processed_at | string/null | 创建 / 更新 / 处理时间 |
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.
- 7d ago First seen · 104 lines · 79 tokens per session scan A fd64c41ab744
report-announcement-list is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (63 stars, last pushed today), licensed MIT. It adds 79 tokens to every session and 1,057 once invoked, about $0.0004 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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