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-comment-desiregit 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-comment-desire)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/stock-comment-desire"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-comment-desire/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-comment-desire"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/stock-comment-desire.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 | $0.00454 |
| Opus 5 | $0.00021 | $0.00227 |
| Sonnet 5 | $0.00008 | $0.00091 |
| Haiku 4.5 | $0.00004 | $0.00045 |
Grade A, and why
stock-comment-desire 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.
What it actually says
千股千评 — 市场参与意愿
1. 接口描述
| 项目 | 说明 |
|---|---|
| 接口名称 | 市场参与意愿 |
| 外部接口 | GET /api/v1/market/data/stock-comment/desire |
| 请求方式 | GET |
| 适用场景 | 查询指定股票的市场参与意愿及 5 日均值变化 |
2. 请求参数
| 参数名 | 类型 | 是否必填 | 描述 | 取值示例 | 备注 |
|---|---|---|---|---|---|
| symbol | string | 是 | 股票代码 | 000001 |
6 位数字代码，不带交易所后缀 |
3. 响应说明
| 字段名 | 类型 | 是否可为空 | 说明 |
|---|---|---|---|
| data.symbol | string | 否 | 股票代码 |
| data.total | int | 否 | 总记录数 |
| data.items[].trade_date | string | 是 | 交易日期 |
| data.items[].security_code | string | 是 | 股票代码 |
| data.items[].participation_wish | string | 是 | 参与意愿 |
| data.items[].participation_wish_5days | string | 是 | 5 日平均参与意愿 |
| data.items[].participation_wish_change | string | 是 | 参与意愿变化 |
| data.items[].participation_wish_5days_change | string | 是 | 5 日平均参与意愿变化 |
4. 用法
python <RUN_PY> stock-comment-desire --symbol 000001
5. 注意事项
symbol为 6 位数字股票代码，不带交易所后缀。- 意愿指数字段以字符串形式返回以保持精度。
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 · 46 lines · 42 tokens per session scan A 37a9058251ed
stock-comment-desire is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (64 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 454 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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