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 etf-adjust-factorgit 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/etf-adjust-factor)<a href="https://agentmods.dev/skills/ftshare-lab/ftshare-skill/etf-adjust-factor"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/etf-adjust-factor/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/etf-adjust-factor"><img src="https://agentmods.dev/badge/skills/ftshare-lab/ftshare-skill/etf-adjust-factor.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 | $0.00059 | $0.00191 |
| Opus 5 | $0.00030 | $0.00096 |
| Sonnet 5 | $0.00012 | $0.00038 |
| Haiku 4.5 | $0.00006 | $0.00019 |
Grade A, and why
etf-adjust-factor 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
ETF复权因子
查询 ETF 复权因子。支持 --symbol、--trade-date 或带 symbol 的 --start-date/--end-date 区间查询，以及 --page/--page-size。接口：GET /api/v1/market/data/etf-adjust-factor。
所有请求必须设置环境变量 FTSHARE_API_KEY；handler 将其作为 FTSHARE_API_KEY 请求头发送。缺失凭据时不会发起请求。
通过主目录 run.py 调用：
python <RUN_PY> etf-adjust-factor
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 · 17 lines · 59 tokens per session scan A 58f94973bb85
etf-adjust-factor is a skill published in the GitHub repository FTShare-Lab/FTShare-skill (63 stars, last pushed 2d ago), licensed MIT. It adds 59 tokens to every session and 191 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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