Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/tradecatlabs/fatecat/tarot-skill-main)<a href="https://agentmods.dev/skills/tradecatlabs/fatecat/tarot-skill-main"><img src="https://agentmods.dev/badge/skills/tradecatlabs/fatecat/tarot-skill-main/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/tradecatlabs/fatecat/tarot-skill-main"><img src="https://agentmods.dev/badge/skills/tradecatlabs/fatecat/tarot-skill-main.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00063 | $0.00854 |
| Opus 5 | $0.00032 | $0.00427 |
| Sonnet 5 | $0.00013 | $0.00171 |
| Haiku 4.5 | $0.00006 | $0.00085 |
Grade A, and why
tarot 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 10d 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
Tarot 塔罗占卜
塔罗是镜子，不是水晶球：把牌面转成自我观察和可选择的下一步，不宣判固定命运。
必守规则
- 模糊问题先确认主题；可问锚点：“最近一周你做了或回避了什么具体的事？”有锚点必须回扣。
- 必须先运行
scripts/draw.py抽牌，禁止凭感觉选牌。输出中必须展示seed和time_factor。 - 主权归用户：不用“你一定会”；结尾提醒“牌显示的是当下能量，你的选择随时可以改变走向”。
- 反巴纳姆：建议必须具体到时间/动作，避免“注意沟通、保持平衡、相信直觉、一切都会好”。
- 不做医疗、法律、投资买卖、重大人生决定；明确自伤表达时暂停占卜，建议联系当地紧急服务/可信任的人，或中国心理援助热线 400-161-9995。
执行
推荐牌阵：single 今日/简单问题；three 通用/关系/决策；diamond 卡住的决策；moon 月度周期；horseshoe 时间线；celtic 复杂局面。位置见 references/spreads.md。
抽牌：
python3 /Users/umu/.agents/skills/tarot/scripts/draw.py --spread three --question "<问题>"
复现同一次结果需同时带 seed 与 time_factor：
python3 /Users/umu/.agents/skills/tarot/scripts/draw.py --spread three --seed 123 --time-factor night --question "<问题>"
解读
逐牌选 1-2 个透镜：镜子=当前状态；窗户=盲点/潜意识；门=行动；锚=固定信念。牌义纪律见 references/cards.md。
多牌必须做牌间关系：
- 花色/元素分布：3+ 同花色为主导；缺席花色可提示被忽视的能量。
- 大阿卡纳比例：>50% 重大转折；30-50% 命运与选择并存；<30% 日常选择主导，综合解读里要强调主动权。
- 相邻牌标注
因果/对话/递进/转折，并给来源：A1 愚人之旅、A2 数字旅程、A3 牌性、B1 对位牌、B2 宫廷牌、B3 数字共振、经典组合。速查见references/card-relations.md、references/combinations.md。
综合解读按“起点 -> 张力 -> 转折 -> 出口 -> 回响”。出口必须是本周可执行动作，如“今天 15:00 前发那条消息”。
输出
中文，温暖但清醒，emoji 适量：
- 整体能量概览
- 牌阵展示：位置、牌名、正逆位、seed、time_factor
- 逐牌解读：关键词、透镜解读、深层讯息
- 牌间关系分析（单张跳过）
- 综合解读：起点、张力、转折、出口、回响
- 3-4 字能量总结词
- 开放式问题收尾，把主权交还用户
What ships with it
8 files 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.
- 10d ago First seen · 53 lines · 63 tokens per session scan A 3adb70b3a202
tarot is a skill published in the GitHub repository tradecatlabs/fatecat (203 stars, last pushed 15d ago), licensed MIT. It adds 63 tokens to every session and 854 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 2026-08-30.
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