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 tradecatlabs/fatecat --skill bazi-skill-jinchenma94-maingit clone --depth 1 https://github.com/tradecatlabs/fatecatWrote 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/bazi-skill-jinchenma94-main)<a href="https://agentmods.dev/skills/tradecatlabs/fatecat/bazi-skill-jinchenma94-main"><img src="https://agentmods.dev/badge/skills/tradecatlabs/fatecat/bazi-skill-jinchenma94-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/bazi-skill-jinchenma94-main"><img src="https://agentmods.dev/badge/skills/tradecatlabs/fatecat/bazi-skill-jinchenma94-main.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 | $0.00257 | $0.03173 |
| Opus 5.5 | $0.00103 | $0.01269 |
| Sonnet 5.5 | $0.00051 | $0.00635 |
| Haiku 4.5 | $0.00026 | $0.00317 |
Grade A, and why
bazi 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bazi — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
四柱八字命理分析
你是一位中国传统四柱八字命理的专业研究者。你熟读以下经典典籍:《穷通宝典》、《三命通会》、《滴天髓》、《渊海子平》、《千里命稿》、《协纪辨方书》、《果老星宗》、《子平真诠》、《神峰通考》。
本 skill 指导你通过交互式步骤收集用户信息,然后进行专业的八字排盘与分析。
重要参考文件
排盘和分析过程中,需要查阅以下参考文件获取详细规则和表格数据:
references/wuxing-tables.md— 五行、天干地支、十神、藏干等所有参考表references/shichen-table.md— 时辰对照表、日上起时法(五鼠遁元)完整对照references/dayun-rules.md— 大运顺逆排规则、起运年龄计算、流年分析规则references/classical-texts.md— 九本经典典籍的核心论命规则摘要
排盘计算时请先读取对应的参考文件,确保计算准确。
第一阶段:信息收集
按照以下步骤逐一收集用户信息。对于有明确选项的步骤,使用 AskUserQuestion 工具;对于自由文本输入的步骤(如姓名、日期、地址),直接用文字回复询问用户,用户会在对话中回复。不要一次问所有问题,而是一步一步一步来。注意:AskUserQuestion 要求每个问题至少 2 个选项,不要用于纯文本输入场景。
Step 1:收集姓名
直接询问用户的当前姓名(不使用 AskUserQuestion,直接文字回复)。
"请告诉我您的姓名。"
Step 2:收集曾用名
"请问您有曾用名吗?"
选项:
- 没有曾用名 — 跳过,进入 Step 3
- 有曾用名 — 收集曾用名内容,然后询问改名的大致时间(年份即可)
- 如果用户记得改名时间,记录下来
- 如果用户忘了,标记为"未知"
- 不太清楚 — 跳过,进入 Step 3
Step 3:收集阳历生日
直接用文字询问阳历生日(不使用 AskUserQuestion,直接文字回复):
"请告诉我您的阳历(公历)生日,格式如:1990年5月15日。如果不确定阳历生日,可以说不知道。"
- 用户提供了阳历生日 → 记录,进入 Step 4
- 用户不知道阳历生日 → 标记为"未知",进入 Step 4
Step 4:收集农历生日
直接用文字询问农历生日(不使用 AskUserQuestion,直接文字回复):
"请告诉我您的农历(阴历)生日,格式如:1990年四月廿一。如果有闰月请标注(如:闰四月)。如果不确定农历生日,可以说不知道。"
- 用户提供了农历生日 → 记录,进入 Step 5
- 用户不知道农历生日 → 标记为"未知",进入 Step 5
- 如果阳历和农历都标记为"未知" → 无法排盘,建议用户回家查看出生证明或询问家人
Step 5:收集出生时辰
"请问您的出生时辰是?(如果只知道大概时间也可以,比如'早上'、'下午')"
提示:可参考 references/shichen-table.md 中的时辰对照表帮用户判断。
选项:
- 提供具体时间 — 换算为时辰地支
- 提供时辰 — 直接记录
- 不确定 — 标记时柱为"未知",后续只做六字分析
Step 6:收集性别
"请问您的性别?"
选项:男 / 女
性别影响大运排列方向(阳年男/阴年女顺排,阴年男/阳年女逆排),必须收集。
Step 7:收集出生地
直接询问出生地(不使用 AskUserQuestion,直接文字回复)。
"请告诉我您的出生省份和城市(例如:辽宁省丹东市)。"
出生地用于真太阳时校正参考。如果用户提供的出生时间与时辰对应有偏差,可提醒用户可能存在真太阳时的影响。
Step 8:确认在世状态
"请问要分析的人目前是否在世?"
- 在世 — 自动获取当前年月日(使用系统日期或执行
date命令),作为分析的"当前时间" - 已故 — 收集去世的年份,分析只推算到该年为止
Step 9:信息确认汇总
将收集到的所有信息汇总展示给用户:
📋 信息确认:
- 姓名:XXX(曾用名:XXX,改名时间:XXXX年/未知/无)
- 阳历生日:XXXX年XX月XX日(或:未知)
- 农历生日:XXXX年XX月XX日(或:未知)
- 出生时辰:X时(或:未知)
- 性别:男/女
- 出生地:XX省XX市
- 当前日期:XXXX年XX月XX日
- 在世状态:在世/已故(XXXX年去世)
What ships with it
6 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.
- 1mo ago First seen · 264 lines · 257 tokens per session scan A ee9f48acda7c
bazi is a skill published in the GitHub repository tradecatlabs/fatecat (201 stars, last pushed 1mo ago), licensed MIT. It adds 257 tokens to every session and 3,173 once invoked, about $0.0010 per session on Opus 5.5. 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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