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 qimen-dunjiagit 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/qimen-dunjia)<a href="https://agentmods.dev/skills/tradecatlabs/fatecat/qimen-dunjia"><img src="https://agentmods.dev/badge/skills/tradecatlabs/fatecat/qimen-dunjia/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/qimen-dunjia"><img src="https://agentmods.dev/badge/skills/tradecatlabs/fatecat/qimen-dunjia.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 | $0.00114 | $0.02789 |
| Opus 5 | $0.00057 | $0.01394 |
| Sonnet 5 | $0.00023 | $0.00558 |
| Haiku 4.5 | $0.00011 | $0.00279 |
Grade A, and why
qimen-dunjia 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 11d 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 — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
奇门遁甲技能
本技能面向普通求测者,默认使用 mainline-cn-v1 规则集。工作方式是:
- 先访谈,确认用户到底要看什么、看哪一刻、想判断什么。
- 再用脚本做固定计算,不靠心算排盘。
- 最后再用奇门规则做解读、建议和风险提醒。
不要跳过访谈,不要在信息不完整时硬算,也不要把完整内部推理链直接展示给用户。
触发范围
以下场景应使用本技能:
- 用户要求正式奇门排盘或解盘。
- 用户要求用奇门判断一件具体事情能不能成、何时动、往哪边走、避开什么。
- 用户要求奇门择时、方位选择、趋吉避凶建议。
- 用户要求讲解奇门理论、格局、用神、盘例。
以下场景不要直接进入正式排盘:
- 用户只是在闲聊玄学,没有明确要用奇门。
- 用户信息太少,连事情类型和时间都没给。
- 用户要求其他流派,而当前规则集不支持。
总原则
- 默认规则集固定为
mainline-cn-v1,不要在正式排盘路径里混用其他流派。 - 正式排盘前必须先访谈。
- 固定计算一律调用
scripts/qimen_cli.py。 - 不展示完整推理链,只展示关键依据和必要计算结果。
- 重大决策、疾病、法律、投资等高风险主题,必须附现实建议。
- 不用恐吓式语言,不说“必败”“必死”“无救”。
默认规则
当前内置规则固定如下:
- 体系:时家转盘奇门
- 默认时区:
Asia/Shanghai - 默认适用区域:中国大陆优先
- 定局:置闰法工程化实现
- 中宫/寄宫:中宫相关判断一律寄坤处理
详细规则见 references/ruleset-mainline.md。
如果用户明确要求别的流派:
- 先直说当前技能默认使用
mainline-cn-v1。 - 问用户是否接受先按这套规则排。
- 如果不接受,不进入正式排盘,只做理论讨论或说明当前版本不支持。
工作流
第 1 步:先访谈
正式排盘前,必须先做两段式访谈。
先问第一轮核心问题,语言要直白:
- 你要看什么事?一句话说清。
- 事情对应的时间是什么?如果就是现在,直接说“现在”。
- 你人在哪个城市?如果不在中国大陆,请直接说国家/城市。
- 你最想判断什么?比如能不能成、什么时候动、选哪边、要避开什么。
- 这件事现在进展到哪一步了?
- 你要“直接结论”还是“详细讲解”?
第二轮按条件追问:
- 只有日期,没有具体时辰:补问具体小时,至少补到时辰。
- 给的是农历:补问是否闰月。
- 人在海外:补问时区或城市。
- 问题太泛:补问“你最想判断哪一个结果”。
- 高风险主题:补问是否也需要现实建议,并提醒医生、律师、财务顾问等专业帮助。
访谈模板见 references/interview.md。
第 2 步:决定是否进入正式排盘
只有在以下信息确认后,才进入正式排盘:
- 事项类型明确
- 时间明确
- 地点或时区明确到可计算
- 判断目标明确
如果没收齐,只继续追问,不要先排盘。
如果用户只是想学习理论:
- 先问他想学什么。
- 不直接进入正式排盘。
- 可以结合
references/examples.md做教学。
第 3 步:调用脚本做固定计算
前置:确认依赖已安装
执行正式排盘前先检查依赖。如果未安装,先运行:
pip install "lunar_python>=1.4.8,<2" "tzdata>=2024.1"
执行脚本
脚本路径以仓库根目录为基准:
python "qimen-dunjia/scripts/qimen_cli.py" \
--input "tmp/qimen_input.json" \
--output "tmp/qimen_output.json"
先把输入 JSON 写入 tmp/qimen_input.json,再执行上述命令,读取 tmp/qimen_output.json。tmp/ 也可以替换成任意可写的临时目录。
输入 JSON 最低字段:
{
"question_type": "",
"question_goal": "",
"time_input": "",
"calendar_type": "solar|lunar|now",
"location": {
"country": "",
"city": "",
"timezone": ""
},
"ruleset": "mainline-cn-v1"
}
What ships with it
7 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.
- 11d ago First seen · 306 lines · 114 tokens per session scan A d32031c3b501
qimen-dunjia is a skill published in the GitHub repository tradecatlabs/fatecat (203 stars, last pushed 16d ago), licensed MIT. It adds 114 tokens to every session and 2,789 once invoked, about $0.0006 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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