GOD is a control room for observing and directing societies of language-model agents running in simulated worlds. It lets researchers inspect replays, question individual agents, alter future events, reset simulations, and export experiments for reuse. The catalogue entries are skills and agents for operating and investigating these simulations.
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 XiaoLuoLYG/GOD --skill deepep-to-cam-convertergit clone --depth 1 https://github.com/XiaoLuoLYG/GODWrote 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/xiaoluolyg/god/deepep-to-cam-converter)<a href="https://agentmods.dev/skills/xiaoluolyg/god/deepep-to-cam-converter"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/deepep-to-cam-converter/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/xiaoluolyg/god/deepep-to-cam-converter"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/deepep-to-cam-converter.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 | $0.00063 | $0.03040 |
| Opus 5 | $0.00032 | $0.01520 |
| Sonnet 5 | $0.00013 | $0.00608 |
| Haiku 4.5 | $0.00006 | $0.00304 |
Grade A, and why
deepep-to-cam-converter 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.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CAM算子替换专家技能
技能概述
本技能专注于将基于DeepEP的Mixture of Experts (MoE) 代码迁移至昇腾(Ascend)NPU环境。它能自动识别DeepEP dispatch & combine算子,**严格基于代码的实际运行参数(而非默认值)**校验CAM算子约束。
核心原则:
- 思维链先行:在执行任何代码修改前,必须先输出分析过程和自检结果。严禁直接生成代码。
- 原地修改:默认直接在用户指定的原文件上修改,严禁创建新文件(除非用户显式要求)。
- 动态值优先:判断约束时,必须追踪参数的实际运行时值,严禁直接使用代码中的硬编码默认值。
- 强制交互:在算子模式选择(如A3普通 vs Shmem)和功能支持度处理上,必须询问用户,严禁AI自作主张。
工作流程
1. 代码扫描与依赖分析
1.1 目标检测
- DeepEP识别:扫描代码中是否存在
import deep_ep及dispatch/combine相关调用。 - MOE/EP模式识别:确认代码是否涉及Mixture of Experts或Expert Parallelism逻辑。
- 无匹配处理:若未检测到相关代码,直接告知用户并终止流程。
1.2 依赖与上下文分析
- 导入分析:提取所有
import语句,识别本地模块依赖。 - 通信域扫描:检查是否存在
dist.init_process_group、backend='nccl'、torch.cuda.set_device等特征代码。 - 外部依赖检查:识别是否调用了外部初始化函数(如
utils.init_dist),需同步分析这些函数的实现。
🛑 阶段 1 阻断检查(必须显式输出) 在生成任何回复前,请先输出以下内容:
[阶段 1 自检] - DeepEP 代码识别:[是/否] - 本地依赖文件列表:[列出文件] - 状态:[通过/失败]若“DeepEP 代码识别”为否,直接终止。若为是,继续下一阶段。
2. 环境与策略决策
注意:此阶段必须与用户交互,不得跳过。
2.1 环境选择
向用户展示以下选项,确定目标运行环境:
- 选项1:基于当前环境自动检测(尝试执行
npu-smi info,根据设备型号判断是A2环境或A3环境)。 - 选项2:指定目标环境(用户手动指定A2环境或A3环境,用于交叉编译或代码预研场景)。
2.2 参数值解析与约束校验
重要原则:代码中的默认值(Default Value)≠ 实际运行值(Runtime Value)
在执行替换前,必须对约束条件涉及的参数进行实际值溯源:
-
参数溯源:
- 检查参数是否通过
argparse、config.yaml或命令行传入。 - 严禁使用
parser.add_argument(..., default=XXX)中的XXX作为判断依据。 - 必须假设代码中的默认值可能不是实际运行值。
- 检查参数是否通过
-
交互确认机制:
- 对于关键约束参数(如 hidden_size, top_k, expert_num 等),如果无法从代码逻辑中100%确定其运行时赋值,必须向用户发起询问:
“检测到参数 [参数名] 在代码中的默认值为 [默认值],但这可能不是实际运行值。请确认实际运行时的数值是多少?以便准确判断是否满足CAM算子约束。”
- 对于关键约束参数(如 hidden_size, top_k, expert_num 等),如果无法从代码逻辑中100%确定其运行时赋值,必须向用户发起询问:
-
约束校验逻辑:
- 情况A(值确定且满足):用户确认实际值,且满足CAM算子约束 -> 进入算子模式选择。
- 情况B(值确定但不满足):用户确认实际值,不满足约束 -> 停止替换,告知用户不支持。
- 情况C(值不确定):用户未提供 -> 暂停替换,标记该处需人工确认。
2.3 算子模式选择
在确认参数值满足约束后,根据目标环境确定具体算子模式:
- A2环境:仅使用 CAM A2 算子。
- A3环境:
- 检查代码是否同时满足 普通A3算子、 Shmem算子 和fused deep moe算子的约束条件。
- 注意fused deep moe算子替换有严格的约束范式,请仔细阅读说明文档,检查是否满足范式,需要基于dispatch & combine函数去查找调用链,查看最终调用方是否满足约束范式
- 阻断规则:如果有多个都满足,AI必须立即停止自动流程,并向用户展示选择题,等待用户回复,以三个条件都满足的场景为例,可以展示如下选项
检测到当前代码同时满足多组算子的约束条件,请做出选择:()
- 选项1(普通A3):参数配置简单,无需额外共享内存初始化。
- 选项2(Shmem A3):性能更优,但需满足特定约束及初始化。
- 选项2(fused deep moe算子):性能最优,将通信计算过程融合为一个大算子。 请回复选项继续。
- 如果仅满足其一:自动选择该模式并告知用户。
What ships with it
5 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.
- 8d ago First seen · 168 lines · 63 tokens per session scan A 4e729708335c
deepep-to-cam-converter is a skill published in the GitHub repository XiaoLuoLYG/GOD (1,107 stars, last pushed 15d ago), licensed Apache-2.0. It adds 63 tokens to every session and 3,040 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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