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 ascend-moe-optimizer-trace-analyzergit 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/ascend-moe-optimizer-trace-analyzer)<a href="https://agentmods.dev/skills/xiaoluolyg/god/ascend-moe-optimizer-trace-analyzer"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/ascend-moe-optimizer-trace-analyzer/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/ascend-moe-optimizer-trace-analyzer"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/ascend-moe-optimizer-trace-analyzer.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 | $0.00115 | $0.03175 |
| Opus 5.5 | $0.00046 | $0.01270 |
| Sonnet 5.5 | $0.00023 | $0.00635 |
| Haiku 4.5 | $0.00012 | $0.00317 |
Grade A, and why
ascend-moe-optimizer-trace-analyzer 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.
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ascend MoE 性能 Trace 分析
分析 Chrome/Perfetto 风格的 trace.json，把原始 trace event 转换为结构化统计表、图表和 Markdown 报告，用于替代人工在 Perfetto 中做第一轮耗时分布和瓶颈定位。本 skill 的内置名称为 ascend-moe-optimizer-trace-analyzer；当前目录为 ascend-moe-optimizer-trace-analyzer。
何时使用
- 用户需要分析 算子或 runtime 打点 导出的 Chrome/Perfetto
trace.json，关注 phase 分布、category、Ascend core group、线程 tid、overlap、bubble。 - 调优 Ascend 上 MoE / FusedDeepMoe（如
fused_deep_moe） 或需沿用本仓库默认config/phase_map.yaml的场景。 - 需要 确定性自动诊断，或可选的
--llm-analysis二次解读。
脚本位置
- 用户安装后的 skill 根目录：
<ASCEND_MOE_OPTIMIZER_SKILL>=~/.jiuwenclaw/agent/jiuwenclaw_workspace/skills/ascend-moe-optimizer-trace-analyzer - 入口：
<ASCEND_MOE_OPTIMIZER_SKILL>/app.py - 从本仓库资源运行时，将上述路径换为
jiuwenclaw/resources/agent/jiuwenclaw_workspace/skills/ascend-moe-optimizer-trace-analyzer（相对仓库根目录）。
执行命令前请先 cd 到 <ASCEND_MOE_OPTIMIZER_SKILL>，或使用下文绝对路径形式的 python3 .../app.py。
能力概览
本 skill 面向的核心对象是 trace.json，不是某一个固定算子。它本身负责：
- 解析 trace 中的完整区间事件。
- 将原始 trace name 映射为可稳定统计的 phase。
- 按 phase、category、core group、tid、raw name 聚合耗时。
- 计算 phase overlap 和外层阶段 bubble。
- 生成统计图、文字化统计摘要和 Markdown 报告。
- 生成稳定、可复现的自动诊断。
- 可选调用外部 LLM，把统计上下文扩写成专家分析段落。
当前仓库默认携带的 config/phase_map.yaml 和部分诊断规则来自 UMDK FusedDeepMoe trace 的实践经验。因此，默认配置对 FusedDeepMoe 最友好；如果要分析其他来源的 trace，应替换或扩展 phase/category 映射配置，并逐步沉淀对应领域的诊断规则。
Agent 执行原则
执行本 skill 时，agent 不应把文档中的示例路径当成固定输入。应先从用户请求或当前工作区中确认以下上下文，并把它们替换到命令中：
TRACE_JSON：必需，用户要分析的 trace 文件。OUTPUT_DIR：必需或由 agent 选择，建议按本次任务命名，例如output/<case_name>。PHASE_MAP：可选，phase/category 映射配置。若用户指定算子或已有对应配置，应使用对应配置；否则使用默认config/phase_map.yaml。SOURCE_ROOT：可选，算子源码工程目录，例如某个 UMDK 工程。当前 CLI 尚未消费该参数，但 agent 可以用它阅读源码、理解打点语义和辅助维护 phase map。OPERATOR：可选，用户指定的算子名，例如fused_deep_moe。当前 CLI 尚未消费该参数，但 agent 应用它选择或维护对应的 phase/category 规则和诊断上下文。
如果用户只提供 trace.json，按 trace-only 模式分析。如果用户同时提供源码目录和算子名，agent 应先阅读相关源码打点，再决定是否需要补充或调整 PHASE_MAP。
执行命令
在 <ASCEND_MOE_OPTIMIZER_SKILL> 目录下执行（以下 <ASCEND_MOE_OPTIMIZER_SKILL> 含义见「脚本位置」）：
What ships with it
10 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.
- analyzers/__init__.py 0 B runs code
- analyzers/diagnosis.py 10 KB runs code
- analyzers/llm_analysis.py 7.8 KB runs code
- analyzers/metrics.py 19 KB runs code
- analyzers/parser.py 4.1 KB runs code
- analyzers/phase_mapper.py 3.6 KB runs code
- analyzers/plots.py 5.5 KB runs code
- analyzers/reporter.py 14 KB runs code
- app.py 11 KB runs code
- config/phase_map.yaml 7.3 KB
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 · 218 lines · 115 tokens per session scan A 5ff28d84022a
ascend-moe-optimizer-trace-analyzer is a skill published in the GitHub repository XiaoLuoLYG/GOD (1,235 stars, last pushed 2d ago), licensed Apache-2.0. It adds 115 tokens to every session and 3,175 once invoked, about $0.0005 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 2026-09-03.
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