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 advanced-daily-reportgit 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/advanced-daily-report)<a href="https://agentmods.dev/skills/xiaoluolyg/god/advanced-daily-report"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/advanced-daily-report/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/advanced-daily-report"><img src="https://agentmods.dev/badge/skills/xiaoluolyg/god/advanced-daily-report.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 | $0.00032 | $0.02533 |
| Opus 5.5 | $0.00013 | $0.01013 |
| Sonnet 5.5 | $0.00006 | $0.00507 |
| Haiku 4.5 | $0.00003 | $0.00253 |
Grade A, and why
advanced-daily-report 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 — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
进阶版日报生成器
自动采集多源数据，智能分析工作效率，生成日报/周报/月报并推送到飞书。
核心能力
1. 多数据源采集
| 数据源 | 采集内容 | 频率 |
|---|---|---|
| Git 仓库 | 提交记录、代码变更统计 | 实时 |
| 网易邮箱 | 收发邮件统计、未读提醒 | 实时 |
| 记忆系统 | 今日工作记录、长期记忆 | 实时 |
| 待办事项 | 任务状态、完成率 | 实时 |
2. 智能工作分析
-
效率指标计算
- 任务完成率 = 已完成 / 总任务
- 生产力得分（0-100）
- 专注度得分（0-100）
-
趋势对比
- 与昨日对比
- 与上周同期对比
- 周趋势图
-
关键词提取
- 自动提取今日工作关键词
- 工作主题聚类
3. 多报告类型
| 类型 | 触发方式 | 推送时间 |
|---|---|---|
| 日报 | 手动/定时 | 每天 18:00 |
| 周报 | 定时 | 每周五 18:00 |
| 月报 | 定时 | 每月最后一天 18:00 |
目录结构
daily-report/
├── SKILL.md # 技能定义（本文件）
├── collectors/ # 数据采集模块
│ ├── __init__.py
│ ├── git_collector.py # Git 提交采集
│ ├── email_collector.py # 邮件统计采集
│ ├── memory_collector.py # 记忆数据采集
│ ├── todo_collector.py # 待办事项采集
│ └── aggregator.py # 数据聚合器
├── analyzers/ # 分析模块
│ ├── __init__.py
│ └── work_analyzer.py # 工作分析引擎
├── generators/ # 报告生成模块
│ ├── __init__.py
│ └── report_generator.py # 报告生成器
└── report_helper.py # 兼容旧版脚本
使用方式
⚠️ 重要：执行方式
本技能通过执行 Python 脚本来采集数据（Git提交、邮箱邮件、记忆、待办）。
必须使用 bash 工具执行脚本，而不是直接回复用户。
脚本会自动采集以下数据：
- Git 提交记录：通过
git log命令读取D:/Download/jiuwenclaw仓库的提交历史 - 邮箱邮件统计：通过 IMAP 协议连接
.env中配置的邮箱账户读取邮件统计（需要邮箱授权码） - 记忆系统：读取
~/.jiuwenclaw/agent/memory/目录下的每日记忆文件 - 待办事项：读取
~/.jiuwenclaw/agent/sessions/下各会话的todo.md文件
手动触发
当用户请求生成日报/周报/月报时，执行以下命令：
# 生成今日日报（记忆/待办/Git 等；Git 在仓库根目录统计）
python ~/.jiuwenclaw/agent/skills/daily-report/run_report.py daily --save
# 生成指定日期日报
python ~/.jiuwenclaw/agent/skills/daily-report/run_report.py daily --date 2026-03-06 --save
# 生成周报（聚合一周数据）
python ~/.jiuwenclaw/agent/skills/daily-report/run_report.py weekly --save
# 生成月报（聚合一月数据，包含每日Git提交统计）
python ~/.jiuwenclaw/agent/skills/daily-report/run_report.py monthly --save
# 生成月报（指定月份）
python ~/.jiuwenclaw/agent/skills/daily-report/run_report.py monthly --year 2026 --month 3 --save
What ships with it
14 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.
- AI_ANALYSIS_TASKS.md 3.8 KB
- analyzers/__init__.py 706 B runs code
- analyzers/ai_analyzer.py 16 KB runs code
- analyzers/work_analyzer.py 17 KB runs code
- collectors/__init__.py 702 B runs code
- collectors/aggregator.py 9.2 KB runs code
- collectors/email_collector.py 8.7 KB runs code
- collectors/git_collector.py 8.4 KB runs code
- collectors/memory_collector.py 4.7 KB runs code
- collectors/todo_collector.py 7.4 KB runs code
- generators/__init__.py 215 B runs code
- generators/report_generator.py 16 KB runs code
- report_helper.py 12 KB runs code
- run_report.py 27 KB runs code
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 · 297 lines · 32 tokens per session scan A 12519ec9f9b4
advanced-daily-report is a skill published in the GitHub repository XiaoLuoLYG/GOD (1,233 stars, last pushed 2d ago), licensed Apache-2.0. It adds 32 tokens to every session and 2,533 once invoked, about $0.0001 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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