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 mxyhi/ok-skills --skill deep-researchgit clone --depth 1 https://github.com/mxyhi/ok-skillsWrote 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/mxyhi/ok-skills/deep-research)<a href="https://agentmods.dev/skills/mxyhi/ok-skills/deep-research"><img src="https://agentmods.dev/badge/skills/mxyhi/ok-skills/deep-research/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/mxyhi/ok-skills/deep-research"><img src="https://agentmods.dev/badge/skills/mxyhi/ok-skills/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.09957 |
| Opus 5 | $0.00016 | $0.04979 |
| Sonnet 5 | $0.00006 | $0.01991 |
| Haiku 4.5 | $0.00003 | $0.00996 |
Grade C, and why
deep-research scanned grade C with 1 finding 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
1. 清理中间文件 `{TMPDIR}` 目录：Windows 用 `Remove-Item -Recurse -Force "{TMPDIR}"`，Linux 用 `rm -rf {TMPDIR}` This is a copy
98% identical to deep-research — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 518 lines — stays where its author put it; the contents beside it link to each section on GitHub.
deep-research
生成对标券商/第三方研究机构标准的深度调研报告。
- 架构：主 agent 调度 4 个子 agent Task（大纲/数据/预检/装配）+ 1 轮主控并行派发章节，中间数据走临时文件
- 数据源：在线模式 → 工具内置引擎（Layer 0，如有）+ 大纲建议源定向搜索（Layer 1） + sources.json 优质源搜索（Layer 2）并行 → 按质量触发免费源补强（Layer 3 兜底）→ Scrapling 批量抓取；离线模式 → 用户指定的本地文件（md/txt/pdf/docx）
- 安装：见下方「安装与配置」
- 输出：
$TMPDIR/outline.json（临时，非最终报告） - 最终报告：保存到 skill 目录下的
reports/ - 参考文件：
RULES.md（硬约束/反模式）、TYPES.md（分类标准/编号规范）、profiles.json（三档模式参数，修改后重启软件即全局生效） - 容错原则：调研不阻塞。所有脚本/命令调用必须有兜底路径。主路径失败 → 自动尝试替代方案（换
sys.executable/检查路径/直接 Python 实现）→ 三次失败后向用户报告具体问题。详见「容错原则」。
0. 支付级质量标准（所有 Task 共用，缺任何一项即降级）
| # | 标准 | 说明 |
|---|---|---|
| 1 | 结论先行 | 每章以 > 引用格式 核心判断开头 |
| 2 | 来源可追溯 | 每个数字标注（机构，年份） |
| 3 | 反方视角 | 至少 1 处呈现争议或反对观点 |
| 4 | 三层深度 | 事实层 → 因果层 → 判断层 |
| 5 | 零套话 | 无"近年来""值得注意的是"等填充词 |
| 6 | 标题含判断 | "格局：高度集中"✅ | "行业概况"❌ |
| 7 | 可自包含 | 首章必须定义核心概念 |
| 8 | 无内部编号 | 正文无任何流程编号，标题自解释 |
| 9 | 时间戳正确 | 文件名和报告尾时间必须 date 命令获取 |
| 10 | 目录源自大纲 | 目录从 outline.json 的第一级章节生成，不从正文提取 |
| 11 | 强制目录 | 报告正文前必须包含 ## 目录 标题及自动目录（TOC），列出所有章节标题 |
| 12 | 元数据完整 | 报告头部必须包含 总字数、阅读时间、数据截至日期（精确到月）、报告生成具体时间（精确到秒）、调研模式、Skill版本 六个字段，用 · 隔开。另起一行 > **参考来源**：{主要来源} 等 · 共引用 N 个来源。报告末尾须附 ## 参考来源（列出所有引用机构及链接）和 ## 免责声明。版本号从本 skill 的 VERSION 文件读取。 |
| 13 | 篇幅达标 | 见项目根目录 profiles.json。所有模式限制以 profiles.json 为准，修改后重启软件即全局生效。 |
| 14 | 四段式结构 | 顺序固定为：报告标题 → 元数据块（含六字段 + 参考来源行） → ## 目录 → 正文各章 → 尾部（参考来源 + 免责声明） |
| 15 | 编码洁净 | 所有中间文件（outline.json / data-pool.json / chapter-*.md）必须使用 UTF-8 无 BOM 编码写入，不得出现替换字符（\ufffd）或 GBK→UTF-8 Mojibake。子 agent 在写入前必须自行验证编码洁净，不得将编码问题遗留到主 agent |
| 16 | 纯文本公式 | 报告中不得使用 LaTeX/math 公式语法（$...$、$$...$$、\[...\] 等）。公式必须用纯文本或 Unicode 符号表达，确保复制到任何编辑器都不产生渲染问题 |
时间锚定规则
所有主题默认以 {CURRENT_YEAR} 为目标搜索最新数据。时间锚定模式在 Task 1 中由大纲 agent 按以下规则判定：
| 模式 | 符号 | 判定条件 | target_year | 验收 |
|---|---|---|---|---|
latest（默认） |
⏳ | 所有主题的默认值，除非符合 relaxed 或 user_specified | {CURRENT_YEAR} |
严格：≥50% 数据来自当年/前一年 |
relaxed（放宽） |
🔓 | 指南/教程/概念类主题，或用户问历史/原理（"草书发展""起源""背景"） | {CURRENT_YEAR} |
宽松：标记旧数据但不过滤 |
user_specified |
📌 | 用户提问显式指定了年份/月份（"2025年""2026Q1""2020年至今"） | 用户的指定年份 | 硬约束：>50% 匹配用户指定时间 |
What ships with it
24 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.
- command/research.md 751 B
- command/update.md 1.4 KB
- LICENSE 1.0 KB
- profiles.json 344 B
- prompts/task1_outline.md 8.8 KB
- prompts/task2_data_collection.md 29 KB
- prompts/task3_chapter_agent.md 3.8 KB
- prompts/task4_assembly.md 6.2 KB
- reports-browser/favicon.svg 241 B
- reports-browser/html-docx.min.js 406 KB runs code
- reports-browser/html2pdf.bundle.min.js 885 KB runs code
- reports-browser/index.html 19 KB
- reports-browser/marked.min.js 52 KB runs code
- reports-browser/reports.json 2 B
- RULES.md 6.9 KB
- scrapling-mcp-server.py 6.6 KB runs code
- sources.json 7.5 KB
- tools/dr_check.py 21 KB runs code
- tools/dr_gen.py 37 KB runs code
- tools/dr_tools.py 17 KB runs code
- tools/generate_pages.py 28 KB runs code
- tools/lang_config.py 24 KB runs code
- TYPES.md 4.9 KB
- VERSION 6 B
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 · 518 lines · 32 tokens per session scan C bfa5780983c1
deep-research is a skill published in the GitHub repository mxyhi/ok-skills (485 stars, last pushed 3d ago), licensed Apache-2.0. It adds 32 tokens to every session and 9,957 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). It is 98% identical to deep-research, differing in 12 lines, and is treated as a copy.
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