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 binary-husky/AlphaAutoResearch --skill general_markdowngit clone --depth 1 https://github.com/binary-husky/AlphaAutoResearchWrote 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/binary-husky/alphaautoresearch/general_markdown)<a href="https://agentmods.dev/skills/binary-husky/alphaautoresearch/general_markdown"><img src="https://agentmods.dev/badge/skills/binary-husky/alphaautoresearch/general_markdown/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/binary-husky/alphaautoresearch/general_markdown"><img src="https://agentmods.dev/badge/skills/binary-husky/alphaautoresearch/general_markdown.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 | $0.00000 | $0.00730 |
| Opus 5.5 | $0.00000 | $0.00292 |
| Sonnet 5.5 | $0.00000 | $0.00146 |
| Haiku 4.5 | $0.00000 | $0.00073 |
Grade A, and why
general_markdown 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 2mo 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.
What it actually says
第一阶段
针对当前的auto research进展，写一篇学术文章，使用markdown格式，包含 Introduction，Preliminary，Experiment，Conslusion
Introduction: 介绍 Alpha Auto Research，即本项目，主要是这个系统是如何执行自动化研究的，以及它的目标和意义。可以提到自动化研究在加速科学发现和创新方面的重要性。 Preliminary：介绍研究主题，注意需要web search，介绍相关的研究背景和现有的工作 Experiment：Auto Research 的步骤和结果 Conslusion：结论
默认语言：中文
强调：必须图文并茂，需要包含解释结果所需的曲线、表格、示意图等，并且需要对结果进行分析和解释，不能只展示结果，还要分析结果背后的原因和意义
写入 article_version_1.md 文件中
第二阶段
检查所有图表，确保它们绘制无误、布局无误、清晰美观、结论明显。
如果有问题，重新绘制并校验。
从基本的训练曲线和训练图表中，进行更深层的分析，发掘更多细节和规律，并绘制更具体的seaborn图标验证这些规律。
写入 article_version_2.md 文件中
第三阶段
-
使用以下skill生成炫酷的封面图 alpha_auto_research/skills/banana_image
-
检查一下章节、小节之间的衔接，如果衔接不好，进行润色修改。必要时，每个小节开头都要有一个过渡段，介绍一下这一小节的内容和它与前一小节的关系。
-
对不合理的表述进行修改，确保你是在跟读者沟通，而不是机械地完成任务。
- 例如 “> 生成日期：2026-04-14” 这个是不合理的表述，因为它没有什么意义，读者也不关心这个日期，要予以移除。
- 再例如 “与 v1 的差别：基于...” 这就很荒谬，你认为读者关心文章有没有 v1 版本吗？
- 再比如 “Alpha Auto Research（本项目） 是一个面向大模...” 这个表述也不合理，因为读者并不知道什么是 Alpha Auto Research，所以你应该直接介绍 Alpha Auto Research 是什么，而不是先说它是“本项目”，再在后面介绍它是什么。总之，你要确保你的表述是合理的，能够让读者理解你的意思，而不是让读者感到困惑或者无聊。
- 举一反三，找到其他类似的表述，并进行修改。
-
有些内容需要做成示意图（例如流程图、架构图等），以帮助读者更直观地理解复杂的概念和过程。例如 “PPO 在每个迭代中 ... 1, 2, 3 ppo_epochs ...mini_batch_num ...”，在文字表述的同时，需要用 banana_image 生成流程图并highlight这些参数如何起作用。另外这是GRPO训练，而不是PPO
-
把所有的图片通过以上的 skill 上传到图床中，方便分享和传输：使用图床处理所有图片 alpha_auto_research/skills/banana_image
写入 article_version_3.md 文件中
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.
- 2mo ago First seen · 48 lines · 0 tokens per session scan A be62db930c61
general_markdown is a skill published in the GitHub repository binary-husky/AlphaAutoResearch (11 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 730 tokens. 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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