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 rednotegit 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/rednote)<a href="https://agentmods.dev/skills/binary-husky/alphaautoresearch/rednote"><img src="https://agentmods.dev/badge/skills/binary-husky/alphaautoresearch/rednote/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/rednote"><img src="https://agentmods.dev/badge/skills/binary-husky/alphaautoresearch/rednote.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 | $0.00100 | $0.00569 |
| Opus 5.5 | $0.00040 | $0.00228 |
| Sonnet 5.5 | $0.00020 | $0.00114 |
| Haiku 4.5 | $0.00010 | $0.00057 |
Grade A, and why
rednote-convert 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
Markdown文章转小红书
概述
中间和结果文件都放置在 ${rednote_root} = ${源Markdown文件所在路径}/rednote 下
小红书生成分为 4 步
-
生成标题
- 一般沿用Markdown文章的标题即可
-
生成封面
- 见 alpha_auto_research/skills/writing/rednote_cover/SKILL.md
- 见 alpha_auto_research/skills/banana_image/SKILL.md
-
生成长文（这个过程比较复杂，请按照以下步骤执行） 3-1. 对文章的分区，然后对每个分区进行摘要，注意摘要还要包含对分区中所包含的图片的描述。生成
${rednote_root}/partitions.md3-2. 每个分区都转化为 4:3 的竖屏图片 -
生成正文短文 （带关键词hashtag）
写作约束（严格执行）
禁止：
- 绝对不使用任何形式的项目符号或编号列表（不用 *、-、1.2.3. 等列表形式）
- 不使用破折号（——）
- 禁用"A而且B"的对仗结构
- 尽量避免使用冒号（：），用句号代替
- 开头不用设问句
- 一句话只表达一个完整意思
- 每段不超过3句话
- 避免嵌套从句和复合句
- 所有内容都用自然段落呈现，用"第一"、"第二"、"第三"等词语自然串联
改写策略：
- 长句拆短：把复合句拆成多个简单句
- 术语翻译：把专业词汇翻译成大白话
- 增加温度：适当加入个人感受和真实体验
- 逻辑清晰：用"第一、第二、第三"标注顺序
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 · 47 lines · 100 tokens per session scan A 0073b34e3d77
rednote-convert is a skill published in the GitHub repository binary-husky/AlphaAutoResearch (11 stars, last pushed 5mo ago), licensed MIT. It adds 100 tokens to every session and 569 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…