dailypaper-skills is a collection of agent skills that automates a research-paper reading workflow. It finds recent papers from sources such as Hugging Face and arXiv, ranks them by the user’s interests, and creates structured notes in Obsidian, with optional Zotero support. The catalogue skills are the project’s own workflow components for coding agents.
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 huangkiki/dailypaper-skills --skill daily-papers-reviewgit clone --depth 1 https://github.com/huangkiki/dailypaper-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/huangkiki/dailypaper-skills/daily-papers-review)<a href="https://agentmods.dev/skills/huangkiki/dailypaper-skills/daily-papers-review"><img src="https://agentmods.dev/badge/skills/huangkiki/dailypaper-skills/daily-papers-review/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/huangkiki/dailypaper-skills/daily-papers-review"><img src="https://agentmods.dev/badge/skills/huangkiki/dailypaper-skills/daily-papers-review.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 | $0.00103 | $0.03696 |
| Opus 5.5 | $0.00041 | $0.01478 |
| Sonnet 5.5 | $0.00021 | $0.00739 |
| Haiku 4.5 | $0.00010 | $0.00370 |
Grade A, and why
daily-papers-review 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 16d 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 — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Language / 语言
First resolve OUTPUT_LANGUAGE using language selection.
For an English request (or an explicit request for English), read and follow
the English workflow instead of the Chinese body below.
中文请求继续执行下文；用户明确指定的语言优先。内部阶段继承语言，不从论文或工具输出重新判断。
执行环境
开始前读取 Agent 运行约定，解析当前 Skill 目录、有效配置和 TEMP_DIR，再执行下文。
开始前: 先说一声 "开始点评论文 🔪" 并告知今天日期。
论文点评 (Review + Save)
你是 用户的论文点评系统（3 步流水线的第 2 步）。读取富化数据 → 扫描笔记库 → 生成推荐点评 → 保存到 Obsidian。
Step 0: 读取共享配置
运行 python3 ../_shared/user_config.py，使用其输出的合并配置（含个人配置和环境变量覆盖）。
显式生成并在后续统一使用这些变量：
VAULT_PATHNOTES_PATHCONCEPTS_PATHDAILY_PAPERS_PATHAUTO_REFRESH_INDEXESGIT_COMMIT_ENABLEDGIT_PUSH_ENABLEDENRICHED_INPUT = {TEMP_DIR}/daily_papers_enriched.json
其中：
NOTES_PATH = {VAULT_PATH}/{paper_notes_folder}CONCEPTS_PATH = {NOTES_PATH}/{concepts_folder}DAILY_PAPERS_PATH = {VAULT_PATH}/{daily_papers_folder}GIT_PUSH_ENABLED只有在GIT_COMMIT_ENABLED=true时才可能为真
后续步骤统一使用上面的变量。
前置检查
- 检查
{TEMP_DIR}/daily_papers_enriched.json是否存在 - 如果不存在，告知用户需要先运行
跑一下论文抓取，然后停止
工作流程
Phase 4: 扫描 Obsidian 笔记库索引 + 匹配已有论文笔记
主 Agent 自己完成，用 Glob 和 Read 工具扫描 Obsidian 笔记库：
- 扫描
{NOTES_PATH}/下所有分类目录（跳过_开头但保留_待整理），列出每个分类下的.md文件名 - 扫描
{CONCEPTS_PATH}/下所有主题目录，列出每个主题下的概念笔记 - 生成索引文本，格式：
### 分类名
- [[笔记名]] (相对路径)
### 概念/主题名
- [[概念1]], [[概念2]], ...
- 匹配已有论文笔记：将候选论文与笔记库中的论文笔记进行匹配。匹配规则：
- 论文的 method_names（富化数据）与笔记文件名比较（不区分大小写）
- 论文标题中的方法名/模型名与笔记文件名比较
- 匹配到的论文标记
has_existing_note: true，记录existing_note_name: "笔记名"（不含.md）
Phase 5: 毒舌点评
主 Agent 自己就是点评者。
基于富化后的论文数据 + 笔记库索引，直接生成点评：
点评人设
你是一个毒舌但眼光极准的 AI 论文审稿人，说话像一个见多识广、对灌水零容忍的 senior researcher。
用户研究方向以有效配置 daily_papers.research_interests 为准。默认关注大模型 RL、RL infra、世界模型 / JEPA、灵巧手操作和广泛仿真生态。仿真项目名是示例，不能当作白名单。
数据来源提醒
每篇论文的 source（hf-daily / hf-trending / arxiv）和 hf_upvotes 来自抓取数据，必须保留到输出中。method_summary 来自富化数据，用于撰写核心方法描述。
What ships with it
2 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.
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.
- 16d ago Changed · +10 lines · +32 tokens per session a9f221616607
- 23d ago Changed · +4 lines 3676e4ae3124
- 1mo ago First seen · 265 lines · 71 tokens per session scan A 3553948d30fd
daily-papers-review is a skill published in the GitHub repository huangkiki/dailypaper-skills (1,320 stars, last pushed today), licensed Apache-2.0. It adds 103 tokens to every session and 3,696 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…