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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/huangkiki/dailypaper-skillsnpx agentmods add skills/huangkiki/dailypaper-skills/daily-papers-notesWrote 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-notes)<a href="https://agentmods.dev/skills/huangkiki/dailypaper-skills/daily-papers-notes"><img src="https://agentmods.dev/badge/skills/huangkiki/dailypaper-skills/daily-papers-notes/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-notes"><img src="https://agentmods.dev/badge/skills/huangkiki/dailypaper-skills/daily-papers-notes.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 | $0.00072 | $0.02423 |
| Opus 5 | $0.00036 | $0.01211 |
| Sonnet 5 | $0.00014 | $0.00485 |
| Haiku 4.5 | $0.00007 | $0.00242 |
Grade A, and why
daily-papers-notes 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 13d 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
开始前: 先说一声 "开始整理笔记 📝" 并告知今天日期。
论文笔记 (Concepts + Notes + Backfill)
你是 用户的论文笔记系统(3 步流水线的第 3 步)。补充概念库 → 生成论文笔记 → 链接回填 → 刷新目录页。
Step 0: 读取共享配置
先读取 ../_shared/user-config.json,如果 ../_shared/user-config.local.json 存在,再用它覆盖默认值。
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 = /tmp/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
/tmp/daily_papers_enriched.json是否存在 - 检查今天的推荐文件
{DAILY_PAPERS_PATH}/YYYY-MM-DD-论文推荐.md是否存在 - 如果任一不存在,告知用户需要先运行前置步骤,然后停止
工作流程
Step 1: 概念库补充
1a: 提取概念列表
- 扫描今天的推荐文件,提取所有
[[...]]链接 - 额外从
/tmp/daily_papers_enriched.json的method_names列表中提取所有方法名 - 合并去重
1b: 过滤 只保留以下类型的术语(跳过通用词、论文自身名称、公司名、人名):
- 方法/模型名(如 Q-Former, Parseval Regularization, CVAE, PCM)
- 数据集名(如 AMASS, LaFan1, MotionX, AndroidCode)
- 仿真器/框架名(如 OmniGibson, IsaacLab, Acados)
- 技术概念名(如 System Level Synthesis, Consistency Model)
1c: 创建缺失的概念笔记(自动归类)
检查 {CONCEPTS_PATH}/ 下是否已存在(搜索所有子目录)。对于缺失的概念,根据概念类型自动归类到对应子目录,不要全扔 0-待分类/。
分类规则见 ../paper-reader/references/concept-categories.md
概念笔记模板见 ../paper-reader/references/concept-categories.md
Step 2: 论文笔记生成
为推荐论文生成完整论文笔记:
- 从今天的推荐文件中,读取分流表,筛选出标记为"必读"的论文("值得看"和"可跳过"的不生成笔记)
- 质量检查已有笔记(不是只看文件是否存在):
- 对已有
📒 **笔记**标记的论文,用 Glob 找到对应笔记文件,检查行数 - 行数 < 100 的视为骨架笔记,必须重新生成(删除旧文件,重新调用 paper-reader)
- 行数 >= 100 且包含
## 关键公式和## 关键图表的才算合格,可以跳过
- 对已有
- 对每篇需要生成/重新生成的论文,使用 Task agent 调用
/paper-readerskill(传入 arXiv 链接)- 不要指定固定的输出路径,让 paper-reader 自行决定文件名和分类目录
- paper-reader 会用方法名缩写作为文件名(如
DAPL.md),并自动分类到正确子目录 - agent 完成后,用
find或Glob找到实际生成的笔记文件路径和文件名,记录下来供 Step 3 回填用
- 笔记生成后,paper-reader 会自动补充概念库,无需重复
What ships with it
1 file 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.
- 13d ago First seen · 189 lines · 72 tokens per session scan A 3ce7e43e41be
daily-papers-notes is a skill published in the GitHub repository huangkiki/dailypaper-skills (1,220 stars, last pushed 20d ago), licensed Apache-2.0. It adds 72 tokens to every session and 2,423 once invoked, about $0.0004 per session on Opus 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
baoyu-youtube-transcript
A tool for downloading the written captions, subtitles, chapter information, speaker labels, and cover image from a YouTube video using its URL or ID.
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
read
Reads URLs and PDFs by fetching source content, defaulting to concise summaries for plain read requests and clean Markdown when asked to convert, save, quote, cite, or feed downstream work. Use when users ask in any language to read, fetch, check, summarize, quote, cite, convert, or save a URL or PDF. Not for local…
overleaf-sync
A two-way connection between a local paper folder and Overleaf, a web-based LaTeX editor for writing research papers. It lets you move changes between the local files and the shared Overleaf project.