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 | $0.00108 | $0.02840 |
| Opus 5.5 | $0.00043 | $0.01136 |
| Sonnet 5.5 | $0.00022 | $0.00568 |
| Haiku 4.5 | $0.00011 | $0.00284 |
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 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 — 205 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
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.jsonRECOMMENDATION_PATH:上阶段传入的实际推荐文件路径;独立调用时按日期定位中/英文文件,如两份均存在且无法确定本次目标,先确认目标再写入。
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 - 检查今天的推荐文件
{RECOMMENDATION_PATH}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
{TEMP_DIR}/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"或 "Must read" 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**/📒 **已有笔记**/📒 **Note**/📒 **Existing note**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(保留有效内容,重新调用 paper-reader 补全)
- 行数 >= 100 且包含
## 关键公式/## Key equations和## 关键图表/## Figures and tables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,执行 paper-reader(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
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 · +12 lines · +36 tokens per session 4ba4a47ccb4c
- 23d ago Changed · +4 lines 34c5d5b7e583
- 1mo 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 (1,320 stars, last pushed today), licensed Apache-2.0. It adds 108 tokens to every session and 2,840 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.
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