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 github-trendinggit 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/github-trending)<a href="https://agentmods.dev/skills/huangkiki/dailypaper-skills/github-trending"><img src="https://agentmods.dev/badge/skills/huangkiki/dailypaper-skills/github-trending/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/github-trending"><img src="https://agentmods.dev/badge/skills/huangkiki/dailypaper-skills/github-trending.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 | $0.00109 | $0.00914 |
| Opus 5 | $0.00055 | $0.00457 |
| Sonnet 5 | $0.00022 | $0.00183 |
| Haiku 4.5 | $0.00011 | $0.00091 |
Grade A, and why
github-trending 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 11d 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
开始前: 先说一声 "开始抓 GitHub 榜单 🐙" 并告知今天日期与抓取周期（默认周榜）。
GitHub Trending 抓取
抓取 GitHub Trending → 用关键词打分标注 → 写成 Obsidian 笔记。纯 Python 脚本，零 token。
Step 0: 读取共享配置
配置在 ../_shared/user-config.json，个人覆盖在 ../_shared/user-config.local.json（存在则以 local 为准）。
脚本内部已通过 ../_shared/user_config.py 自动加载，无需手动传参。相关字段：
paths.obsidian_vault— vault 根路径paths.github_trending_folder— 输出文件夹（默认GitHubTrending）daily_papers.keywords / negative_keywords / domain_boost_keywords— 复用它们给项目打分、标注相关性
解析周期与语言
从用户输入解析 --since：
- "周榜"、"过去一周"、"这周"、"本周" →
--since weekly（默认） - "日榜"、"今天"、"今日" →
--since daily - "月榜"、"这个月"、"本月" →
--since monthly
可选 --language（如 "只看 Python" → --language python），不指定则抓全部语言。
工作流程
Phase 1: 抓取 + 打分（fetch_trending.py）
python3 fetch_trending.py --since weekly > /tmp/github_trending.json
# 指定语言示例：
# python3 fetch_trending.py --since weekly --language python > /tmp/github_trending.json
脚本自动完成：
- 抓取
github.com/trending?since=<周期>（兼容 SSL 拦截环境，自动退回不验证证书） - regex 解析：repo、简介、语言、累计 star/fork、本周期新增 star
- 用共享配置关键词打分，标注
relevant（命中正向/领域词且未被负向词压过），不丢弃非相关项 - 按本周期新增 star 降序排序
检查输出：确认 /tmp/github_trending.json 是非空 JSON 数组。为空则看 stderr 诊断。
Phase 2: 写 Obsidian 笔记（write_trending_note.py）
python3 write_trending_note.py /tmp/github_trending.json
- 输出到
{vault}/{github_trending_folder}/ - 周榜文件名按 ISO 周编号，如
2026-W28 GitHub周榜.md；日/月榜用日期 - 笔记结构：摘要 → 「🎯 与研究方向相关」列表 → 「📊 完整榜单」表格（含 ✅ 相关标记）
- 脚本把最终笔记路径打到 stdout
输出
完成后告知用户：
- 抓取了多少个项目、其中多少个与研究方向相关
- 生成的笔记路径
注意事项
- 两个脚本都不启动 Task Agent，零 token 消耗
- 不做 git 操作（与 daily-papers 一致，git 自动化默认关闭）
- 网络不通时参考全局 CLAUDE.md 的代理规则（17890 端口）；脚本已内置 SSL 兜底
- GitHub Trending 只有 daily/weekly/monthly 三档，无法自定义任意天数
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.
- 11d ago First seen · 76 lines · 109 tokens per session scan A 7db3ffcbf2fb
github-trending is a skill published in the GitHub repository huangkiki/dailypaper-skills (1,217 stars, last pushed 18d ago), licensed Apache-2.0. It adds 109 tokens to every session and 914 once invoked, about $0.0005 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
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…
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…