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 agentmods add skills/ethan2409/paperflow/scopus-subscriptionnpx skills add Ethan2409/PaperFlow --skill scopus-subscriptiongit clone --depth 1 https://github.com/Ethan2409/PaperFlowWrote 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/ethan2409/paperflow/scopus-subscription)<a href="https://agentmods.dev/skills/ethan2409/paperflow/scopus-subscription"><img src="https://agentmods.dev/badge/skills/ethan2409/paperflow/scopus-subscription.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.00923 |
| Opus 5 | $0.00031 | $0.00462 |
| Sonnet 5 | $0.00012 | $0.00185 |
| Haiku 4.5 | $0.00006 | $0.00092 |
Grade A, and why
scopus-subscription 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 5d 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
scopus-subscription
Scopus 周更文献自动评分与 Obsidian 推荐笔记生成。
前置步骤：读取配置
开始前先读取项目根目录下的 config.yaml（如不存在则读取 config.example.yaml 并提示用户复制修改）。从配置中获取：
paths.scopus_csv_dir— Scopus CSV 下载目录paths.python_exe— Python 解释器路径literature.summary_dir— 推荐笔记输出目录（在 Obsidian Vault 内）
触发条件
当用户说"处理本周文献"、"文献推送处理"、"生成文献推荐"、"更新 Scopus 推送"或类似表述时触发。
核心流程
步骤 1：确认 CSV 文件
从 config.yaml 的 scopus_topics 列表中读取所有主题。对每个主题，拼接路径：
{scopus_csv_dir}/{csv_filename}
如果某个 CSV 不存在，跳过该主题并告知用户，继续处理其余文件。全部不存在则报错终止。
步骤 2：运行评分引擎
使用配置文件中的 Python 解释器执行 scripts/score.py：
{python_exe} <SKILL_DIR>/scripts/score.py --config <项目根目录>/config.yaml "csv1" "csv2" "csv3"
score.py 输出 JSON 数组，每个元素包含 topic、total（论文总数）、top10（评分最高的 10 篇论文，每篇含 title/authors/journal/year/doi/abstract/score）。
静默执行：禁止向用户展示代码、运行日志或评分细节。
步骤 3：读取输出格式规范
读取 prompt.md，严格按其定义的模板生成 Markdown。核心格式：
- 概览：主题名、处理论文数、Top 10 最高分、新趋势总结（从 Top 10 摘要中提炼 2-3 句）
- Top 3 论文：一句话总结 + 核心贡献（具体数据/方法） + 详细分析（方法亮点、关联启发）
- 第 4-10 篇：作者/期刊/年份/DOI/评分 + 一句话总结 + 核心贡献（不含详细分析）
- 评分维度：相关性 50%（5分）+ 影响力 30%（3分）+ 研究质量 20%（2分）
步骤 4：生成 Markdown 文件
对 score.py 返回的每个主题：
- 计算当前 ISO 周数：
python -c "import datetime; print(datetime.date.today().isocalendar()[1])" - 文件名格式：
W{周数}-{topic}.md，如W24-点蚀预测.md - 保存到
{literature.summary_dir}，目录不存在则自动创建 - 同名文件直接覆盖
步骤 5：汇报结果
简洁告知处理结果：
处理完成，已生成 X 个文件：
- {summary_dir}/W24-点蚀预测.md（共 Y 篇文献，Top 10 最高分 Z.Z）
- ...
约束
- 评分过程对用户完全透明无感 — 禁止暴露代码或运行日志
- 每篇论文的一句话总结基于摘要内容，禁止凭空编造
- 新趋势总结综合 Top 10 摘要提炼，不简单罗列
- 自然语言内容使用简体中文
- 论文标题和期刊名保留原文，不翻译
- 评分保留一位小数，格式为
X.X/10
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.
- 5d ago First seen · 85 lines · 62 tokens per session scan A 8dfd82706161
scopus-subscription is a skill published in the GitHub repository Ethan2409/PaperFlow (9 stars, last pushed 2mo ago), licensed MIT. It adds 62 tokens to every session and 923 once invoked, about $0.0003 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 2026-08-31.
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…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
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…