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 chtc66/academic-skills --skill paper-deep-notegit clone --depth 1 https://github.com/chtc66/academic-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/chtc66/academic-skills/paper-deep-note)<a href="https://agentmods.dev/skills/chtc66/academic-skills/paper-deep-note"><img src="https://agentmods.dev/badge/skills/chtc66/academic-skills/paper-deep-note/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/chtc66/academic-skills/paper-deep-note"><img src="https://agentmods.dev/badge/skills/chtc66/academic-skills/paper-deep-note.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00054 | $0.00576 |
| Opus 5 | $0.00027 | $0.00288 |
| Sonnet 5 | $0.00011 | $0.00115 |
| Haiku 4.5 | $0.00005 | $0.00058 |
Grade A, and why
paper-deep-note 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 9d 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
Paper Deep Note
用这个 skill 处理单篇论文精读。优先输出中文精读卡，强调研究问题、方法、证据、局限和对当前研究的启发。
工作流
- 先判断输入证据级别：全文、局部正文、摘要、仅标题。
- 明确声明判断边界。
- 按
references/note_template.md输出结构化精读卡。 - 在需要判断是否值得继续读时，参考
references/reading_guidelines.md。
输入处理规则
- 接收 PDF、arXiv 链接、标题加摘要、正文片段或用户自述笔记。
- 如果只有摘要或标题，明确写出“仅基于摘要判断”或“仅基于标题与摘要判断”。
- 如果实验设置、数据集、指标或结果未在输入中明确给出，直接标记为未知，不要补全想象内容。
- 如果用户给出的是局部正文，区分“原文明确给出”和“由局部内容推测”。
输出规则
- 默认输出完整精读卡。
- 如果用户只想快速筛论文，保留相同字段，但压缩每项长度。
- 在“优势”“局限”“复现难点”“启发”部分，优先给出和 AI / NLP / LLM / Agent / RAG / Safety 研究直接相关的判断。
- 在“是否值得精读”部分，只使用以下标签之一：
值得精读值得速读可暂缓
证据与表述约束
- 不要假装读过未提供的全文。
- 不要把论文 claim 直接改写成既定事实。
- 不要编造实验结果、消融结论、数据集细节或开源状态。
- 如果判断主要基于摘要，弱化关于方法细节和实验设计的断言。
何时读引用文件
- 始终读取
references/note_template.md以保持输出结构稳定。 - 在判断阅读优先级、复现难点或阅读深度时，读取
references/reading_guidelines.md。
默认交付
- 默认给出长版中文精读卡。
- 如果信息不足，在卡片顶部先给出“输入覆盖范围说明”。
What ships with it
3 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.
- 9d ago First seen · 50 lines · 54 tokens per session scan A d03d9c2884b1
paper-deep-note is a skill published in the GitHub repository chtc66/academic-skills (348 stars, last pushed 5mo ago), licensed MIT. It adds 54 tokens to every session and 576 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 2026-08-30.
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