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-feishu-digestgit 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-feishu-digest)<a href="https://agentmods.dev/skills/chtc66/academic-skills/paper-feishu-digest"><img src="https://agentmods.dev/badge/skills/chtc66/academic-skills/paper-feishu-digest/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-feishu-digest"><img src="https://agentmods.dev/badge/skills/chtc66/academic-skills/paper-feishu-digest.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 | $0.00052 | $0.00605 |
| Opus 5 | $0.00026 | $0.00302 |
| Sonnet 5 | $0.00010 | $0.00121 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
paper-feishu-digest 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
Paper Feishu Digest
用这个 skill 抓取最近时间窗口内的 arXiv 论文，按相关性做保守排序，生成中文速递，并在需要时推送到飞书 webhook。
默认场景
- 类别：
cs.AI,cs.CL - 时间窗口：
24小时 - 关键词：
agent,reasoning,rag,safety review,安全评审 - 输出数量：
top 10 - 输出语言：中文
工作流
- 优先调用
scripts/arxiv_digest.py获取最近论文并生成结构化结果。 - 依据
references/message_template.md组织最终消息格式。 - 如果需要发飞书，参考
references/operations.md中的操作约束。
输入处理规则
- 接收类别、时间窗口、最大抓取数量、关键词、top-k、webhook 等参数。
- 如果用户没有给参数，使用默认场景。
- 如果用户只需要离线摘要，不要主动发 webhook。
- 如果 webhook 缺失或无效，继续输出 Markdown / JSON，不要静默失败。
输出规则
- 每篇论文至少输出：
- 标题
- 链接
- 摘要
- 核心贡献
- 局限
- 是否值得读
- “核心贡献”“局限”“是否值得读”只能基于摘要做保守判断。
- 明确标注这是“基于 arXiv 摘要的快速筛选”，不是全文评审。
脚本使用
优先使用脚本，而不是手写抓取逻辑。
python paper-feishu-digest\scripts\arxiv_digest.py --hours 24 --top-k 10
常见参数：
--categories--hours--max-results--top-k--keywords--webhook--post--json-out--md-out
证据与表述约束
- 不要把摘要判断写成全文结论。
- 不要把“值得读”写成绝对推荐，给出简短理由。
- 如果 API 返回结果不足，明确告诉用户当前窗口内样本有限。
何时读引用文件
- 始终读取
references/message_template.md以保持消息结构一致。 - 在执行 webhook 推送或说明运维约束时读取
references/operations.md。
What ships with it
4 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 · 73 lines · 52 tokens per session scan A e5ef15c61743
paper-feishu-digest is a skill published in the GitHub repository chtc66/academic-skills (348 stars, last pushed 5mo ago), licensed MIT. It adds 52 tokens to every session and 605 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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