annota: Skill for Claude Code

.claude/skills/annota-summarize/SKILL.md

annota-summarize is a skill for Claude Code from dengls24/annota. It costs 72 tokens per session (799 once invoked), scanned A, original, MIT.

A workflow for creating structured, bilingual reading notes about an academic paper and saving them as child notes in Zotero, a reference manager.

In plain words
What is it for?
Use it to find a paper in Zotero, extract its text, summarise it, record critical details, and save the notes to the paper's Zotero entry.
Why use it?
It organises a paper's metadata, main question, contributions, methods, results, limitations, and follow-up ideas in one consistent format.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

This is dengls24/annota's own configuration. It tells Claude Code how to work on annota itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything annota configures →

Reuse

Borrowing it

Nothing to install: this file belongs to dengls24/annota. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/dengls24/annota/master/.claude/skills/annota-summarize/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/dengls24/annota

Made for: Claude Code.

Wrote 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.

agentmods badge for annota-summarize

README.md
[![agentmods](https://agentmods.dev/badge/skills/dengls24/annota/annota-summarize/github.svg)](https://agentmods.dev/skills/dengls24/annota/annota-summarize)
Your own site
<a href="https://agentmods.dev/skills/dengls24/annota/annota-summarize"><img src="https://agentmods.dev/badge/skills/dengls24/annota/annota-summarize/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.

agentmods 80×15 button for annota-summarize

Your own site · 80×15
<a href="https://agentmods.dev/skills/dengls24/annota/annota-summarize"><img src="https://agentmods.dev/badge/skills/dengls24/annota/annota-summarize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 799 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00072 $0.00799
Opus 5 $0.00036 $0.00400
Sonnet 5 $0.00014 $0.00160
Haiku 4.5 $0.00007 $0.00080

Measured 10d ago against content hash fcbd544a2c68, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

annota-summarize 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 10d 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.

.claude/skills/annota-summarize/SKILL.md · 98 lines

What it actually says

论文结构化笔记

为一篇学术论文生成结构化阅读笔记,并保存到 Zotero。

输入解析

用户输入:$ARGUMENTS

工作流程

Step 1:定位论文

如果用户提供了文件路径,直接使用。否则用 search_zotero_items 搜索。

Step 2:获取元数据

调用 get_item_metadata(item_id) 获取:

  • 标题、作者、年份、期刊/会议、DOI

Step 3:提取全文

调用 get_pdf_text_bulk(item_id, skip_refs=True) 提取论文正文。

Step 4:生成结构化笔记

仔细阅读全文,按以下模板生成笔记:

<h2>📄 论文信息</h2>
<p><b>标题:</b>{title}</p>
<p><b>作者:</b>{authors}</p>
<p><b>发表:</b>{venue} {year}</p>
<p><b>DOI:</b>{doi}</p>

<h2>🎯 核心问题</h2>
<p>这篇论文要解决什么问题?为什么重要?</p>

<h2>💡 核心贡献</h2>
<ol>
<li>贡献 1</li>
<li>贡献 2</li>
<li>贡献 3</li>
</ol>

<h2>🔧 方法</h2>
<p>提出了什么方法/架构/算法?关键设计决策是什么?</p>

<h2>📊 实验结果</h2>
<ul>
<li>关键指标和数据</li>
<li>与 baseline 的对比</li>
</ul>

<h2>⚠️ 局限性</h2>
<ul>
<li>论文自述的 limitation</li>
<li>你认为的潜在不足</li>
</ul>

<h2>💭 个人启发</h2>
<p>这篇论文对我的研究有什么启发?可以借鉴什么?</p>

<h2>🔗 相关工作</h2>
<p>值得进一步阅读的参考文献</p>

Step 5:保存到 Zotero

提醒用户:保存笔记需要关闭 Zotero。

调用 add_child_note(parent_item_id, note_content) 将笔记保存为论文的子笔记。

parent_item_id 是文献条目的 ID(不是 PDF 附件的 ID)。可以从 get_item_metadata 返回的 itemID 获取。

Step 6:确认

告诉用户笔记已保存,提醒打开 Zotero 按 Ctrl+Shift+R 刷新查看。

注意事项

  • 笔记内容使用 HTML 格式(Zotero 原生支持)
  • 每个要点简洁有力,避免复制粘贴原文大段内容
  • "个人启发"部分可以根据用户的研究方向定制(如果已知)
  • 如果论文是中文的,笔记用中文;英文论文默认中文笔记,关键术语保留英文
Changes

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.

  1. 10d ago First seen · 98 lines · 72 tokens per session scan A fcbd544a2c68

Subscribe to this mod's changes

annota-summarize is a skill published in the GitHub repository dengls24/annota (19 stars, last pushed 4mo ago), licensed MIT. It adds 72 tokens to every session and 799 once invoked, about $0.0004 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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