Evidence-aware Chinese paper reading rules for Codex, Claude Code, Claude Project, and ChatGPT Project
Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add MrGeDiao/paper-reading-zh
Maintenance
fresh
1d since push
Risk
Safe to try
Quality score needs review
GitHub quality
86
78/100 quality · 76/100 trust
Coverage tags
Review notes
Quality score needs review · GitHub adoption: 86 GitHub stars
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Safe to tryInstall readiness, security metadata, maintenance, and adoption risk.
Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
86 GitHub stars
Repo activity
86 stars, 1 forks
Maintenance
1d since push
License
MIT
Install
npx skills add MrGeDiao/paper-reading-zh
Install safety
standard package or runtime install path
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add MrGeDiao/paper-reading-zhDo not use when
Alternative
52.7K stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K stars
npx skills add assafelovic/gpt-researcher
Alternative
19.1K stars
npx skills add dzhng/deep-research
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Install targets
Copy the registry command or an agent-specific install prompt for Codex, Claude Code, and Cursor.
Use the registry command when your workflow supports the OpenAgentSkill installer.
$ npx skills add MrGeDiao/paper-reading-zhAgent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Resolve JSON
/api/agent/resolve?task=Use%20Paper%20Reading%20Zh%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20Paper%20Reading%20Zh%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mrgediao-paper-reading-zh/install
Agent should check
Copy prompt
Task: Use Paper Reading Zh in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20Paper%20Reading%20Zh%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mrgediao-paper-reading-zh/install
Install command: npx skills add MrGeDiao/paper-reading-zh
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mrgediao-paper-reading-zh/install
LLM text format
/api/skills/mrgediao-paper-reading-zh/install?format=text
Find alternatives
/api/skills/search?q=Paper%20Reading%20Zh&limit=3
Agent prompt
Use Paper Reading Zh for this task. Review https://www.openagentskill.com/api/skills/mrgediao-paper-reading-zh/install, then install with: npx skills add MrGeDiao/paper-reading-zhRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mrgediao-paper-reading-zh
LLM text
/api/registry/manifest/mrgediao-paper-reading-zh?format=text
Install alias
/api/registry/install/mrgediao-paper-reading-zh
Recommend
/api/registry/recommend?task=Use%20Paper%20Reading%20Zh%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Python, Claude Code, OpenAI Agents
Audit report
Review install readiness, maintenance, trust, quality, and metadata warnings before adding this skill to an agent workflow.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
RAG and knowledge
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
Review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
CHECK86 GitHub stars
Stars/forks activity
CHECK86 stars, 1 forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Stack fit
Ingest, retrieve, and cite
A stack for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A stack for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Inspect, patch, and verify code
A stack for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills in this category, ranked with the same readiness and quality signals.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction overtime and deep dive into a topic.
# paper-reading-zh
给 AI 加一套论文阅读的证据规则：未核验的不补，读不到的不编，比较前先对口径。
[](./LICENSE) [](./CHANGELOG.md) [](./CONTRIBUTING.md) [](https://linux.do)
An evidence-rule pack for AI-assisted paper reading. Docs and outputs are in Chinese by design.
`paper-reading-zh` 是一个中文论文精读规则包，面向 Codex、Claude Code、Claude Project 和 ChatGPT Project。它不是论文翻译器，也不是文献管理器；它的目标是让 AI 先判断材料和论文类型，再基于可定位证据解释结论，少一点顺滑猜测。
- **证据边界**：venue、年份、CCF、代码链接未核验就写“未核验”；实验数字必须锚定原文 Table / Figure 或具体段落。 - **防顺滑编造**：公式抽取乱码不硬补，图表读不到不描述，只能读到摘要时明确写“仅基于摘要”。 - **跨论文口径审计**：比较多篇论文前先检查数据集、指标定义、模型规模、训练预算和测试 setting。 - **论文类型自适应**：算法、系统/测量、数据集/benchmark、理论/证明、综述/立场、观点/路线图使用不同主体骨架。 - **证据审计**：可把核心主张逐项对到原文锚点、证据类型、支持强度和未覆盖问题。 - **双入口复用**：同一套规则同时提供 Agent Skill（CLI / Agent 环境）和 Web Prompt Kit（网页端项目）。
## 和直接把论文丢给 AI 的区别
经常用 AI 读论文的人大多见过这些行为：查不到 venue 就补一个像样的，只读到摘要却写出全文精读，两篇论文口径不同也直接判胜负。这套规则把它们逐条挡住：
| 场景 | 无规则的典型行为 | 本规则下的输出 | |---|---|---| | venue / 代码链接查不到 | 凭印象补一个 | 写“未核验”；找过没找到写“未找到” | | 只能读到摘要 | 当作读完整篇开始精读 | 写“仅基于摘要”，先问是否继续轻量解读 | | 引用实验数字 | 复述时丢失出处 | 锚定到原文 Table / Figure 或段落，定位不到就不写 | | 公式抽取乱码 | 凭记忆重构公式 | 说明 PDF 抽取异常，只解释上下文可确认的含义 | | 多篇论文比较 | 按分数直接排名 | 先对数据集 / 指标 / 规模口径，不可比就标注“口径不完全可比” | | 理论、benchmark 或路线图论文 | 一律硬套“方法 / 实验 / 结果” | 按主要贡献与证据结构选择论文类型；无法判断时不强套模板 | | 用户要求证据审计 | 只给笼统的“有实验支持” | 列出主张、锚点、证据类型、支持强度依据和未覆盖问题 |
左列是常见失败模式的示意，不是对某个具体产品的实测记录；右列是规则的硬性要求。
## 输出长什么样
默认输出是约 2000 到 3500 中文字的中等深读。关键词（不超过 5 个）、一段话总结（不超过 150 字）和论文基本信息（标题 / venue/年份 / 链接 / 任务领域 4 项）保持稳定；主体会按论文类型调整，不再把系统、理论、benchmark 或路线图论文硬塞进同一骨架。
证据标注落在输出里是这样的（依据一篇真实验证过的 16 页路线图论文改写的示意节选，完整记录见 [docs/validation-2026-05-27.md](./docs/validation-2026-05-27.md)）：
```text 论文基本信息： - 标题：A Time Scaling Theory for Multi-Layer Electronic Systems - venue/年份：未核验 - 链接：未找到 - 任务领域：多层电子系统的时间缩放理论（产业路线图）
…… -
Frameworks & Tools
Decision snapshot
recent repository activity
Audit snapshot
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the audit before production use.
Growth loop
Scenario-led draft for Paper Reading Zh, ready for a manual X post.
Most coding agents don't fail from lack of model power. They fail when repo context disappears. Paper Reading Zh gives coding agents a repeatable way to plan, patch, review, or ship. 86 stars https://www.openagentskill.com/skills/mrgediao-paper-reading-zh?ref=x #AIAgents
Listing + install path for Paper Reading Zh: https://www.openagentskill.com/skills/mrgediao-paper-reading-zh?ref=x Install: npx skills add MrGeDiao/paper-reading-zh
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[](https://www.openagentskill.com/skills/mrgediao-paper-reading-zh)MrGeDiao
@mrgediao
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