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paper-analyst论文分析师

Agent Skill

paper-analyst 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

392

周安装

16

GitHub Stars

16

下载量

125
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:paper-analyst(论文分析师)
来源仓库:https://github.com/flyer-li/paper-analyst
仓库路径:skills/paper-analyst
安装命令:
npx skills add https://github.com/flyer-li/paper-analyst --skill paper-analyst
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/flyer-li/paper-analyst --skill paper-analyst

简介

paper-analyst 用于论文相关的信息查找、检索与筛选。

  • 适合在学术研究中快速获取目标文献或技术资料时使用。
  • 通过 GitHub 安装,使用 npx skills add 命令添加技能。
  • 建议在使用前检查仓库维护状态及是否具备所需的数据访问权限。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Paper Analyst

Analyze academic papers from PDF or pasted text. Output in Chinese by default. All outputs follow references/output-schema.md. Paper type detection uses references/paper-type-rubric.md. Anti-hallucination rules in references/quality-checklist.md.

Quick Reference

FilePurpose
references/output-schema.mdSection structure and field rules
references/paper-type-rubric.mdHow to classify paper type
references/quality-checklist.mdAnti-hallucination checklist
references/presentation-schema.mdSlide plan JSON schema
references/presentation-style-guide.mdContent compression rules for slides
references/pptx-handoff.mdHow to call the pptx skill for rendering
scripts/extract_pdf_meta.pyOptional: extract PDF metadata to JSON

Mode Selection

Default mode: standard. Detect from user's request:

ModeTriggerOutput
quick"quick", "简单说", "一句话", "简要"Header + info + abstract + 3 contributions
standard(default)Full analysis: sections 1–5
extended"前作", "课题组", "prior work"standard + author/group prior work
presentation"PPT", "组会", "汇报大纲", "slides"standard + slide outline
presentation_with_figures"图表", "figures", "带图", "关键图"presentation + figure annotations

If ambiguous, use standard and offer to switch.

Workflow

Step 1: Assess Input Quality

Classify PDF quality before analysis:

  • 良好: Full text extractable
  • 降级处理: Partial text, scanned sections, garbled encoding
  • 严重降级: Minimal text, image-only PDF

If degraded: state reason in header line, proceed with available content, mark all gaps explicitly. Never fabricate content to fill gaps.

Optional: if user has Python, suggest running scripts/extract_pdf_meta.py first for structured metadata.

Step 2: Classify Paper Type

Read references/paper-type-rubric.md and classify. Do NOT assume AI/ML. Output the type label and 2–3 evidence indicators before proceeding.

Step 3: Execute Analysis

Follow references/output-schema.md for the selected mode. Apply all rules from references/quality-checklist.md throughout every section.

Step 4: Self-Check Before Output

Verify before finalizing:

  • Every uncertain field marked [不确定] or [未明确给出]
  • Every contribution tagged [原文声明] or [模型归纳]
  • No section silently omitted — skipped sections state why
  • Paper type label matches rubric evidence

Anti-Hallucination Rules

Full rules in references/quality-checklist.md. Non-negotiable constraints:

  1. Source tagging: [原文声明] = directly stated in paper (cite location); [模型归纳] = inferred by model (state reasoning basis)
  2. Uncertainty: [未明确给出] when absent; [不确定] when ambiguous
  3. No domain assumption: classify paper type first, always
  4. No fabrication: venue, DOI, year, affiliations not in text → [未明确给出]
  5. Evidence binding: each contribution must cite section/figure/table/quote
  6. Degraded PDF: state which sections were unreadable; do not fill gaps

Degraded Input Fallback

SituationAction
Only abstract availablequick mode, note limitation
Scanned PDF, no textAsk user for text or OCR first
Missing references sectionSkip prior work analysis, note absence
Figures unreadableSkip figure analysis, note absence
Non-English paperTranslate key sections, note source language

Extended Mode: Author Prior Work

Only in extended mode:

  1. Extract all author names from paper
  2. Identify self-citations in reference list (shared authors)
  3. Infer research group focus from affiliations + paper title
  4. List prior works from reference list only — no web search, no external knowledge
  5. Tag all output: [基于论文内引用,非外部检索]
  6. If insufficient info: explicitly state "信息不足,无法判断前作关系"

Presentation Mode: PPT Generation

Only in presentation or presentation_with_figures mode.

Step A: Collect Overrides

Before building the slide plan, check if the user specified any of:

  • audience (lab / conference / general) — default: lab
  • duration_hint (10min / 20min / 30min) — default: 20min
  • talk_style (technical / overview / discussion) — default: technical
  • emphasis (which sections to expand)
  • skip (which sections to omit)

If not specified, use defaults silently.

Step B0: Extract PDF Figures (presentation_with_figures only)

Before building the slide plan, run:

python scripts/extract_pdf_figures.py <pdf_path>

This saves all figures to figures/ and writes figures/index.json with name, path, and page for each image. Use this index when assigning figure_ref paths in the handoff.

Step B: Build Slide Plan

Follow references/presentation-schema.md for structure. Follow references/presentation-style-guide.md for compression rules.

  • Map each slide role to the corresponding output-schema section
  • Apply user overrides (emphasis → expand, skip → omit)
  • For presentation_with_figures: set figure_needed: true on method/result slides where a figure is the primary evidence; add figure_ref and figure_hint
  • Slide count from duration_hint (10min→6-7, 20min→9-10, 30min→12-14)

Step C: Call pptx Skill

Follow references/pptx-handoff.md for the exact handoff format.

  • Strip all [原文声明] / [模型归纳] tags before passing to pptx
  • Do NOT include speaker notes in the handoff
  • Call pptx skill automatically — do not ask the user first
  • Exception: if user said "只要大纲" / "just the outline", output the slide plan as text and skip pptx

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.55%
按下载量换算42

Claude

32.13%
按下载量换算40

Cursor

19.63%
按下载量换算25

Gemini CLI

9.85%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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