| name | paper-analyst |
| description | Analyze academic papers and research PDFs. Use this skill when the user uploads or pastes a research paper and wants to understand it. Triggers: 'analyze this paper', 'read this PDF', 'summarize this research', 'paper summary', 'research paper breakdown', 'explain this study', 'paper critique', 'academic paper analysis', '论文分析', '帮我看这篇论文', '解读这篇文章', '这篇 paper 讲什么', '帮我分析这个 PDF', '论文解读', '文献分析', '读一下这篇论文', '帮我总结这篇文章', '这篇论文的创新点', '论文方法分析', '论文结果', '帮我准备组会汇报', '生成 PPT 大纲'. Do NOT use for non-academic PDFs, spreadsheets, or general document tasks. Use when this capability is needed. |
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
| File | Purpose |
|---|
references/output-schema.md | Section structure and field rules |
references/paper-type-rubric.md | How to classify paper type |
references/quality-checklist.md | Anti-hallucination checklist |
references/presentation-schema.md | Slide plan JSON schema |
references/presentation-style-guide.md | Content compression rules for slides |
references/pptx-handoff.md | How to call the pptx skill for rendering |
scripts/extract_pdf_meta.py | Optional: extract PDF metadata to JSON |
Mode Selection
Default mode: standard. Detect from user's request:
| Mode | Trigger | Output |
|---|
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:
- Source tagging:
[原文声明] = directly stated in paper (cite location);
[模型归纳] = inferred by model (state reasoning basis)
- Uncertainty:
[未明确给出] when absent; [不确定] when ambiguous
- No domain assumption: classify paper type first, always
- No fabrication: venue, DOI, year, affiliations not in text →
[未明确给出]
- Evidence binding: each contribution must cite section/figure/table/quote
- Degraded PDF: state which sections were unreadable; do not fill gaps
Degraded Input Fallback
| Situation | Action |
|---|
| Only abstract available | quick mode, note limitation |
| Scanned PDF, no text | Ask user for text or OCR first |
| Missing references section | Skip prior work analysis, note absence |
| Figures unreadable | Skip figure analysis, note absence |
| Non-English paper | Translate key sections, note source language |
Extended Mode: Author Prior Work
Only in extended mode:
- Extract all author names from paper
- Identify self-citations in reference list (shared authors)
- Infer research group focus from affiliations + paper title
- List prior works from reference list only — no web search, no external knowledge
- Tag all output:
[基于论文内引用，非外部检索]
- 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: labduration_hint (10min / 20min / 30min) — default: 20mintalk_style (technical / overview / discussion) — default: technicalemphasis (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
Source: flyer-Li/paper-analyst — distributed by TomeVault.
