paper-interview

name

paper-interview

description

Generate a podcast-style in-depth scientific interview that introduces an academic paper. Uses multi-agent analysis (field expert, methods specialist, context historian, critical reviewer, accessibility translator, impact assessor) to prepare rich source material, then an editor agent curates the narrative, and a writer agent composes the final interview between a professional science interviewer and the paper's author. The user drops a PDF of the paper; all supplementary context is gathered via web search and PubMed/bioRxiv. MANDATORY TRIGGERS: "paper interview", "podcast interview for paper", "introduce this paper", "generate interview for this paper", "paper podcast", "deep dive interview", "interview about this paper", "논문 인터뷰", "논문 소개 인터뷰", "paper introduction interview". Also trigger when the user uploads a PDF and asks for a podcast, interview, deep dive, or accessible introduction of a scientific paper.

Paper Interview Generator

Generate a dense, engaging, podcast-style interview that introduces an academic paper to scientists in the same field. The interview should provide enough depth for specialists while remaining compelling throughout.

Language Selection

At the very beginning of the workflow, ask the user to choose the output language:

• English (en) — Interview text, UI labels, and PDF in English. Uses Pretendard font.
• Korean (ko, 한국어) — Interview text, UI labels, and PDF in Korean. Uses Pretendard font (which has full Korean glyph coverage).

Store the choice as a variable (e.g., LANG="en" or LANG="ko") and pass it to both generate_interview.py --language $LANG and typeset_interview.py --language $LANG.

If the user does not express a preference, default to English.

Architecture Overview

The pipeline has 6 stages:

1. Extract — Read the PDF and extract structured content (including figure/table inventory)
2. Research — Gather background via web search and PubMed/bioRxiv
3. Analyze — Run 6 specialist sub-agents to prepare diverse material
4. Curate — Editor agent selects topics, structures the narrative, and creates a Visual Plan (deciding where to place embedded diagrams and original-paper figure references)
5. Compose — Writer agent produces the final interview transcript with Mermaid diagrams and figure placeholders interspersed in the dialogue
6. Typeset — Extract referenced figures from the paper PDF, render Mermaid diagrams to images, and compile everything into a professionally typeset PDF via Typst

Stage 1: Extract Paper Content

Read the uploaded PDF. Use pdftotext first; if it produces garbled output, fall back to pypdf:

from pypdf import PdfReader
reader = PdfReader("<paper_pdf_path>")
text = "\n".join(page.extract_text() or "" for page in reader.pages)

Then parse the text to identify these structural elements and save as JSON (paper_structure.json):

{
    "title": "...",
    "authors": ["..."],
    "abstract": "...",
    "introduction": "...",
    "methods_summary": "...",        # first ~2000 chars of methods
    "results_summary": "...",        # first ~3000 chars of results
    "discussion_summary": "...",     # first ~2000 chars of discussion
    "figures_tables": ["captions"],  # extracted figure/table captions (see below)
    "has_graphical_abstract": true,  # whether the paper has a graphical abstract
    "references_sample": ["..."],    # first 20 references
    "keywords": ["..."]
}

Figure and Table Extraction

Pay special attention to figures_tables. For each figure and table, extract:

• The identifier (e.g., "Figure 1", "Figure 2A-C", "Table 1", "Graphical Abstract")
• The full caption text
• A brief note on what type of visual it is (e.g., "bar chart", "heatmap", "microscopy image", "schematic", "flow diagram", "gel image", "scatter plot")

Format each entry as: "Figure 1: [caption] [type: bar chart]"

This metadata feeds into the editor's Visual Plan, which decides where to reference original figures and where to generate explanatory diagrams in the interview.

If the paper has a graphical abstract (common in Cell, Elsevier, and Nature journals), set has_graphical_abstract: true and include it as the first entry in figures_tables.

Extraction does NOT need to be perfect. Capture the gist of each section; the sub-agents work well even with imperfect extraction. Spend at most 2 tool calls on this step.

Stage 2: Research Background

Use web_search and PubMed tools to gather context. This material will be injected into each sub-agent so they can make informed, grounded analyses.

2a. Search for the paper itself

Search for the paper by title to find:

• The published version (DOI, journal, publication date)
• Any preprint on bioRxiv/medRxiv
• Press coverage or blog posts about it

2b. Search for related work

Based on the paper's topic and references, run 3-5 targeted searches:

• Key prior work the paper builds on (search by first author names + topic keywords)
• Competing or complementary recent papers in the same area
• Any controversies or debates in this sub-field

2c. PubMed search (if PubMed tool is available)

Use the PubMed tool to find:

• Recent reviews in this area (for background context)
• The authors' related publications (for their research arc)

Save all research findings to background_research.md as structured notes. Aim for 1-2 pages of relevant context. Do NOT over-research — 5-8 search calls total is enough. The sub-agents need context, not exhaustive literature review.

Stage 3: Multi-Agent Analysis

This is the heart of the skill. Run the orchestration script that calls the Anthropic API to get diverse analytical perspectives on the paper.

Setup and run

pip install anthropic --break-system-packages -q
python <skill_dir>/scripts/generate_interview.py \
    --paper paper_text.txt \
    --structure paper_structure.json \
    --background background_research.md \
    --output-dir agent_outputs/ \
    --language $LANG   # "en" or "ko"

The --model flag defaults to claude-sonnet-4-20250514 but you can pass any Anthropic model ID (e.g. --model claude-opus-4-6).

The script will call 6 specialist agents, 1 editor agent, and 1 writer agent. Read references/agent_prompts.md for the detailed role definitions.

If the script fails (API key not available, network issue, etc.), fall back to the manual agent simulation described below.

Fallback: Manual Agent Simulation

If the Python script cannot run, simulate the sub-agents yourself. For each of the 6 specialist roles defined in references/agent_prompts.md, write a focused analysis (300-500 words each) from that agent's perspective. Save each to a separate file in the agent_outputs/ directory. Then proceed to Stage 4 yourself.

The 6 specialist roles are:

1. Field Expert — Technical significance, novelty, positioning in the field
2. Methods Specialist — Experimental design, analytical rigor, technical innovation
3. Context Historian — Historical arc, how this work connects to the field's evolution
4. Critical Reviewer — Limitations, alternative interpretations, open questions
5. Accessibility Translator — Analogies, explanations for complex concepts, jargon
6. Impact Assessor — Real-world implications, future directions, broader significance

Stage 4: Editorial Curation

Whether sub-agent outputs came from the script or manual simulation, now act as the Editor Agent. Read all 6 analyses and the figure inventory, then produce an editorial plan. Follow the detailed Editor prompt in references/agent_prompts.md (Agent 7). The editorial plan must include:

• Hook: the single most compelling opening angle
• Narrative Arc: 4-5 acts with themes, topics, sources, and visual annotations
• Visual Plan: 3-6 visual elements (mix of diagram for concepts and figure_ref for original data). Space them out — consecutive visuals without dialogue between them break reading rhythm and feel like a slideshow rather than a conversation.
• Must-Include Topics: 5-8 non-negotiable points
• Tension Points: 2-3 moments for pushback or devil's advocate

Save to editorial_plan.md.

Stage 5: Compose the Interview

Act as the Writer Agent defined in references/agent_prompts.md (Agent 8). Using the editorial plan and all agent outputs, compose the final interview. This is the most important stage — the output quality depends entirely on the writing here.

Key principles (see the full Writer prompt in agent_prompts.md for details):

• Density: every exchange advances understanding — no filler
• Progressive disclosure: layer complexity gradually so a reader who stops at any point has learned something proportional
• Natural dialogue: vary exchange lengths, include pushback moments, show candor
• Visual embedding: embed <diagram> and <figure_ref> blocks between dialogue turns as specified in the Visual Plan. Diagrams illustrate concepts (like whiteboard sketches); figure refs point to data in the original paper.

Save the final interview to interview_final.md and present to the user.

Stage 6: Typeset to PDF

The final stage converts the markdown interview into a professionally typeset PDF that resembles the layout of high-quality science journals (Nature, Science, Cell). This stage has four sub-steps handled by the typesetting script.

Prerequisites

pip install pymupdf typst --break-system-packages -q
npm install @mermaid-js/mermaid-cli  # provides mmdc

6a. Figure Extract Agent — Extract referenced figures from the paper PDF

Parse the markdown interview for <figure_ref> blocks, extract the figure_id from each (e.g., "Figure 3B", "Graphical Abstract"), and use PyMuPDF to locate and render the corresponding page from the original paper PDF as a high-resolution PNG (288 DPI).

Strategy for locating figures:

• Graphical Abstract: always page 1 of Cell/Elsevier papers
• Figure N: search PDF page text for the caption string "Figure N." and render that page
• Fallback: scan all pages for the figure ID string

The extracted PNGs are saved to the working directory as fig_<sanitized_id>.png.

Not every figure extraction will succeed (e.g., multi-panel figures spanning pages, or figures with no text-searchable caption). When extraction fails, the typesetter falls back to a styled reference callout box (gold accent bar) that directs the reader to the original paper — this ensures the visual plan is always represented even on extraction failure.

6b. Render Mermaid Diagrams to PNG

Parse the markdown for <diagram> blocks, extract the Mermaid code from each, and render to PNG using mermaid-cli (mmdc):

mmdc -i diagram.mmd -o diagram.png -b white -s 2 -w 1200

The -s 2 flag produces 2× scale for crisp diagrams. If mermaid-cli is unavailable or rendering fails, the typesetter falls back to a monospace code block showing the Mermaid source with caption.

6c. Convert Markdown Interview to Typst Markup

The build script converts the interview markdown to Typst markup:

• **Host**: text... → red-accented host label with body text
• **[Author Name]**: text... → dark author label with body text
• <diagram> blocks → #figure(image("diagram_N.png"), caption: [...]) if a rendered PNG is available, otherwise a styled code block
• <figure_ref> blocks → #figure(image("fig_X.png"), caption: [...]) with a gold-accent annotation box if an extracted PNG is available, otherwise a gold-accent callout box citing the original figure
• Inline **bold**, *italic*, `code` → Typst equivalents

6d. Compile with Typst

The converted markup is injected into the Typst template at templates/interview.typ, which defines:

• Page layout: A4, generous margins, page numbers in header (from page 2)
• Title block: Red accent bar, "In-Depth Interview" section label, article title, metadata box (authors, journal, DOI link)
• Typography: Pretendard (Inter-based sans-serif with full Korean coverage), 9pt body text, justified paragraphs. Fonts are auto-downloaded and cached by the script (see ensure_pretendard_fonts() in typeset_interview.py).
• Dialogue styling: Host labels in red (#c0392b), author labels in dark navy (#1a1a2e)
• Figures: Standard Typst #figure() with captions
• Figure reference callouts: Gold-accented boxes (#fff8e1 fill, #f9a825 left bar)
• Closing: Thin rule followed by italic generation note

Compilation uses the typst Python package:

import typst
pdf_bytes = typst.compile("interview.typ")

Running the typesetting script

python <skill_dir>/scripts/typeset_interview.py \
    --interview interview_final.md \
    --paper <paper_pdf_path> \
    --structure paper_structure.json \
    --template <skill_dir>/templates/interview.typ \
    --output-dir typeset_output/ \
    --output interview.pdf \
    --mmdc ./node_modules/.bin/mmdc \
    --language $LANG   # "en" or "ko" — must match generate_interview.py

Fallback: Manual Typesetting

If the script fails, you can manually perform each sub-step:

1. Extract figures: Use PyMuPDF interactively:

   import fitz
   doc = fitz.open("paper.pdf")
   page = doc[3]  # page containing Figure 1
   pix = page.get_pixmap(matrix=fitz.Matrix(2, 2))
   pix.save("fig_figure_1.png")
   

2. Render diagrams: Save each Mermaid block to a .mmd file and run:

   ./node_modules/.bin/mmdc -i diagram.mmd -o diagram.png -b white -s 2
   

3. Build Typst file: Copy the template, replace PARAM_* placeholders with actual metadata, replace the INTERVIEW_CONTENT_START/END block with hand-converted Typst markup, and compile:

   import typst
   pdf_bytes = typst.compile("interview.typ")
   open("interview.pdf", "wb").write(pdf_bytes)
   

Quality Checklist (verify before delivering)

Before presenting the final output, verify these key areas:

Content quality — Opening hook is compelling (not generic); all Must-Include Topics are covered; technical claims match the paper (no hallucinated results); dialogue feels natural with varied exchange lengths; host asks at least 2 challenging questions; author acknowledges at least 1 limitation; length is 3000-5000 words.

Visuals — 3-6 visual elements embedded (at least 1 diagram + 1 figure ref); all Mermaid diagrams use valid syntax; figure refs cite correct IDs from the paper; no two visuals appear back-to-back without dialogue between them.

PDF output — Compiles without Typst errors; figures and diagrams appear at correct positions; metadata (title, authors, journal, DOI) is accurate in the header; layout has clean typography and spacing.

Notes

• The skill works best for empirical research papers with clear methods and results. For review articles or theoretical papers, the Methods Specialist agent's output may be thin — compensate by expanding the Context Historian's role.
• Language selection (--language en or --language ko) controls: (a) the editor and writer agent prompts (Korean output when ko), (b) PDF UI labels and footer text, (c) the Typst lang attribute, and (d) font priority order. The Pretendard font family supports both Latin and Korean glyphs and is auto-downloaded by the typeset script if not already cached.
• For very long papers (>30 pages), focus extraction on the abstract, intro, results, and discussion. Methods can be summarized briefly.