# Journal Reading

> Convert a medical paper (PDF or folder with supplements) into a professional, academic PowerPoint presentation (PPTX) with extracted figures/tables, mirroring the paper's own structure, with clean medical aesthetics.

- Skill: `yizhao6211999/journal-reading` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add yizhao6211999/journal-reading`
- Raw SKILL.md: https://api.skillmd.com/api/skills/yizhao6211999/journal-reading/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: yizhao6211999 (https://skillmd.com/u/yizhao6211999)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/yizhao6211999/journal-reading

---


# Journal Reading PPTX Conversion

## Overview

When a user provides a medical paper and asks for a journal reading presentation (Journal Reading 簡報 / 晨會簡報), use this skill to generate a professional, academic `python-pptx` presentation. The input can be:

- **A single PDF file** — the main paper
- **A folder** containing the main paper PDF plus supplementary files (e.g., supplement PDFs, appendix tables, additional figures downloaded from the journal website)

The slide structure should **follow the paper's own organization** — not a fixed template — to faithfully represent the study's logic and highlight its academic rigor.

## Prerequisites

1. The python modules `python-pptx` and `pymupdf` must be installed:
   ```bash
   pip3 install python-pptx pymupdf
   ```

## Workflow

### 0a. Ask for Presenter Information

Before starting any processing, **ask the user** for presenter information. This ensures the title slide and ending slide display the correct names. Present the question concisely — the user may skip it:

> **Presenter info:** Who is presenting and who is the supervisor? (e.g., "R2 王大明 / VS 李教授") — press Enter to skip.

- If the user provides names → use them on the title slide and ending slide
- If the user skips (empty reply or says "skip" / "略過") → check memory for saved user profile; if none found, leave presenter info blank or use a generic placeholder ("Presenter / Supervisor")
- Only ask **once** at the beginning — do not re-ask during the workflow

### 0b. Identify Input & Create Output Folder

#### Detect input type

The user may provide:
- **A single PDF file** → treat it as the main paper
- **A folder path** → scan the folder for all relevant files

```python
import os, re, glob

user_input = "..."  # path provided by user

if os.path.isdir(user_input):
    # Folder input: find all PDFs, images, and supplementary files
    input_dir = user_input
    all_pdfs = sorted(glob.glob(os.path.join(input_dir, "*.pdf")))
    all_images = sorted(
        glob.glob(os.path.join(input_dir, "*.png")) +
        glob.glob(os.path.join(input_dir, "*.jpg")) +
        glob.glob(os.path.join(input_dir, "*.jpeg")) +
        glob.glob(os.path.join(input_dir, "*.tif")) +
        glob.glob(os.path.join(input_dir, "*.tiff"))
    )
    # Identify main paper vs supplements by filename heuristics
    # Main paper: usually the largest PDF, or one without "suppl/supplement/appendix" in name
    main_pdf = None
    supplement_pdfs = []
    for pdf in all_pdfs:
        basename = os.path.basename(pdf).lower()
        if any(kw in basename for kw in ["suppl", "supplement", "appendix", "table_s", "figure_s"]):
            supplement_pdfs.append(pdf)
        elif main_pdf is None:
            main_pdf = pdf
        else:
            # Multiple non-supplement PDFs: pick the largest as main
            if os.path.getsize(pdf) > os.path.getsize(main_pdf):
                supplement_pdfs.append(main_pdf)
                main_pdf = pdf
            else:
                supplement_pdfs.append(pdf)
    print(f"Main paper: {main_pdf}")
    print(f"Supplements: {supplement_pdfs}")
    print(f"Standalone images: {all_images}")
else:
    # Single file input
    main_pdf = user_input
    input_dir = os.path.dirname(user_input)
    supplement_pdfs = []
    all_images = []
```

#### Create output folder

Create a dedicated output folder **in the same directory as the input**:

```
{ShortTitle}_journal_reading/
├── figures/                ← extracted figures & tables (from main + supplements)
└── presentation.pptx      ← final presentation
```

**Naming convention:** derive `{ShortTitle}` from the paper title — use 3-5 key English words in snake_case, e.g.:
- "The Effect of Topical Tranexamic Acid on..." → `topical_TXA_rhinoplasty_journal_reading/`
- "A Randomized Trial of Platelet-Rich Plasma..." → `PRP_randomized_trial_journal_reading/`

```python
paper_title = "..."  # extracted from the paper
short = "_".join(paper_title.split()[:5]).replace("/","_")
short = re.sub(r'[^a-zA-Z0-9_\-]', '', short)
base_dir = input_dir if os.path.isdir(user_input) else os.path.dirname(main_pdf)
output_dir = os.path.join(base_dir, f"{short}_journal_reading")
figures_dir = os.path.join(output_dir, "figures")
os.makedirs(figures_dir, exist_ok=True)
```

All subsequent outputs must be saved into this `output_dir`.

### 1. Read All Source Files

#### Main paper
Use the `Read` tool with `pages` parameter to read the main PDF, or `pdftotext` for full extraction:
```bash
pdftotext "paper.pdf" /tmp/paper_text.txt
```

#### Supplement PDFs
Read each supplement PDF as well — these often contain important supplementary tables, figures, methods, and sensitivity analyses:
```bash
for pdf in supplement_pdfs:
    pdftotext "$pdf" "/tmp/supplement_$(basename $pdf .pdf).txt"
```

#### Standalone images
Copy any standalone images (e.g., high-res figures downloaded from the journal website) directly into `figures/`:
```python
import shutil
for img in all_images:
    shutil.copy2(img, os.path.join(figures_dir, os.path.basename(img)))
```

### 2. Extract Figures & Tables from All PDFs

Apply the extraction process to **both the main paper and all supplement PDFs**. Supplement PDFs often contain high-resolution versions of figures, extended data tables, and flow diagrams.

Use a **three-tier approach** with PyMuPDF (`fitz`) for maximum quality. **Tier 1 MUST be caption-aware** (see warning below).

> ⚠️ **CRITICAL — DO NOT use `page.get_images()` indices to name files.**
> `page.get_images(full=True)` returns images in **xref order** (PDF resource
> dictionary order), NOT spatial / reading order. When a page has multiple
> figures, naming `embedded_p{N}_1`, `embedded_p{N}_2` produces SWAPPED labels.
> Real failure: in the Kappenstein 2026 thyroid paper, page 5 returned FIG 3
> (bottom) before FIG 2 (top), and the same happened on page 6 with FIG 4 / 5.
> Always use the caption-aware helper below, which sorts by spatial bbox
> position and matches each image to its "FIG. N" caption text block.

```python
import fitz
import os
import sys

# Use the caption-aware helper from this skill
SKILL_SCRIPTS = "<absolute path to>/.claude/skills/journal-reading/scripts"
sys.path.insert(0, SKILL_SCRIPTS)
from extract_figures_by_caption import extract_figures_with_captions

pdf_path = "paper.pdf"
# ──────────────────────────────────────────────
# TIER 1: Caption-aware extraction (REQUIRED)
# Maps each embedded image to its FIG N caption by:
#  - sorting images by bbox.y0 (true spatial order)
#  - finding nearest "FIG. N" / "Figure N" text block below the image
#  - naming files as fig1.{ext}, fig2.{ext}, etc.
# Falls back to img_p{N}_pos{M} for images with no caption (logos, etc.)
# ──────────────────────────────────────────────
saved = extract_figures_with_captions(pdf_path, figures_dir)
# saved is a list of dicts with {filename, fig_num, label, page, xref, bbox, size, ext}

doc = fitz.open(pdf_path)  # keep doc open for Tier 2 / 3 below

# ──────────────────────────────────────────────
# TIER 2: Block-based detection for precise bounding boxes
# ──────────────────────────────────────────────
PADDING = 8  # points of padding

for page_idx in range(len(doc)):
    page = doc[page_idx]
    blocks = page.get_text("dict")["blocks"]
    img_blocks = [b for b in blocks if b["type"] == 1]
    for i, block in enumerate(img_blocks):
        bbox = block["bbox"]
        print(f"  Page {page_idx+1} image block {i+1}: bbox={bbox}")

# ──────────────────────────────────────────────
# TIER 3: Full-page renders + padded crop
# For figures/tables spanning multiple blocks or needing captions.
# ──────────────────────────────────────────────
scale = 2.5
mat = fitz.Matrix(scale, scale)
for i, page in enumerate(doc):
    pix = page.get_pixmap(matrix=mat)
    pix.save(os.path.join(figures_dir, f"page_{i+1}.png"))

def crop_save(page_idx, rect_tuple, filename, padding=PADDING):
    """Crop a region from a PDF page with padding."""
    page = doc[page_idx]
    page_rect = page.rect
    x0 = max(rect_tuple[0] - padding, page_rect.x0)
    y0 = max(rect_tuple[1] - padding, page_rect.y0)
    x1 = min(rect_tuple[2] + padding, page_rect.x1)
    y1 = min(rect_tuple[3] + padding, page_rect.y1)
    clip = fitz.Rect(x0, y0, x1, y1)
    pix = page.get_pixmap(matrix=fitz.Matrix(3.0, 3.0), clip=clip)
    pix.save(os.path.join(figures_dir, filename))

doc.close()
```

#### Precise cropping workflow (CRITICAL)

Academic PDFs pack figures, tables, captions, footnotes, and body text tightly together. **Guessing crop coordinates by eye leads to stray text bleeding into the crop** (e.g., page headers, adjacent table footnotes, neighboring figure captions). You MUST follow this two-step process:

**Step 1 — Block analysis (mandatory before ANY crop):**
Run `page.get_text("dict")["blocks"]` on every page that contains a figure or table you need. Print each block's `type` (TXT=0, IMG=1) and `bbox`, plus a text preview for TXT blocks. This gives you the exact pixel boundaries of every element on the page.

```python
for page_idx in pages_with_figures:
    page = doc[page_idx]
    blocks = page.get_text("dict")["blocks"]
    for i, b in enumerate(blocks):
        btype = "IMG" if b["type"] == 1 else "TXT"
        bbox = [round(x, 1) for x in b["bbox"]]
        if btype == "TXT":
            text_preview = ""
            for line in b.get("lines", []):
                for span in line.get("spans", []):
                    text_preview += span["text"] + " "
            text_preview = text_preview.strip()[:80]
            print(f"  Block {i:2d} [{btype}] bbox={bbox}  \"{text_preview}\"")
        else:
            print(f"  Block {i:2d} [{btype}] bbox={bbox}")
```

**Step 2 — Derive crop coordinates from block boundaries:**
- For a **figure**: use the IMG block's bbox as the top boundary, and its caption TXT block's bbox bottom as the lower boundary
- For a **table**: use the table title TXT block's bbox top as the upper boundary, and the last footnote TXT block's bbox bottom as the lower boundary
- **Exclude** adjacent elements: page headers (e.g., "Plastic and Reconstructive Surgery • March 2024"), body text paragraphs, other figures' captions, copyright lines
- Use **small padding (4–6pt)** — just enough for clean edges without capturing neighboring content

```python
# Example: crop Fig 1 using block analysis results
# IMG block bbox = (147.5, 194.2, 435.5, 397.0)
# Caption block bbox = (147.5, 405.9, 437.4, 453.0)
# → crop from (145, 192) to (440, 455) with padding=4
crop_save(page_idx, (145, 192, 440, 455), "fig1.png", padding=4)
```

**Step 3 — Visual verification (mandatory):**
After cropping, use the `Read` tool to view each cropped image and confirm:
- No stray text from adjacent elements (headers, body text, other tables/figures)
- The complete figure/table is captured including title, data, and footnotes
- If any crop is wrong, re-examine block coordinates and re-crop

#### Figure extraction decision guide

| Scenario | Method | Notes |
|----------|--------|-------|
| Standalone photo/chart as raster image | **Tier 1** (`extract_image`) | Best quality — native resolution |
| Need precise crop of a figure region | **Tier 2** (block analysis → `crop_save()`) | MUST run block analysis first |
| Complex figure with caption, or table | **Tier 2** (block analysis → `crop_save()`) | Use block boundaries, not guesses |
| Vector graphics (PDF-drawn charts) | **Tier 2** block analysis + higher scale (3.5–4.0) | Won't appear in `get_images()` |

**Key principles:**
- **NEVER guess crop coordinates** — always derive them from `get_text("dict")["blocks"]` bounding boxes
- **Always use small padding (4–6pt)** when cropping — large padding captures neighboring elements
- **Tier 1 must be caption-aware** — use `extract_figures_with_captions()`, never `get_images()` index
- **For tables**: include title block through footnote blocks, but NOT adjacent body text or page headers
- **For figures**: include IMG block through caption block, but NOT adjacent tables or text
- **Verify FIG-N mapping AND content**: after Tier 1, read each `figN.{ext}` with the Read tool and confirm the image content matches what FIG N is described as in the paper (e.g., `fig2.jpeg` must show whatever the paper's "FIG. 2." caption describes — not just a clean crop). The caption-matching helper is robust on standard journal layouts but can fail on multi-panel figures with sub-captions only ("a)", "b)" without "FIG N"); always cross-check.
- **Verify ALL Tier-2/3 crops visually**: read each cropped image to confirm no stray content before proceeding — if wrong, re-crop immediately

### 3. Analyze the Paper Structure

Read the full text carefully. Identify the paper's **own sections** (e.g., Introduction, Methods, Results, Discussion, Conclusion) and key elements:

- **Title, authors, journal, year, DOI**
- **Study type** (RCT, cohort, meta-analysis, case series, systematic review, etc.)
- **Level of Evidence (LOE)**
- **PICO** — Population, Intervention, Comparison, Outcome
- **Key tables and figures** — map extracted images to their original labels (Table 1, Figure 2, etc.)
- **Statistical results** — p-values, confidence intervals, effect sizes, NNT
- **Limitations and strengths**
- **Clinical implications**

### 3.5. Text Formatting Rules (CRITICAL)

These rules apply to ALL python-pptx text in the generated script. Violating them causes formatting bugs (black text, missing font sizes).

#### Never use `p.text = "..."`
Always use `set_run()` or `p.add_run()` to add text. The `p.text =` pattern creates a run but does not guarantee formatting on subsequent lines.

```python
# ❌ WRONG — only first line gets formatted
p.text = "Line 1\nLine 2\nLine 3"
run = p.runs[0]
run.font.size = Pt(22)  # Only Line 1 is 22pt!

# ✅ CORRECT — each line is a separate paragraph with its own run
for i, line in enumerate(lines):
    p = tf.paragraphs[0] if i == 0 else tf.add_paragraph()
    run = p.add_run()
    run.text = line
    run.font.size = Pt(22)
    run.font.color.rgb = DARK_GRAY
    run.font.name = "Helvetica"
```

#### Never use `\n` in text strings
Each visual line must be a separate paragraph. Split multi-line content into a list before passing to any helper function.

#### Every text element must have explicit formatting
Every `run` must set: `font.size`, `font.color.rgb`, `font.name`. Never rely on defaults.

### 4. Design Slides Based on the Paper's Structure

**Do NOT force a fixed slide count or fixed template.** The number of slides should be determined by the content — use as many slides as needed to present the material clearly with comfortable spacing. **Prefer more slides with less content each** over fewer dense slides. A typical journal reading presentation may range from 15 to 25+ slides depending on the paper's complexity.

#### Layout density principles (critical):
- **Max 4–5 bullet points per slide** — each bullet should be one concise line
- **Max 1 table + 1–2 figures per slide** — if a slide has a table AND figures AND a highlight box, split it
- **Two-column layouts:** max 4–5 items per column
- **Leave breathing room** — generous padding, whitespace between elements; do not fill every pixel
- **When in doubt, split** — it is always better to add a slide than to cram content
- **Font sizes must be readable from the back of a conference room** — body text ≥ Pt(22), table cells ≥ Pt(16), titles ≥ Pt(32)

#### Required slides (always present):
- **Title slide** — Paper title, authors, journal, year, LOE badge, presenter info
- **Outline slide** (2nd slide) — Numbered table of contents listing all subsequent sections. This serves as a roadmap for the audience and must match the actual slide titles that follow.
- **Background / Introduction** — Clinical problem, knowledge gap, study rationale (1–2 slides)
- **Study Objective & PICO** — separate slide for clarity
- **Study Design & Methods** — split into multiple slides if needed (e.g., design + intervention on one, outcome assessment on another, grading scales/statistics on another)
- **Results** — one slide per major outcome; add a summary comparison slide if multiple outcomes exist
- **Discussion** (multiple slides) — This section should be **detailed and thorough**, with each sub-topic on its own slide: key findings, mechanism of action, comparison with literature, strengths & limitations, clinical implications, and future research directions. **Never condense Discussion into fewer than 4 slides.** When citing other studies in the Discussion, always include the **author name, year, and key finding** (e.g., "Ghavimi et al. (2017): IV TA reduced edema at 24 hrs"). These references come from the paper's own Discussion section — faithfully attribute claims to their cited sources rather than presenting them as standalone facts.
- **Conclusions** — key findings + clinical pearl
- **Ending slide** (last slide) — "Thank you — Questions?" with presenter info

#### Adapt to paper type:
| Paper Type | Structural Emphasis |
|------------|-------------------|
| RCT | CONSORT flow, intervention details, primary/secondary endpoints |
| Meta-analysis | PRISMA flow, forest plots, heterogeneity, subgroup analyses |
| Cohort / Case-control | Exposure definition, matching, confounders, adjusted estimates |
| Systematic review | Search strategy, inclusion criteria, quality assessment |
| Case series / report | Clinical presentation, timeline, management, outcome |
| Diagnostic study | Reference standard, sensitivity/specificity, ROC, STARD |

#### Slide Layout Patterns (16:9, coordinates in Inches)

Use these 5 patterns consistently. Reference them by name in code comments.

| Pattern | Layout | Coordinates | When to use |
|---------|--------|-------------|-------------|
| **A: Figure + Analysis** | Image left, bullets right | Image: (0.6, 1.4, w=6.0), Bullets: (7.0, 1.4, w=5.7, h=5.0) | Single figure with interpretation |
| **B: Figure + Analysis (reversed)** | Bullets left, image right | Bullets: (0.6, 1.4, w=5.7, h=5.0), Image: (6.8, 1.4, w=6.0) | Alternating visual flow |
| **C: Table + Key Takeaway** | Table top, highlight box bottom | Table: (0.6, 1.4, w=12.0, h=3.5), Box: (0.6, 5.2, w=12.0) | Results table with headline finding |
| **D: Side-by-Side Comparison** | Two images side by side, note below | Img1: (0.6, 1.4, w=5.8), Img2: (6.8, 1.4, w=5.8), Caption: (0.6, 6.0) | Comparing groups, before/after |
| **E: Pure Content** | Full-width bullets | Bullets: (0.8, 1.4, w=11.7, h=5.5), max 5 items | Intro, methods, discussion |

#### Emphasis Box Policy
Two types of emphasis boxes are available — use the appropriate one based on importance:

| Box Type | Function | Background | Text Color | When to Use |
|----------|----------|------------|------------|-------------|
| `add_highlight_box()` | Supporting emphasis | Warm yellow (`HIGHLIGHT_BG`) + left gold accent bar | Dark text | Exclusion criteria, study rationale, secondary notes |
| `add_key_point()` | **Primary emphasis** | Deep blue (`KEY_POINT_BG`) | White text, gold bold prefix | **KEY FINDING**, **CLINICAL PEARL**, most important takeaway |

- **Max 1 emphasis box per slide** — never stack multiple boxes
- `add_key_point()` is for the single most important finding on a slide (e.g., significant result, clinical pearl)
- `add_highlight_box()` is for supporting context (e.g., exclusion criteria, rationale)
- Position: typically at the bottom of the slide (Pattern C) or below bullets

#### Image-Text Pairing Rule
- Every figure/table **MUST** appear on the **SAME slide** as its interpretation text
- Use Pattern A or B to pair an image with analysis bullets
- **NEVER** isolate a figure on its own slide without interpretation
- **NEVER** put interpretation on a separate slide from its figure

#### Embedding figures in slides:
- Use `slide.shapes.add_picture()` to insert extracted figures/tables into relevant slides
- Place figures alongside bullet-point summaries for context
- Maintain original figure/table labels as captions
- Size figures appropriately — typically `Inches(5)` width for full-width, `Inches(3.5)` for side-by-side

```python
from pptx.util import Inches
# Example: add a figure to a slide
img_path = os.path.join(figures_dir, "figure1.png")
slide.shapes.add_picture(img_path, Inches(1), Inches(2), width=Inches(5))
```

#### Slide numbers (mandatory):
Every slide **must** display "X / N" in the bottom-right corner. Call `add_slide_numbers(prs)` from the helper library as the **final step** before `prs.save()`. This automatically adds numbers to all slides.

#### Academic quality principles:
- **Faithfully represent the paper** — preserve the authors' logic and data hierarchy
- **Show raw data** — include exact numbers, p-values, CIs; do not over-simplify
- **Use proper statistical reporting** — e.g., "OR 2.3 (95% CI 1.4–3.8, p=0.001)"
- **Cite figures/tables by original labels** — "Table 2", "Figure 3A"
- **Include critical appraisal** — bias assessment, study limitations, generalizability
- **Highlight significant findings** — use red/bold for significant p-values

#### Citation and attribution in Discussion slides:
- When the Discussion references other studies (comparison with literature, mechanism explanations, supporting evidence), **always attribute the claim to its source** with author name and year
- Format: **"Author et al. (Year):"** followed by key finding and study detail (e.g., sample size, route, outcome)
- Clearly distinguish between the current study's own findings vs claims from cited literature
- If the paper's Discussion explains a mechanism or makes an interpretive claim, note that it comes from the paper's own discussion (e.g., "The authors suggest..." or present it as the paper's interpretation)
- Do NOT present cited literature findings as if they are the current study's own results
- Example good format: `**Ghavimi et al. (2017):** IV TA in rhinoplasty (n=60) — reduced edema & ecchymosis at 24 hrs. *BUT systemic route*`
- Example bad format: `IV TA reduces edema and ecchymosis at 24 hours` (no attribution, unclear whose finding this is)

#### Slide layout and readability:
- **Keep bullet points concise** — max 4–5 per slide, one line each; split if more content is needed
- **Font sizes for projection:** body text ≥ Pt(22), titles ≥ Pt(32), table cells ≥ Pt(16), captions ≥ Pt(14)
- **Spacious layout** — do not pack slides tight; leave ≥ Inches(0.5) margins on all sides
- **Figures:** use `Inches(5–6)` width for full-width; `Inches(3–4)` for side-by-side; always leave room for caption
- **Tables:** limit to 4–5 data rows per slide; split large tables across multiple slides if needed
- **Widescreen format:** use `prs.slide_width = Inches(13.333)` and `prs.slide_height = Inches(7.5)` for 16:9 ratio

### 5. Generate the PPTX

Write a **self-contained** Python script to `/tmp/create_presentation.py` that **inlines all helper functions** from `scripts/generate_aesthetic_pptx.py`. Do NOT `import` from the helper file path — copy the function definitions directly into the script so it runs standalone.

#### Script structure:
1. **Inline all helpers** at the top: `set_run()`, `create_presentation()`, `add_title_slide()`, `add_content_slide()`, `add_section_num()`, `add_outline_slide()`, `add_ending_slide()`, `add_bullets()`, `add_highlight_box()`, `add_key_point()`, `add_styled_table()`, `add_image()`, `add_caption()`, `add_slide_numbers()`, `_set_slide_bg()`, `_add_shape_with_fill()`, `_add_card_bg()`, and all constants (`DEEP_BLUE`, `MEDIUM_BLUE`, `DARK_TEXT`, `ACCENT_RED`, `SUCCESS_GREEN`, `WHITE`, `MUTED_GRAY`, `LIGHT_BG`, `CARD_BORDER`, `HIGHLIGHT_BG`, `KEY_POINT_BG`, `TABLE_ALT_ROW`, `SECTION_NUM_COLOR`, font sizes, slide dimensions).
2. **Build slides** using the layout patterns by name in comments (e.g., `# Pattern A: Figure + Analysis`).
3. **Call `add_slide_numbers(prs)`** as the final step before `prs.save()`.

#### Key rules for the generated script:
- Use `add_content_slide(prs, title)` for every content slide (creates light-bg slide with title bar + accent line)
- Use `add_section_num(slide, "01 — Methods")` to add section number labels below title bar
- Use `add_bullets()` for bullet lists — supports plain strings, `("Bold:", "rest")` tuples, `{"red": "p=0.001"}` dicts, and `{"green": "positive finding"}` dicts
- Bold prefixes in tuples render in `DEEP_BLUE` for high contrast; body text uses `DARK_TEXT` (#1E293B)
- Use `add_key_point()` for **KEY FINDING** or **CLINICAL PEARL** — deep blue box with white/gold text
- Use `add_highlight_box()` for supporting context — warm yellow box with gold accent bar
- Use `add_styled_table()` with `{"red": val}` or `{"green": val}` dicts for colored cells
- Use `add_image()` with existence check — always pair with analysis on the same slide
- Use `_add_card_bg()` to create card-like backgrounds with left accent borders when grouping content visually
- Reference layout patterns A–E in comments for every slide

```python
output_path = os.path.join(output_dir, "presentation.pptx")
add_slide_numbers(prs)  # MUST be last step before save
prs.save(output_path)
```

Execute the script:

```bash
python3 /tmp/create_presentation.py
```

### 6. Deliver

1. All output files are saved inside the dedicated output folder:
   ```
   {ShortTitle}_journal_reading/
   ├── figures/              ← extracted figures & tables
   └── presentation.pptx    ← final presentation
   ```
2. Notify the user:
   - The output folder path
   - The number of slides generated and their section breakdown
   - The number of figures/tables extracted and embedded
   - That the file is fully editable in PowerPoint/Keynote

## Content Guidelines

- **Default language: English** — use standard medical/academic terminology
- If the user requests Chinese or bilingual, switch accordingly
- Preserve the paper's own terminology and abbreviations
- Always include LOE and study design on the title slide
- Tables should use the styled format (deep-blue header, clean rows)
- Significant p-values: red bold text
- Non-significant results: still include them — academic honesty matters
- End with clinical relevance — what should the audience take away?

## When to Use

This skill applies when a user:
- Provides a medical paper in PDF format
- Requests a "journal reading" / "Journal Reading 簡報" / "晨會簡報"
- Wants a PowerPoint (.pptx) presentation for academic presentation
- Mentions critical appraisal or evidence-based review