Present-Paper Skill

Purpose

Prepare a polished academic presentation from a research paper. The skill walks through a 5-phase pipeline: paper analysis, supporting research, script writing, slide note injection, and Q&A preparation.

Use it when:

• preparing a journal club or seminar presentation
• presenting a paper for a graduate course
• preparing grand rounds or conference talks based on a published paper
• building speaker notes for an existing slide deck

Communication Rules

• Communicate with the user in their preferred language.
• Use English for medical, statistical, and methodological terminology.
• Add pronunciation guides for drug names and technical abbreviations in the user's language.
• Be direct about paper limitations, but frame them constructively.

Phase 0: Init & Outline

Step 0a — Load design references (read before drafting outline)

Before collecting inputs, the skill loads three reference files:

1. `references/slide_design_principles.md` — Reynolds (Presentation Zen) +

Duarte (Slide:ology Glance Test™) + Knaflic (Storytelling with Data preattentive attributes) + Tufte (Cognitive Style of PowerPoint). Defines the 5 design principles, reading-time budgets per audience, cognitive-load ceilings, and the anti-patterns this skill is built to avoid. Read this first — it shifts the outline from "what content fits" to "what should the audience remember 10 seconds after each slide."

1. `references/medical_presentation_templates.md` — Section structure, slide counts,

and design seeds for the 5 contexts: journal club, grand rounds, conference talk, lecture, and academic lecture multi-paper survey. Pick the matching template after Phase 0 inputs are collected, then customize.

1. `references/slide_visual_styles/` — visual style specs (color palette, typography,

layout grid, slide-type templates) callable from any of the 5 context templates. Currently available: nature_lancet.md (Nature/Lancet aesthetic — white background, navy primary, coral accent, Inter/Pretendard). Default for academic lectures per ~/.claude/rules/academic-lecture-style.md. Paired with the generic builder templates/build_pptx_nature_lancet.py and the PDF figure extractor scripts/extract_pdf_figures.py.

These two files mirror the entry-point pattern used in make-figures/references/design_principles.md (Step 1 "Specify"). Both skills share the same Reynolds / Knaflic / Tufte foundations — slide-level (this skill) and figure-level (make-figures) are companions, not duplicates.

Required Inputs

Before starting, collect these from the user:

Paper Analysis

Read the paper and produce a structured analysis:

## Paper Analysis

### Citation
[Full citation with DOI]

### Background
- What gap does this paper address?
- What was known vs. unknown before this study?

### Study Design
- Type: [RCT / cohort / case series / meta-analysis / etc.]
- Subjects: [n, inclusion/exclusion]
- Methods: [key methodological choices]
- Primary outcome: [what was measured]

### Key Results
1. [Finding 1 with effect size and CI/p-value]
2. [Finding 2]
3. [Finding 3]

### Patient/Case Summary Table
[If applicable — structured table of individual cases or subgroups]

### Limitations
1. [Limitation 1]
2. [Limitation 2]

### Significance
- Why does this matter?
- What changes because of this paper?

Slide Outline

Create a slide-by-slide outline with time allocation:

## Slide Outline ([N] slides, [M] minutes)

| # | Title | Time | Key Content |
|---|-------|------|-------------|
| 1 | Title slide | 0:30 | Paper citation, presenter |
| 2 | Context / Prior sessions | 1:00 | How this connects to prior knowledge |
| 3 | Background | 1:30 | The gap this paper fills |
| ... | ... | ... | ... |
| N | Take-home messages | 0:30 | 3-5 key points |

Gate: User approves outline before proceeding.

Phase 1: Supporting Research

Search Strategy

Find references that strengthen the presentation:

1. Follow-up studies — Has the main finding been replicated or extended?
2. Clinical trial data — Large-scale data that contextualizes the findings
3. Review articles — Authoritative summaries that frame the topic
4. Contradicting evidence — Important for balanced Q&A preparation

Efficiency rule: Limit supporting references to 5-8 total. Only search categories that the approved outline (Phase 0) actually requires. Skip categories not needed for the presentation type (e.g., skip clinical trials for a methods-focused paper).

Selection Criteria

Do NOT summarize every paper found. Extract only:

• Specific data points needed for slides (incidence rates, OR/HR, AUC values)
• Findings that directly support or challenge the main paper
• Context that helps the audience understand significance

Output

## Verified References

### Main Paper
1. [Citation] — PMID: XXXXX, DOI: XX.XXXX/XXXXX

### Supporting References
2. [Citation] — PMID: XXXXX
   → Used for: [specific data point or context]
3. [Citation] — PMID: XXXXX
   → Used for: [specific data point or context]

### Key Data for Slides
- [Statistic 1]: [value] — Source: [Ref #]
- [Statistic 2]: [value] — Source: [Ref #]

Every reference must have a verified DOI or PMID. Mark unverified references with [UNVERIFIED].

Phase 2: Script & Content

Speaker Script

Draft a complete speaker script with these requirements:

1. Language: User's preferred language for narration; English for technical terms
2. Audience adaptation: Adjust explanation depth based on Phase 0 audience profile

• For mixed audiences: add one-line plain-language explanations for specialty-specific terms
• Example: "FLAIR sequence — an MRI technique that suppresses fluid signal to highlight edema"

1. Pronunciation guide: Include native-language pronunciation for drug names, abbreviations

• Example: "lecanemab (leh-KAN-eh-mab)" or local equivalent

1. Timing markers: Note approximate time per slide
2. Transition phrases: Connect each slide to the narrative arc

Structure

## Speaker Script

### Slide 1: Title (0:30)
"[Opening — introduce yourself and the paper]"

### Slide 2: Context (1:00)
"[Connect to prior knowledge or clinical relevance]"

...

### Slide N: Take-home Messages (0:30)
"[Summarize 3-5 key points. Thank audience. Invite questions.]"

Extension Section (Optional)

Only include if user requested in Phase 0. Examples:

• AI/computational research directions stemming from the paper
• Clinical practice implications
• Policy or guideline implications
• Connections to the user's own research

Gate: User reviews script before proceeding.

Phase 3: Slides & Notes

Two Modes

Mode A: Generate new slide deck

Generate a fully-editable PPTX from structured inline data using python-pptx. Two canonical template libraries:

• ${CLAUDE_SKILL_DIR}/references/generate_pptx_templates.py — generic T_lead /

T_text / T_table / T_image_right / etc. templates with smoke-tested main(). Use for journal club, grand rounds, conference talk, and short paper talks.

• ${CLAUDE_SKILL_DIR}/templates/build_pptx_nature_lancet.py — Nature/Lancet visual

style (white + navy + coral, Inter/Pretendard, 47-slide academic lecture proven). Use for academic lecture multi-paper survey (template #5). Functions: new_presentation, add_title_slide, add_toc_slide, add_section_divider, add_transition_slide, add_content_slide, add_glossary_slide, add_closing_slide, plus fix_app_xml() helper. Style spec: references/slide_visual_styles/nature_lancet.md.

For lecture decks pulling figures from PDFs (rather than from /make-figures output), use ${CLAUDE_SKILL_DIR}/scripts/extract_pdf_figures.py — pdftoppm + PIL crop with normalized (0–1) box coordinates. Supports both single-crop CLI and YAML batch config.

After raw extraction, run ${CLAUDE_SKILL_DIR}/scripts/trim_caption.py to auto-remove journal headers / figure captions / surrounding whitespace so that only the figure body remains — the Adobe-Acrobat-crop equivalent in automation. The script uses horizontal-projection segmentation plus text-band detection (height + density + gap + line-pattern signature) and preserves multi-panel figures intact:

python3 "${CLAUDE_SKILL_DIR}/scripts/trim_caption.py" \
  --in-dir  figures/extracted \
  --out-dir figures/cropped

Handles four common journal layouts: top running-head bar, bottom multi-line caption (sparse text), bottom caption fused with figure body (no clear gap, detected via narrow dark/light alternation), and multi-row tables with footnotes (footnote cut, table rows preserved). No tesseract / OCR dependency — Pillow + numpy only. Verified on 12-figure academic deck (80–95% height retention; captions, journal banners, and CellPress-style headers all removed). When the deck slot expects only the figure body (default for build_pptx_nature_lancet.py), point FIG_DIR at the cropped output dir.

Word-boundary aware markdown parser (mandatory for HLA-rich decks)

When the build script parses inline **bold** / *italic* markers in slide body or speaker notes, the italic rule must use word-boundary lookahead / lookbehind so asterisk-bearing scientific tokens (HLA alleles like DRB1*07:01, HLA-A*02:01, SNP IDs, footnote markers) are not eaten as italic delimiters:

import re
pattern = re.compile(
    r"(\*\*(?:(?!\*\*).)+?\*\*"                           # bold; inner single * allowed
    r"|(?<![A-Za-z0-9])\*[^*\n]+?\*(?![A-Za-z0-9]))"      # italic (word-boundary)
)

Two regex tricks together:

1. Italic with boundary: (?<![A-Za-z0-9]) and (?![A-Za-z0-9]) reject

* adjacent to alphanumerics, so DRB1*07:01 is left intact.

1. *Bold tolerates inner single `**: (?:(?!\\).)+?` allows

**DRB1*04:02** (HLA allele inside bold) to match as a single bold span.

Without these, a naive \*[^*]+\* italic pattern silently corrupts every HLA allele in the deck. Add the regex to add_styled() (or equivalent) in every Nature/Lancet-style build script.

Pronunciation auto-augment for non-native presenters

For decks where the presenter is uncomfortable with English pronunciation of acronyms, author names, drug names, or gene symbols, append a per-slide [ Pronunciation ] section to the speaker notes (audience sees nothing — only Presenter View). Use ${CLAUDE_SKILL_DIR}/scripts/inject_pronunciation_notes.py:

python3 "${CLAUDE_SKILL_DIR}/scripts/inject_pronunciation_notes.py" \
  input.pptx output.pptx \
  --dict pron_dict.yaml \
  --header "[ 발음 ]"            # or any header you like

The script:

• Loads a YAML/JSON PRON_DICT (term → [reading, full_name]) supplied by

the caller. The dict is domain-specific — assemble it for your audience (Korean readings, French readings, Spanish readings, etc.).

• Uses word-boundary regex (?<![A-Za-z0-9_]) … (?![A-Za-z0-9_]) so

short acronyms (e.g. AE, OR) only match when standalone, never inside other words.

• Recognizes allele-style tokens via a separate regex

(\b(?:HLA-)?[A-Z]{1,5}[0-9]?\*[0-9]{2}:[0-9]{2}\b by default) and synthesizes their reading from the base allele entry in the dict.

• Skips slides that already contain the header (idempotent — safe to re-run).

Realistic yield on a 47-slide academic deck: ~38 slides receive a section, ~300 total term entries, 5–10 per annotated slide. Transition and divider slides have empty notes and are auto-skipped.

Speaker notes statistics density

When the slide body already shows exact OR / 95% CI / p-value, the notes should NOT repeat the same numbers — the presenter ends up reading statistics aloud and the audience cannot keep up. Notes should be a narrative (key anchors + one-line "see the slide body for the exact numbers" reminder), not a numeric listing.

Quick measurement to spot dense slides during QC:

import re
text = slide.notes_slide.notes_text_frame.text.split(pron_header)[0]
n_char = len(text)
n_stat = len(re.findall(r"\b(?:OR|p|CI)\s*[=<>]?\s*\d|\d+\.\d+|\d+%|×10", text))
needs_compression = n_char > 1000 and n_stat >= 5

Rule of thumb: 700–1,000 chars + 0–2 stat tokens is fine (30–60-second narrative). >1,000 chars + ≥5 stat tokens → compress to narrative tone and point at the slide body. Exact numbers belong in the slide body and footnotes (SSOT), not the notes.

Sharing-ready notes-stripped variant

After the presentation, when the deck is shared with the audience (e.g. a professor asking for the slides), the speaker notes typically contain presenter-only material — second-language narrative, pronunciation hints, self-referential reminders ("Prof. ○○ will likely ask about …"). Stripping notes is mandatory before circulation. Use ${CLAUDE_SKILL_DIR}/scripts/strip_notes_for_sharing.py:

python3 "${CLAUDE_SKILL_DIR}/scripts/strip_notes_for_sharing.py" \
  presenter_v9.pptx share/<topic>_<initials>.pptx

The script:

• Clears every slide's notes_text_frame (idempotent, slide body and

figures untouched).

• Re-writes docProps/app.xml with the correct Slides= and Notes=

counts so PowerPoint Mac does not show its repair dialog (see also the app.xml canonical fix in pptx-mac-compatibility.md §5).

• Verifies that zero notes characters remain.

Recommended 3-file sharing package (filename pattern <topic>_<initials>):

• <topic>_<initials>.pptx — notes-stripped variant for slide reuse
• <topic>_<initials>.pdf — same deck, PDF for environment-agnostic

preview (LibreOffice --convert-to pdf automatically drops the cleared notes pages)

• <topic>_<initials>_references.zip — optional bundle of the reference

PDFs; if it exceeds the email attachment limit, send a Google Drive link.

In the cover email, mention the PPTX is included specifically so the recipient can reuse individual slides if useful.

Architecture

inline structured data (lists/dicts in build_*_slides())
    ↓ template functions (T_lead / T_text / T_table / ...)
editable PPTX with native text frames (selectable, restyleable in PowerPoint)

Three rules that keep slides stable:

1. No markdown parsing. Every slide is a function call with explicit inline data.
2. No `cur_top` cumulative position tracking. Use the fixed coordinate zones below — cur_top accumulates rounding errors and breaks layout after ~10 slides.
3. No Marp. Marp renders to images; the deck becomes uneditable and reviewers cannot copy text or restyle.

Slide-type templates

Figure source formats (when consuming /make-figures output)

When the deck pulls figures from analysis/figures/ produced by /make-figures:

• Preferred for slides: PNG at ≥300 dpi. python-pptx add_picture() handles this directly. Set img_pct (template T_image_right) so the figure occupies ≥40 % of slide width on a 13.33 × 7.5-in widescreen layout.
• Vector source available: prefer PDF only if the slide will be projected at >1080p or printed as a handout — convert PDF → PNG at the target DPI (pdftoppm -r 300 input.pdf out_prefix) before insertion, because python-pptx PDF embedding is unreliable across PowerPoint versions.
• Forbidden: TIFF (Mac PowerPoint silently drops it — see Mac compatibility checklist below); JPEG for line art (compression artifacts on diagonal lines); raw SVG (PowerPoint Mac handles it inconsistently).
• Caption / legend: re-draft for spoken-narration context, not the journal legend verbatim. The journal legend assumes a reader; the slide caption assumes a listener with 5–10 seconds of attention.

Helpers (used by templates — usually you do not call directly)

Design tokens (defaults — change to fit institution/journal)

NAVY    = #1B2A4A   # title text, section divider background
TEAL    = #0072B2   # subtitle, underline, table header bg, quote bar
ORANGE  = #D55E00   # highlight box border
GRAY    = #333333   # body text
FONT    = 'Apple SD Gothic Neo'   # use a Latin-only font on non-Korean decks

Fixed coordinate zones (16:9 = 13.333" × 7.5")

ML / MR = 0.8"     MT = 0.5"     CW = SW − ML − MR = 11.733"

TITLE_Y = 0.5"    TITLE_H = 0.8"
SUB_Y   = 1.3"    SUB_H   = 0.5"
BODY_Y  ≈ 1.9"    BODY_H  ≈ 5.1"

Build script responsibilities

A from-scratch generation script must:

• Convert TIFF images to PNG before add_picture (Mac PowerPoint silently drops TIFF).
• Apply EXIF transpose to iPhone photos before insertion.
• After inserting/removing slides, sync docProps/app.xml (<Slides>, <Notes>, HeadingPairs, TitlesOfParts) to the actual count, or PowerPoint Mac will raise a recovery dialog on open.
• If you copy <a:srcRect> from another deck, copy the values verbatim — they are 1/1000-percent (cap 100000), never EMU. A unit conversion bug here crops 99% of the image off-slide.
• Print slide count, notes count, file size, and editability check at the end.

Forbidden in Mode A

• ❌ Marp CLI for PPTX (always image-rendered, uneditable).
• ❌ Markdown auto-parsing into slides (layout drifts on every regeneration).
• ❌ cur_top cumulative top tracking (accumulates rounding error).
• ❌ Direct iPhone photo insert without EXIF transpose (rotated 90° in PowerPoint).
• ❌ Using python-pptx from-scratch rebuild to edit an existing deck — see Patch over Rebuild below.

Mac PowerPoint compatibility checklist

PowerPoint Mac is stricter than Windows / Keynote / LibreOffice on OOXML defects. Verify before delivering any deck destined for a Mac viewer:

Validation must run on PDF export AND Mac PowerPoint — neither alone catches all four. PDF misses sp3d outlines and srcRect corruption.

Patch over Rebuild — editing an existing PPTX

When the user supplies an existing deck and asks for surgical edits (textbox width, image crop, font swap, sp3d removal), prefer regex/sed patching of the unzipped XML over regenerating with python-pptx. From-scratch rebuild loses:

• <a:srcRect> image crops
• <a:sp3d> / <a:scene3d> (when intentional)
• Slide master / layout / theme details
• app.xml and core.xml metadata

unzip -q original.pptx -d /tmp/work
python3 -c "
import re; from pathlib import Path
p = Path('/tmp/work/ppt/slides/slide23.xml')
s = p.read_text()
s = s.replace('cx=\"9504720\"', 'cx=\"11200000\"')
p.write_text(s)
"
cd /tmp/work && zip -rq ../patched.pptx . -x '*.DS_Store'

python-pptx is reserved for (a) brand-new decks built via the templates above, or (b) appending speaker notes via slide.notes_slide.notes_text_frame.text. The skill's scripts/inject_speaker_notes.py is the canonical example of (b). It parses inline **bold** / *italic* into run-level styling by default (python-pptx stores text verbatim, so the markers would otherwise show literally in Presenter View — the failure mode pptx-speaker-notes.md warns against); pass --no-markdown for legacy plain text. A reproducible check lives at tests/test_speaker_notes_markdown.py.

Standard structure (10–15 min paper talk)

1. Title slide (T_lead) — paper citation + presenter
2. Background (T_text × 1–2)
3. Study design / Methods (T_text or T_two_col)
4. Key results with figures (T_image_right / T_table × 2–3)
5. Discussion (T_text)
6. Limitations (T_two_col_with_box works well)
7. Take-home (T_text or T_highlight_slide)

Output

Save to output/presentation.pptx. Speaker notes go into the notes pane only — never modify slide design when adding notes.

Step 3.5 — Slide critic (run before delivering deck)

After exporting the PPTX, run the slide critic rubric at references/critic_rubrics/slide.md. Score each slide and the deck-level Mac compatibility checks (Section F) as PASS / PARTIAL / FAIL. Produce concrete edits for every FAIL or PARTIAL item before treating the deck as ready.

Mandatory deck-level checks (cross-link with ~/.claude/rules/pptx-mac-compatibility.md):

# F.22 No TIFF
find ppt/media -iname '*.tif*' || true   # must be empty

# F.23 No 3-D bevel
grep -l '<a:sp3d>' ppt/slides/*.xml      # must be empty

# F.24 app.xml count sync
grep -c '<Slides>\|<Notes>' docProps/app.xml
ls ppt/slides/slide*.xml | wc -l         # must match

# F.25 srcRect bounds (any value > 100000 = bug)
grep -oE '"[0-9]{6,}"' ppt/slides/*.xml | head

Record critic_pass: yes | partial | no and refine_rounds: N in _quick_review.md.

Mode B: Add notes to existing slides (more common)

• Read existing PPTX to understand slide structure and count
• Map speaker script sections to corresponding slides
• Generate inject_notes.py script tailored to the specific presentation

Note Injection Script

Generate a tailored inject_notes.py following the pattern in ${CLAUDE_SKILL_DIR}/scripts/inject_speaker_notes.py. The generated script should contain only the notes dictionary customized for this presentation and the main injection loop from the template.

Critical Rule

Speaker notes are injected without modifying slide design, layout, text, or images. The script only touches the notes pane. Verify by comparing slide content before and after.

Phase 4: Q&A Preparation

Question Generation

Generate questions from multiple perspectives:

1. Methodology critics: "Why this design? Why not...?"
2. Domain experts: Deep technical questions about the specific field
3. Generalists: "What does this mean for clinical practice?"
4. Students/trainees: Clarification questions about unfamiliar concepts

Answer Structure

Every answer should follow the pattern:

Acknowledge → Evidence → Conclude

"That's an important limitation. [Acknowledge the concern honestly.]
However, [cite specific supporting evidence — author, year, finding].
So while [restate limitation], [conclude with the paper's contribution despite it]."

Quick Review Sheet

A single-page reference for last-minute review:

## Quick Review

### Must-Know Numbers
| Metric | Value | Source |
|--------|-------|--------|
| [Key stat 1] | [value] | [Ref] |
| [Key stat 2] | [value] | [Ref] |

### Common Pitfalls
- Don't confuse [X] with [Y]
- [Classification A] and [Classification B] are independent frameworks
- Slide says [rounded value], precise value is [exact value]

### Key Takeaways (memorize these)
1. [Point 1]
2. [Point 2]
3. [Point 3]

Output File Structure

All outputs go in the user's presentation directory:

{presentation_dir}/
├── _analysis.md              # Phase 0: Paper analysis + outline
├── _references.md            # Phase 1: Verified references + key data
├── _script.md                # Phase 2: Speaker script
├── _qa_prep.md               # Phase 4: Expected Q&A
├── _quick_review.md          # Phase 4: Pre-presentation review sheet + critic_pass record
├── _slide_critic.md          # Phase 3.5: Slide rubric scores per slide
├── inject_notes.py           # Phase 3: Tailored note injection script
├── figures/                  # Extracted paper figures (if needed)
└── reference/                # Supporting paper PDFs (if downloaded)

Cross-skill / Cross-rule integration

This skill composes with adjacent skills and global rules:

Constraints

• Never fabricate references. Every citation must be verified against PubMed, DOI, or the PDF itself.
• Never modify slide design when injecting notes. Notes and slides are separate concerns.
• Always ask audience first. Do not start drafting until the target audience is defined.
• Extension sections are opt-in. Do not add AI/clinical/policy sections unless explicitly requested.
• Respect presentation time. Script length must match allocated time (roughly 130-150 words per minute for academic presentations).

Anti-Hallucination

• Never fabricate references. All citations must be verified via /search-lit with confirmed DOI or PMID. Mark unverified references as [UNVERIFIED - NEEDS MANUAL CHECK].
• Never invent clinical definitions, diagnostic criteria, or guideline recommendations. If uncertain, flag with [VERIFY] and ask the user.
• Never fabricate numerical results — compliance percentages, scores, effect sizes, or sample sizes must come from actual data or analysis output.
• If a reporting guideline item, journal policy, or clinical standard is uncertain, state the uncertainty rather than guessing.