| name | present-paper |
| description | Academic presentation preparation — paper-driven (journal club, grand rounds, seminar) and lecture/teaching decks (course material, workshop slides, conference talks). Analyzes source material, finds supporting references, drafts audience-adapted speaker scripts, generates or augments PPTX with speaker notes, and prepares Q&A.
|
| triggers | present paper, paper presentation, journal club, seminar presentation, grand rounds, academic presentation, presentation prep, lecture, lecture material, teaching slides, course slides, 강의자료, 발표자료, 슬라이드, pptx |
| tools | Read, Write, Edit, Bash, Grep, Glob |
| model | inherit |
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:
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."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.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:
| Input | Why |
|---|
| Paper | PDF path, DOI, or PMID |
| Presentation time | Determines depth and slide count |
| Target audience | Specialty mix, knowledge level — controls terminology depth |
| Context | Course name, conference, journal club format, prior session topics |
| Extension section | Optional topic to include (e.g., AI directions, clinical implications). Default: none |
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:
- Follow-up studies — Has the main finding been replicated or extended?
- Clinical trial data — Large-scale data that contextualizes the findings
- Review articles — Authoritative summaries that frame the topic
- 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:
- Language: User's preferred language for narration; English for technical terms
- 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"
- Pronunciation guide: Include native-language pronunciation for drug names, abbreviations
- Example: "lecanemab (leh-KAN-eh-mab)" or local equivalent
- Timing markers: Note approximate time per slide
- 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"(\*\*(?:(?!\*\*).)+?\*\*"
r"|(?<![A-Za-z0-9])\*[^*\n]+?\*(?![A-Za-z0-9]))"
)
Two regex tricks together:
- Italic with boundary:
(?<![A-Za-z0-9]) and (?![A-Za-z0-9]) reject
* adjacent to alphanumerics, so DRB1*07:01 is left intact.
- 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 ]"
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:
- No markdown parsing. Every slide is a function call with explicit inline data.
- No
cur_top cumulative position tracking. Use the fixed coordinate zones below — cur_top accumulates rounding errors and breaks layout after ~10 slides.
- No Marp. Marp renders to images; the deck becomes uneditable and reviewers cannot copy text or restyle.
Slide-type templates
| Template | Use for | Required fields |
|---|
T_lead | Title slide, section divider | title, subtitle?, extra? |
T_text | Bullet body (most common) | title, body_lines[], subtitle? |
T_table | Cohort tables, comparisons | title, headers[], rows[][], body_before? |
T_image_right | Body + figure on right | title, body_lines[], img_path, img_pct? (PNG ≥300dpi or vector PDF — see Figure source formats below) |
T_quote_slide | Verbatim citations, witness quotes | title, quotes[], body_after?, img_path? |
T_two_col | Compare/contrast | title, left_lines[], right_lines[] |
T_two_col_with_box | Compare + emphasis | as above + metaphor_col, metaphor_lines[] |
T_highlight_slide | Single key result | title, highlight_lines[], body_before? |
T_metaphor_body | Body + analogy footer | title, body_lines[], metaphor_lines[] |
T_table_two_col | Take-aways + numeric table | title, left_lines[], headers[], rows[][] |
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)
| Helper | Role |
|---|
_text | Single text box with **bold** inline markup |
_multiline | Multi-line block with bullet (- , ✓ ) and ### subhead support |
_title_block | Title + teal underline + optional subtitle |
_table | Styled table (teal header row, alternating rows) |
_quote | Blockquote — teal left bar + light-blue background |
_highlight | Yellow rounded box + orange 2pt border |
_metaphor | Same shape as quote, lighter font |
_image | PIL aspect-preserving image insert (handles iPhone EXIF if you transpose first) |
_slidenum | Bottom-right page number |
Design tokens (defaults — change to fit institution/journal)
NAVY =
TEAL =
ORANGE =
GRAY =
FONT = 'Apple SD Gothic Neo'
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:
| Defect | Detect | Fix |
|---|
| TIFF images | find ppt/media -iname '*.tif*' | sips -s format png in.tif --out out.png + replace .tif→.png in _rels/*.rels |
<a:sp3d> in rPr | grep -l '<a:sp3d>' ppt/slides/*.xml | Regex-strip the <a:sp3d>...</a:sp3d> block (renders as red outline only on Mac) |
app.xml count mismatch | <Slides> value + HeadingPairs count + TitlesOfParts size vs actual slide files | Sync all four fields to real count |
srcRect corruption | Any value > 100000 (1/1000-percent cap) | Compare with original deck; restore verbatim |
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)
- Title slide (
T_lead) — paper citation + presenter
- Background (
T_text × 1–2)
- Study design / Methods (
T_text or T_two_col)
- Key results with figures (
T_image_right / T_table × 2–3)
- Discussion (
T_text)
- Limitations (
T_two_col_with_box works well)
- 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):
find ppt/media -iname '*.tif*' || true
grep -l '<a:sp3d>' ppt/slides/*.xml
grep -c '<Slides>\|<Notes>' docProps/app.xml
ls ppt/slides/slide*.xml | wc -l
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:
- Methodology critics: "Why this design? Why not...?"
- Domain experts: Deep technical questions about the specific field
- Generalists: "What does this mean for clinical practice?"
- 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:
| When | Use | Why |
|---|
| Need a figure on a slide (ROC, forest, KM, flow) | /make-figures first, then embed | Both skills share Reynolds/Knaflic/Tufte foundations; figure-level + slide-level companions |
| Manuscript reporting checklist parallel | /check-reporting for the same paper | Paper presentations often shadow manuscript revision; reporting-guideline gaps surface in Q&A |
| Visual abstract / Central Illustration | /make-figures visual-abstract templates | Then verify against ~/.claude/rules/journal-ai-image-policies.md (JACC prohibits, Radiology allows with disclosure) |
| PPTX edits to existing institutional template | ~/.claude/rules/pptx-mac-compatibility.md | Patch over rebuild; preserve master/layout/srcRect |
| Manuscript companion deck | ~/.claude/rules/manuscript-style-classical.md | Heading style, AI-Disclosure policy, em-dash discipline carry over to slides for senior MA reviewer audiences |
| References on slides | /verify-refs (audit-only) before delivery | Same anti-hallucination gate as manuscript references |
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.
