pptx skill
Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. If a .pptx or .potx file needs to be opened, created, or touched, use this skill.
Is the pptx skill safe?
Clean: nothing in its files matched our rules. We read 17 files in the folder on 2026-09-28.
No findings.
Install the pptx skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills mkdir -p ~/.claude/skills cp -r /tmp/scientific-agent-skills/skills/pptx ~/.claude/skills/pptx
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
PPTX creation, editing, and analysis
A .pptx is a ZIP archive of XML files. Choose your approach by task:
Scripts
Paths are relative to this skill's directory. Everything else is plain Python, node, or shell.
Creating with pptxgenjs — gotchas
pptxgenjs is preinstalled — do not run npm install first; write the script and require('pptxgenjs') directly. Only if that require fails: npm install pptxgenjs. The model knows the API; these are the footguns:
- Set pres.layout before adding slides. The default canvas is LAYOUT16x9 = 10" × 5.625", not 13.3" wide. Coordinates past the edge are written, not clamped — the shape just isn't on the slide. (LAYOUTWIDE is 13.3" × 7.5".)
- Hex colors: never #, never 8 digits. color: "FF0000". Both "#FF0000" and alpha baked into the hex ("00000020") corrupt the file. For translucency: transparency: 0-100 on fills and images, opacity: 0.0-1.0 on shadows — each is silently ignored on the other.
- pptxgenjs mutates option objects in place (converts values to EMU on first use). Never share one shadow/options object across two add* calls — build a fresh object each time.
- Shadow offset must be ≥ 0 — a negative offset corrupts the file. To cast a shadow upward, use angle: 270 with a positive offset.
- letterSpacing is silently ignored — the real option is charSpacing.
- Lists: bullet: true on each item, never a literal • (renders double bullets). Set breakLine: true on every array item except the last. Space bulleted paragraphs with paraSpaceAfter, not lineSpacing (huge gaps).
- One new pptxgen() per output file — never reuse an instance.
- rectRadius only works on ROUNDEDRECTANGLE**, not RECTANGLE.
- Gradient fills aren't supported — use a gradient image as the background instead.
- Text boxes have built-in internal padding — set margin: 0 whenever text must align with a shape, line, or icon at the same x.
- Speaker notes go in slide.addNotes("...") (plain text, once per slide), never in a text box on the slide.
- Keep charts native. Use addChart() for everything PowerPoint can chart (pass an array of {type, data, options} for combos). For PowerPoint-native features the library doesn't expose (trendlines, error bars), compute the extra series yourself or post-process the generated OOXML — do not fall back to a rendered image. Only chart types PowerPoint has no native form for (Sankey, network, chord) go in as images.
Editing existing decks and templates
Pick layouts first: python scripts/thumbnail.py template.pptx template-thumbs writes a labeled grid of every slide and prints the file(s) it created — template-thumbs.jpg, split into template-thumbs-N.jpg past 12 slides. Always pass that second argument, named after the deck. It defaults to thumbnails, so two decks thumbnailed in one directory silently overwrite each other's grids — the first deck's are simply gone (template analysis only — visual QA needs the full-resolution renders from Converting to Images; it only accepts .pptx, so copy a .potx to a .pptx name first). Use it with markitdown to map each content section onto a template slide, and vary the layouts — don't put every section on the same title-and-bullets slide.
python3 -c "import sys,zipfile; zipfile.ZipFile(sys.argv[1]).extractall('unpacked')" deck.pptx
python scripts/add_slide.py unpacked/ slide2.xml --after slide2.xml # duplicate a slide (or slideLayoutN.xml); prints the new slide's path
# reorder / delete slides = edit <p:sldIdLst> in ppt/presentation.xml
python scripts/clean.py unpacked/ # after deletions: removes orphaned slides, media, rels
# edit slide content in ppt/slides/slideN.xml
(cd unpacked && rm -f ../out.pptx && zip -Xr ../out.pptx .) # zip from INSIDE the dir; rm first or deleted parts survive
python scripts/office/validate.py out.pptx --original deck.pptx- Do all structural work — add, delete, reorder — before editing any slide's content. add_slide.py copies a slide file verbatim, so duplicating after you edit clones the edited content; and clean.py deletes any slide missing from , including one you just wrote.
- Never copy a slide file by hand — addslide.py does every registration a new slide needs and reports what it made (Created ppt/slides/slide17.xml from slide2.xml). It also works directly on a file: addslide.py deck.pptx slide2.xml -o out.pptx — pass -o, or it rewrites the input deck in place. A duplicated slide still references its source's chart/SmartArt/embedded-object parts rather than cloning them, so editing one slide's chart changes the other's.
- If you use python-pptx, three things it won't do: duplicate a slide (its only entry point is addslide(layout)), preserve formatting through textframe.text = "..." (that collapses the paragraph to a single unstyled run — assign run.text instead), or read the SVG/EMF most template art uses (add_picture raises UnidentifiedImageError).
- Legacy .ppt must be converted first: python scripts/office/soffice.py --headless --convert-to pptx file.ppt. .potx templates unpack and pack identically — keep the .potx extension on the output.
- To reuse a template icon or image, duplicate a slide or layout that already contains it.
When filling in a template:
- If you script an XML transform, parse with defusedxml.minidom — round-tripping OOXML through xml.etree.ElementTree rewrites namespace prefixes and corrupts the deck.
- Template slots ≠ source items. If the template shows 4 team members and you have 3, delete the 4th member's entire group (image + text boxes), not just its text — then check for orphaned visuals in QA.
- One per list item — never concatenate items into a single paragraph. Copy the sibling to preserve spacing, and put b="1" on the of titles, section headers, and inline labels (Status:, Owner:).
- Let bullets inherit from the layout; only add , (numbered), or to override — never a literal • in the text.
- Text with leading or trailing spaces needs xml:space="preserve" on its .
Design Ideas
Don't create boring slides. Plain bullets on a white background won't impress anyone. Consider ideas from this list for each slide.
Before Starting
- Pick a bold, content-informed color palette: The palette should feel designed for THIS topic. If swapping your colors into a completely different presentation would still "work," you haven't made specific enough choices.
- Dominance over equality: One color should dominate (60-70% visual weight), with 1-2 supporting tones and one sharp accent. Never give all colors equal weight.
- Dark/light contrast: Dark backgrounds for title + conclusion slides, light for content ("sandwich" structure). Or commit to dark throughout for a premium feel.
- Commit to a visual motif: Pick ONE distinctive element and repeat it — rounded image frames, icons in colored circles. Carry it across every slide. Do not use a color bar or accent stripe as your motif (see Avoid list).
Color Palettes
Choose colors that match your topic — don't default to generic blue. Use these palettes as inspiration:
For Each Slide
Every slide needs a visual element — image, chart, icon, or shape. Text-only slides are forgettable.
Layout options:
- Two-column (text left, illustration on right)
- Icon + text rows (icon in colored circle, bold header, description below)
- 2x2 or 2x3 grid (image on one side, grid of content blocks on other)
- Half-bleed image (full left or right side) with content overlay
Data display:
- Large stat callouts (big numbers 60-72pt with small labels below)
- Comparison columns (before/after, pros/cons, side-by-side options)
- Timeline or process flow (numbered steps, arrows)
Visual polish:
- Icons in small colored circles next to section headers
- Italic accent text for key stats or taglines
Typography
Font names you write into the .pptx are rendered by the user's PowerPoint, not by this environment. Your visual QA renders via LibreOffice, which substitutes fonts it doesn't have — and for some fonts the substitute has different widths, so your QA preview can show text overflow (or fit) that the real deck won't have. To keep your QA trustworthy:
- Safe fonts (render true-to-width in QA and ship with Office): Arial, Calibri, Cambria, Times New Roman, Courier New, Bookman Old Style, Century Schoolbook. Use these for body text and anything where fit matters.
- Headers with personality at zero QA risk: pair a safe-list serif header (Cambria, Bookman Old Style, Century Schoolbook) with a safe-list sans body (Calibri or Arial). You get visual contrast without giving up reliable overflow checks.
- If the user asks for a font outside the safe list (e.g. Georgia or Trebuchet MS): use it where the user asked, but size those containers with extra slack (~10%) and don't trust QA text-fit on those elements — the preview of that font is approximate. If the user hasn't specified, prefer safe-list fonts for body text.
- QA-unreliable fonts (substitute has different widths — overflow checks can be wrong): Georgia, Trebuchet MS, Impact, Arial Black, Garamond, Consolas, Palatino Linotype. Calibri Light substitution varies by environment; treat as QA-unreliable. Fine for titles/accents with slack; don't trust QA text-fit on these.
- Never default to Aptos — Office's post-2023 default has no metric-compatible substitute here and is missing from older Office installs, so it's unreliable on both ends.
Spacing
- 0.5" minimum margins
- 0.3-0.5" between content blocks
- Leave breathing room—don't fill every inch
Avoid (Common Mistakes)
- Don't repeat the same layout — vary columns, cards, and callouts across slides
- Don't center body text — left-align paragraphs and lists; center only titles
- Don't skimp on size contrast — titles need 36pt+ to stand out from 14-16pt body
- Don't default to blue — pick colors that reflect the specific topic
- Don't mix spacing randomly — choose 0.3" or 0.5" gaps and use consistently
- Don't style one slide and leave the rest plain — commit fully or keep it simple throughout
- Don't create text-only slides — add images, icons, charts, or visual elements; avoid plain title + bullets
- Don't forget text box padding — when aligning lines or shapes with text edges, set margin: 0 on the text box or offset the shape to account for padding
- Don't use low-contrast elements — icons AND text need strong contrast against the background; avoid light text on light backgrounds or dark text on dark backgrounds
- NEVER use accent lines under titles — these are a hallmark of AI-generated slides; use whitespace or background color instead
- NEVER add decorative color bars or accent stripes — this includes: header/footer bars spanning the slide width, vertical sidebar stripes down one edge of the slide, thin accent stripes along one edge of a card or content block, and "single-side borders" on rectangles. These read as AI-generated filler. If you want to set a card apart, use a subtle background tint, a drop shadow, or an icon — not an edge stripe.
- Don't default to cream/beige backgrounds — when no background is specified, use white (FFFFFF) or the user's brand palette; avoid warm-neutral defaults like F5F5DC, FAF0E6, FAEBD7, FFF8E1
QA (Required)
Your first render usually has a few real issues — overlaps, overflow, misalignment. Find and fix those, re-render only the slides you changed, and stop.
Content QA
markitdown output.pptxCheck for missing content, typos, wrong order.
When using templates, check for leftover placeholder text:
markitdown output.pptx | grep -iE "\bx{3,}\b|lorem|ipsum|\bTODO|\[insert|this.*(page|slide).*layout"If grep returns results, fix them before declaring success.
File QA (required)
python scripts/office/validate.py output.pptx # built from scratch
python scripts/office/validate.py output.pptx --original src.pptx # built from a templateIf the deck came from a template, always pass --original. A template may itself contain parts the XSD rejects, so a bare run can report failures you never caused — and a genuine regression can hide among them. --original baselines the schema and slide checks against the template, suppressing errors it already had. The structural checks — relationships, content types, charts — ignore --original and report template-inherited problems either way, so read those on their own merits.
pptxgenjs emits chart XML PowerPoint refuses to open, and every other tool accepts: python-pptx opens those decks, LibreOffice renders them, the XSD passes them. Every failure names its fix. Fix it in the generator and rebuild.
Visual QA
Convert the slides to images (see Converting to Images) and inspect every one. After staring at the generating code you tend to see what you expect rather than what rendered, so look at the images fresh (a subagent works well for this if you have one). User-visible defects to look for:
- Text overflow or text cut off at a box or slide boundary — check this first. It is the most common defect and always user-visible. (For a font the previewer renders unreliably per Typography, the preview is approximate: trust the ~10% slack you left, not its apparent fit.)
- Overlapping elements (text through shapes, lines through words, stacked elements)
- Source citations or footers colliding with content above
- Elements too close (< 0.3" gaps) or cards/sections nearly touching
- Uneven gaps (large empty area in one place, cramped in another)
- Insufficient margin from slide edges (< 0.5")
- Columns or similar elements not aligned consistently
- Low-contrast text (e.g., light gray text on cream-colored background)
- Template decoration mispositioned after text replacement — e.g., a title underline positioned for one line, but the replaced title wrapped to two
- Low-contrast icons (e.g., dark icons on dark backgrounds without a contrasting circle)
- Text boxes too narrow causing excessive wrapping
- Leftover placeholder content
Converting to Images
Convert presentations to individual slide images for visual inspection:
python scripts/office/soffice.py --headless --convert-to pdf output.pptx
rm -f slide-*.jpg
pdftoppm -jpeg -r 150 output.pdf slide
ls -1 "$PWD"/slide-*.jpgPass the absolute paths printed above directly to the view tool. The rm clears stale images from prior runs. pdftoppm zero-pads based on page count: slide-1.jpg for decks under 10 pages, slide-01.jpg for 10-99, slide-001.jpg for 100+.
After fixes, rerun all four commands above — the PDF must be regenerated from the edited .pptx before pdftoppm can reflect your changes.
Dependencies
pptxgenjs (npm, preinstalled — install only if require('pptxgenjs') fails) · markitdown[pptx], Pillow, defusedxml, lxml (pip — text dump, thumbnail, clean, validate) · LibreOffice (soffice, auto-configured for sandboxed environments via scripts/office/soffice.py) · pdftoppm (Poppler)
*This skill is created and maintained by Anthropic. Vendored here unmodified except for frontmatter metadata; s
More skills from K-Dense-AI/scientific-agent-skills
- AadaptyvHow to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
- AaeonThis skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
- AalphagenomeLook up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.
- Aanalytical-method-validationPlan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
- AanndataData structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
- AarborAutonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
- AarboretoInfer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
- AastropyCore Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
- AautoskillObserve the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
- Abenchling-integrationBenchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
- Abgpt-paper-searchSearch scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
- AbidsUse this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.