Scientific Agent Skills

🔔 Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills standard, not just Claude.

New: K-Dense BYOK — A free, open-source AI co-scientist that runs on your desktop, powered by Scientific Agent Skills. Bring your own API keys, pick from 40+ models, and get a full research workspace with web search, file handling, 100+ scientific databases, and access to all 163 skills in this repo. Your data stays on your computer, and you can optionally scale to cloud compute via Modal for heavy workloads. Get started here.

🎥 Webinar recording — Getting Started with K-Dense BYOK A hands-on walkthrough of K-Dense BYOK, our free, open-source AI co-scientist that runs locally on your own machine and is powered by Scientific Agent Skills. We cover how to set it up, bring your own API keys, and run real research workflows with these skills. No prior technical experience needed. Watch the recording →

Stay up to date: Follow K-Dense on X, LinkedIn, YouTube, and Reddit for new skills, release announcements, walkthroughs, research workflow demos, and examples you can use with your own AI agent.

A comprehensive collection of 163 ready-to-use scientific and research skills (covering cancer genomics, individual-level 1000 Genomes queries, hosted regulatory-sequence prediction, live pathogen-variant surveillance, analytical method validation, PK/PD modelling and dose selection, full-text biomedical and regulatory literature retrieval, drug-target binding, bounded biomedical knowledge graph search, molecular dynamics, RNA velocity, microbiome foundation models, geospatial science, time series forecasting, scientific ML resource discovery via Hugging Science, 78+ scientific databases, and more) for any AI agent that supports the open Agent Skills standard, created by K-Dense. The repository is also a portable Agent Plugins package (plugin.json + skills/), so plugin-capable clients can load the whole collection as one plugin. Works with Cursor, Claude Code, Codex, Google Antigravity, and more. Transform your AI agent into a research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and beyond.

⭐ Help make AI for science easier to discover: If Scientific Agent Skills saves you time, teaches your agent a workflow, or helps your lab move faster, please star this repository. A star is a public signal that these open, reusable research skills are worth maintaining: it helps scientists, engineers, and open-source contributors find the project, shows which agent-skill standards are gaining real adoption, and gives us a clear reason to keep expanding the collection for the community.

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These skills enable your AI agent to seamlessly work with specialized scientific libraries, databases, and tools across multiple scientific domains. While the agent can use any Python package or API on its own, these explicitly defined skills provide curated documentation and examples that make it significantly stronger and more reliable for the workflows below:

• 🧬 Bioinformatics & Genomics - Sequence analysis, single-cell RNA-seq, gene regulatory networks, variant annotation, phylogenetic analysis
• 🧪 Cheminformatics & Drug Discovery - Molecular property prediction, virtual screening, ADMET analysis, molecular docking, lead optimization
• 🔬 Proteomics & Mass Spectrometry - LC-MS/MS processing, peptide identification, spectral matching, protein quantification
• 🏥 Clinical Research & Evidence Workflows - Clinical trials, pharmacogenomics, variant evidence review, pharmacokinetic/pharmacodynamic modelling and dose-regimen evaluation, aggregate decision-support evaluation, source-bound draft report structures, and formatting of clinician-authored treatment decisions
• 🧠 Healthcare AI & Biosignal Research - EHR and model research, physiological signal analysis, and retrospective validation—not patient-specific diagnosis, treatment, alarms, or deployment decisions
• 🐭 Preclinical Research & Animal Welfare - Multivariate severity scoring and humane-endpoint forecasting for laboratory animal studies, for 3Rs/refinement analysis and EU Directive 2010/63/EU reporting—an aid to severity assessment, never a decision rule
• 🖼️ Medical Imaging & Digital Pathology - Privacy-aware DICOM processing and research-only whole-slide image analysis, computational pathology, and radiology data workflows
• 🤖 Machine Learning & AI - Deep learning, reinforcement learning, time series analysis, model interpretability, Bayesian methods
• 🔮 Materials Science & Chemistry - Crystal structure analysis, phase diagrams, metabolic modeling, computational chemistry
• 🌌 Physics & Astronomy - Astronomical data analysis, coordinate transformations, cosmological calculations, symbolic mathematics, physics computations
• ⚙️ Engineering & Simulation - Discrete-event simulation, multi-objective optimization, metabolic engineering, systems modeling, process optimization
• 📊 Data Analysis & Visualization - Statistical analysis, network analysis, time series, publication-quality figures, large-scale data processing, EDA
• 🌍 Geospatial Science & Remote Sensing - Satellite imagery processing, GIS analysis, spatial statistics, terrain analysis, machine learning for Earth observation
• 🧪 Laboratory Automation - Liquid handling protocols, lab equipment control, workflow automation, LIMS integration
• 📚 Scientific Communication - Evidence-traceable writing, confidential authorized peer review, literature synthesis, document processing, macro-free PPTX posters, slides, schematics, and citation management
• 🔬 Multi-omics & Systems Biology - Multi-modal data integration, pathway analysis, network biology, systems-level insights
• 🧬 Protein Engineering & Design - Protein language models, structure prediction, sequence design, function annotation
• 🧰 Agent Platforms & Infrastructure - Build on Pi with SDK, RPC, extensions, custom providers/models, packages, TUI components, and session tooling
• 🎓 Research Methodology - Evidence-bounded candidate hypotheses, scientific brainstorming, critical thinking, grant writing, and qualitative low-stakes evaluation of scholarly works
• ⚖️ Regulatory & Standards - Draft evidence-preparation artifacts for ISO management-system and laboratory standards, plus analytical method validation, verification, and transfer under ICH/USP/CLSI frameworks—prepared for qualified review, never a certification, accreditation, or method-release decision

Transform your AI coding agent into an 'AI Scientist' on your desktop!

🎬 New to Scientific Agent Skills? Watch our Getting Started with Scientific Agent Skills video for a quick walkthrough.

🎥 More tutorials

Recorded walkthroughs of these skills on real research tasks, from the K-Dense YouTube channel:

Video	What it covers
Skills 101: Build Your Own Scientific Agent Skill	Writing, testing, and packaging a new skill from scratch
Literature Review and Hypothesis Generation	Searching the literature and generating grounded hypotheses
Draft and Budget an Experimental Protocol	Turning a planned experiment into a costed, written protocol
Draft Responses to Reviewer Comments	Building a point-by-point rebuttal from reviewer feedback
Can AI Reproduce a Nature Medicine Paper?	An end-to-end reproduction attempt on a published analysis

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📦 What's Included

This repository provides 163 scientific and research skills organized into the following categories:

• 100+ Scientific & Financial Databases - A unified database-lookup skill provides deterministic, provenance-rich access to 78 public databases (PubChem, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, FRED, USPTO, and more), plus dedicated skills for DepMap, Imaging Data Commons, PrimeKG, NCATS ARAX, U.S. Treasury Fiscal Data, Hugging Science, OneKGPd, and Genomic Intelligence. Multi-database packages like BioServices (~40 bioinformatics services), BioPython (39 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage
• 70+ Optimized Python Package Skills - Explicitly defined, version-aware workflows for RDKit, Scanpy, PyTorch Lightning, scikit-learn, PyTDC, PathML, pydicom, NeuroKit2, PufferLib, QuTiP, GeoPandas, pymatgen, BioPython, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), and others. The agent can still use any Python package; these skills provide stronger, safer guidance for the packages listed
• 9 Scientific Integration Skills - Explicitly defined skills for Benchling, DNAnexus, LatchBio, OMERO, Protocols.io, Open Notebook, Ginkgo Cloud Lab, LabArchives, and Opentrons. Again, the agent is not limited to these — any API or platform reachable from Python is fair game; these skills are the optimized, pre-documented paths
• 30+ Analysis & Communication Tools - Literature review, evidence-traceable scientific writing, confidential peer review, document processing, Paperclip (full-text papers, FDA/PMDA/EMA filings, and trial registries with line-pinned citations), Paperzilla, Exa Search, macro-free PPTX posters, slides, schematics, infographics, Mermaid diagrams, and more
• 10+ Research & Clinical Tools - Evidence-bounded hypothesis generation, grant writing, aggregate clinical decision-support research, clinician-authored treatment-plan formatting, PK/PD modelling and simulation (NCA, population PK, exposure-response, bioequivalence, first-in-human dose), BIDS, ISO standards-readiness evidence preparation (ISO 13485, ISO 14971, ISO/IEC 17025, ISO 15189), analytical method validation and transfer (ICH Q2(R2)/Q14, ICH M10, USP, CLSI EP), scenario analysis, and workflow-derived skill drafting with Autoskill

Each skill includes:

• ✅ Comprehensive documentation (SKILL.md)
• ✅ Practical code examples
• ✅ Use cases and best practices
• ✅ Integration guides
• ✅ Reference materials
• ✅ A test suite for every skill that ships scripts/ — CI blocks a pull request that adds bundled tooling without one

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📋 Table of Contents

• What's Included
• Why Use This?
• Getting Started
• Security Disclaimer
• Support Open Source
• Prerequisites
• Quick Examples
• Use Cases
• Available Skills
• From the Blog
• Contributing
• Troubleshooting
• FAQ
• Support
• Citation
• License

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🚀 Why Use This?

⚡ Accelerate Your Research

• Save Days of Work - Skip API documentation research and integration setup
• Reviewed Starting Points - Tested examples with explicit validation, provenance, and safety boundaries; verify them in the target environment
• Multi-Step Workflows - Execute complex pipelines with a single prompt

🎯 Comprehensive Coverage

• 163 Skills - Extensive coverage across all major scientific domains
• 100+ Databases - Unified access to 78+ databases via database-lookup, plus dedicated data access skills and multi-database packages like BioServices, BioPython, and gget
• 70+ Optimized Python Package Skills - Current, version-scoped guidance for packages including RDKit, Scanpy, PyTorch Lightning, scikit-learn, PyTDC, pydicom, PufferLib, QuTiP, GeoPandas, pymatgen, Qiskit, Molecular Dynamics (OpenMM/MDAnalysis), scVelo, and TimesFM (the agent can use any Python package; these are the pre-documented paths)

🔧 Easy Integration

• Simple Setup - Copy skills to your skills directory and start working
• Configured Discovery - Compatible hosts can find and use relevant skills from their configured skill paths
• Well Documented - Each skill includes examples, use cases, and best practices

🌟 Maintained & Supported

• Regular Updates - Continuously maintained and expanded by K-Dense team
• Tested in CI - Every skill that ships scripts/ has a suite under tests/, plus a repo-wide structural contract (frontmatter, link resolution, script parsing, --help behavior) that runs on every pull request
• Community Driven - Open source with active community contributions
• Enterprise Ready - Commercial support available for advanced needs

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🎯 Getting Started

Option 1: npx (supported hosts)

Install Scientific Agent Skills with a single command:

npx skills add K-Dense-AI/scientific-agent-skills

This is a common standards-based installer for supported Agent Skills hosts, including current versions of Claude Code, Claude Cowork, Codex, Gemini CLI, Google Antigravity, and Cursor. Confirm installation paths and optional metadata behavior in your host's current documentation.

Option 2: GitHub CLI (gh skill)

If you use the GitHub CLI (v2.90.0+), you can install skills with gh skill:

# Browse and install interactively
gh skill install K-Dense-AI/scientific-agent-skills

# Install a specific skill directly
gh skill install K-Dense-AI/scientific-agent-skills scanpy

# Target a specific agent host
gh skill install K-Dense-AI/scientific-agent-skills --agent cursor
gh skill install K-Dense-AI/scientific-agent-skills --agent claude-code
gh skill install K-Dense-AI/scientific-agent-skills --agent codex
gh skill install K-Dense-AI/scientific-agent-skills --agent gemini

gh skill automatically installs to the correct directory for your agent host and records provenance metadata for supply chain integrity.

Version pinning

Pin to a specific release tag or commit SHA for reproducible installs:

# Pin to a release tag
gh skill install K-Dense-AI/scientific-agent-skills --pin v2.65.0

# Pin to a commit SHA
gh skill install K-Dense-AI/scientific-agent-skills --pin abc123def

Keeping skills up to date

# Check for updates interactively
gh skill update

# Update all installed skills
gh skill update --all

Option 3: Agent Plugins (Cursor, Codex, and other plugin clients)

This repository is a valid Agent Plugins 1.0.0 package: root plugin.json plus Agent Skills under skills/. Clients that support the standard discover every immediate child of skills/ that contains a SKILL.md.

Cursor — symlink or copy the repo into the local plugins directory, then reload:

mkdir -p ~/.cursor/plugins/local
ln -s "$(pwd)" ~/.cursor/plugins/local/scientific-agent-skills

Restart Cursor or run Developer: Reload Window, then confirm the plugin and its skills appear under Customize. See Cursor plugins.

Codex — install from a local checkout (confirm the current CLI flag names in Codex docs):

codex plugins install .

Compatible clients (Cursor, Codex, GitHub Copilot, VS Code, Kiro, and others listed at agent-plugins.org) share the same package layout; installation UX stays client-specific.

Other Agent Skills hosts (OpenClaw, NemoClaw, Pi, Hermes, …)

Agent hosts differ in install paths, discovery settings, and support for optional frontmatter fields. npx skills add (Option 1) commonly installs into the ~/.agents/skills/ convention, with project-scoped installs under .agents/skills/; confirm both paths against your host's current documentation. To install manually on a host configured to scan one of those locations:

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git ~/.agents/skills/scientific-agent-skills   # user-level
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git .agents/skills/scientific-agent-skills      # project-level

For Hermes versions that support skill taps, add the repository as a tap:

hermes skills tap add K-Dense-AI/scientific-agent-skills

Every SKILL.md has YAML frontmatter, but legacy and community skills vary in metadata formatting (block or flow style) and optional extension fields. Repository updates must keep metadata.version as a quoted numeric string and pass canonical skills-ref validate ./skills/<skill-name> checks. Hosts may interpret optional metadata and credential prompts differently, so verify behavior on the target host. Because 163 skills add up to a lot of standing context, consider installing a topical subset rather than the whole collection.

NemoClaw note: NemoClaw runs agents inside NVIDIA OpenShell with default-deny outbound networking. Skills are discovered and loaded normally, but any skill that needs the network — package installs via uv, or API calls (Exa, Parallel, Benchling, NCBI, Materials Project, …) — only works once the operator pre-approves the relevant domains in the OpenShell TUI.

That's it! A compatible host can discover the skills from its configured paths and use them when relevant. You can also invoke any skill manually by mentioning the skill name in your prompt.

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⚠️ Security Disclaimer

Skills can execute code and influence your coding agent's behavior. Review what you install.

Agent Skills are powerful — they can instruct your AI agent to run arbitrary code, install packages, make network requests, and modify files on your system. A malicious or poorly written skill has the potential to steer your coding agent into harmful behavior.

We take security seriously. All contributions go through a review process, and we run LLM-based security scans (via Cisco AI Defense Skill Scanner) on every skill in this repository. However, as a small team with a growing number of community contributions, we cannot guarantee that every skill has been exhaustively reviewed for all possible risks.

It is ultimately your responsibility to review the skills you install and decide which ones to trust.

We recommend the following:

• Do not install everything at once. Only install the skills you actually need for your work. While installing the full collection was reasonable when K-Dense created and maintained every skill, the repository now includes many community contributions that we may not have reviewed as thoroughly.
• Read the SKILL.md before installing. Each skill's documentation describes what it does, what packages it uses, and what external services it connects to. If something looks suspicious, don't install it.
• Check the contribution history. Skills authored by K-Dense (K-Dense-AI) have been through our internal review process. Community-contributed skills have been reviewed to the best of our ability, but with limited resources.
• Run the security scanner yourself. Before installing third-party skills, scan them locally:

  uv pip install cisco-ai-skill-scanner
  skill-scanner scan /path/to/skill --use-behavioral
  

• Report anything suspicious. If you find a skill that looks malicious or behaves unexpectedly, please open an issue immediately so we can investigate.

Skills are scanned weekly — incrementally, so unchanged skills carry their previous findings forward, with a full rescan of everything at least every 30 days and whenever the scanner or model changes — and the results are published to docs/security-report.md. See SECURITY.md for our security policy, what is in scope, how to report a vulnerability privately, and how to contest a scan finding. We try to address security gaps as they arise.

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❤️ Support the Open Source Community

Scientific Agent Skills is powered by 50+ incredible open source projects maintained by dedicated developers and research communities worldwide. Projects like Biopython, Scanpy, RDKit, scikit-learn, PyTorch Lightning, and many others form the foundation of these skills.

If you find value in this repository, please consider supporting the projects that make it possible:

• ⭐ Star their repositories on GitHub
• 💰 Sponsor maintainers via GitHub Sponsors or NumFOCUS
• 📝 Cite projects in your publications
• 💻 Contribute code, docs, or bug reports

👉 View the full list of projects to support

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🙏 Skill Credits

The docx, pdf, pptx, and xlsx document skills are created and maintained by Anthropic and vendored here from anthropics/skills. They are used under Anthropic's terms — see each skill's LICENSE.txt — and we track upstream so you get their latest improvements. All credit for those four skills goes to Anthropic.

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⚙️ Prerequisites

• Python: 3.13+ for repository tooling; individual skill dependencies may support broader Python ranges
• uv: Python package manager (required for installing skill dependencies)
• Client: Any agent that supports the Agent Skills standard (Cursor, Claude Code, Gemini CLI, Codex, Google Antigravity, etc.)
• System: macOS, Li

…

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docx

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name: docx description: "Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation." license: Proprietary. LICENSE.txt has complete terms metadata: version: "2.1" skill-author: Anthropic, PBC source: https://github.com/anthropics/skills/tree/main/skills/docx

DOCX creation, editing, and analysis

A .docx is a ZIP archive of XML files. Choose your approach by task:

Task	Approach
Create a new document	Write a docx (npm) script — see gotchas below
Edit an existing document	unzip → edit word/document.xml → zip (docx-js cannot open existing files)
Read content	pandoc -t markdown file.docx

Script paths below are relative to this skill's directory.

Creating with docx-js — gotchas

docx is preinstalled — do not run npm install first; write the script and require('docx') directly. Only if that require fails: npm install docx. The model knows the API; these are the footguns:

• Page size defaults to A4. For US Letter set page: { size: { width: 12240, height: 15840 } } (DXA; 1440 = 1″).
• Landscape: pass portrait dimensions and orientation: PageOrientation.LANDSCAPE — docx-js swaps width/height internally.
• Tables need dual widths: set columnWidths on the table AND width on every cell, both in WidthType.DXA (PERCENTAGE breaks in Google Docs). Column widths must sum to the table width.
• Table shading: use ShadingType.CLEAR, never SOLID (renders black).
• Lists: never insert • literally; use a numbering config with LevelFormat.BULLET.
• ImageRun requires type: ("png", "jpg", …).
• PageBreak must be inside a Paragraph.
• Never use \n — use separate Paragraph elements.
• TOC: headings must use built-in HeadingLevel.*; custom heading styles need outlineLevel set or they won't appear.
• Don't use a table as a horizontal rule — use a paragraph bottom border instead.
• Dot-leader / right-aligned-on-same-line: use PositionalTab (alignment: PositionalTabAlignment.RIGHT, leader: PositionalTabLeader.DOT) inside a TextRun, not literal . or space padding.

Verify the output

After writing a .docx, render it and look at it:

python scripts/office/soffice.py --headless --convert-to pdf output.docx
pdftoppm -jpeg -r 100 output.pdf page
ls page-*.jpg   # then Read the images

pdftoppm zero-pads page numbers to the width of the page count (page-01.jpg…page-12.jpg).

Editing existing documents

Legacy .doc files must be converted first: python scripts/office/soffice.py --headless --convert-to docx file.doc.

unzip -q doc.docx -d unpacked/
find unpacked -type l -delete   # strip symlink entries — docx from external parties is untrusted
python scripts/merge_runs.py unpacked/   # coalesce fragmented runs so text is findable
# edit unpacked/word/document.xml in place — do NOT reformat or pretty-print
(cd unpacked && rm -f ../out.docx && zip -Xr ../out.docx .)
python scripts/office/validate.py out.docx --original doc.docx   # XSD checks; --auto-repair fixes common issues
# redlining? add --author "<the name you redlined under>" to check every edit is tracked

Word splits text across many <w:r> runs (revision ids, spell-check markers), so a phrase you can see in the document often doesn't exist as a contiguous string in the XML. merge_runs.py merges adjacent identically-formatted runs in word/document.xml without changing content or rendering; it also accepts a .docx directly (python scripts/merge_runs.py doc.docx -o merged.docx).

Tracked changes: when redlining, validate with --author "<the name you redlined under>" (needs --original) — it reports any text you changed without a <w:ins>/<w:del> around it, which is easy to do by accident and invisible in the accepted view. Wrap runs in <w:ins>/<w:del> with w:id, w:author, w:date attributes. Inside <w:del>, the text element is <w:delText>, not <w:t>. A deleted paragraph mark (<w:pPr><w:rPr><w:del w:id=".." w:author=".." w:date=".."/></w:rPr></w:pPr>) means "merge this paragraph into the next" — so deleting a paragraph outright is that plus a <w:del> around every run. The <w:del/> must come before the rPr's other children; their order is schema-enforced.

To produce a clean copy with all tracked changes accepted: python scripts/accept_changes.py in.docx out.docx.

Accepting a deleted paragraph mark should join that paragraph to the one below it, so a paragraph whose runs are all deleted vanishes. Word does this; accept_changes.py and pandoc --track-changes=accept don't always. Both fail the same way — they strip the deleted text but leave the emptied paragraph behind, which reads as a stray empty bullet when it was auto-numbered:

• pandoc --track-changes=accept never joins the paragraphs.
• accept_changes.py (LibreOffice) joins them correctly, except when the deleted paragraph is followed by an empty spacer paragraph.

An empty bullet in either view is an artifact of that view, not a defect in the document. Check paragraph deletions in the XML.

Comments

Comments require six cross-linked files. Use the helper — directory mode when you'll also be editing document.xml (saves an unzip/rezip cycle), .docx-direct mode otherwise:

# Against an already-unpacked directory (preferred when also placing markers)
python scripts/comment.py unpacked/ "Fees & expenses cap is too low"
python scripts/comment.py unpacked/ "Agreed" --parent 0

# Against a .docx directly
python scripts/comment.py contract.docx "This cap is too low" -o annotated.docx

The script writes comments.xml, commentsExtended.xml, commentsIds.xml, commentsExtensible.xml, the relationships, and the content-type overrides. Comment IDs are auto-assigned. It then prints the <w:commentRangeStart>/<w:commentRangeEnd>/<w:commentReference> snippet to add to word/document.xml so the comment anchors to specific text — until you place those markers, the comment exists but is not visible.

Dependencies

docx (npm, preinstalled — install only if require('docx') fails) · pandoc · LibreOffice (soffice) · pdftoppm (Poppler)

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This skill is created and maintained by Anthropic. Vendored here unmodified except for frontmatter metadata; see LICENSE.txt for terms.

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pdf

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name: pdf description: Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill. license: Proprietary. LICENSE.txt has complete terms metadata: version: "1.2" skill-author: Anthropic, PBC source: https://github.com/anthropics/skills/tree/main/skills/pdf

PDF Processing Guide

Overview

This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see reference.md. If you need to fill out a PDF form, read forms.md and follow its instructions.

Quick Start

from pypdf import PdfReader, PdfWriter

# Read a PDF
reader = PdfReader("document.pdf")
print(f"Pages: {len(reader.pages)}")

# Extract text
text = ""
for page in reader.pages:
    text += page.extract_text()

Python Libraries

pypdf - Basic Operations

Merge PDFs

from pypdf import PdfWriter, PdfReader

writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
    reader = PdfReader(pdf_file)
    for page in reader.pages:
        writer.add_page(page)

with open("merged.pdf", "wb") as output:
    writer.write(output)

Split PDF

reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
    writer = PdfWriter()
    writer.add_page(page)
    with open(f"page_{i+1}.pdf", "wb") as output:
        writer.write(output)

Extract Metadata

reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"Title: {meta.title}")
print(f"Author: {meta.author}")
print(f"Subject: {meta.subject}")
print(f"Creator: {meta.creator}")

Rotate Pages

reader = PdfReader("input.pdf")
writer = PdfWriter()

page = reader.pages[0]
page.rotate(90)  # Rotate 90 degrees clockwise
writer.add_page(page)

with open("rotated.pdf", "wb") as output:
    writer.write(output)

pdfplumber - Text and Table Extraction

Extract Text with Layout

import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    for page in pdf.pages:
        text = page.extract_text()
        print(text)

Extract Tables

with pdfplumber.open("document.pdf") as pdf:
    for i, page in enumerate(pdf.pages):
        tables = page.extract_tables()
        for j, table in enumerate(tables):
            print(f"Table {j+1} on page {i+1}:")
            for row in table:
                print(row)

Advanced Table Extraction

import pandas as pd

with pdfplumber.open("document.pdf") as pdf:
    all_tables = []
    for page in pdf.pages:
        tables = page.extract_tables()
        for table in tables:
            if table:  # Check if table is not empty
                df = pd.DataFrame(table[1:], columns=table[0])
                all_tables.append(df)

# Combine all tables
if all_tables:
    combined_df = pd.concat(all_tables, ignore_index=True)
    combined_df.to_excel("extracted_tables.xlsx", index=False)

reportlab - Create PDFs

Basic PDF Creation

from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

c = canvas.Canvas("hello.pdf", pagesize=letter)
width, height = letter

# Add text
c.drawString(100, height - 100, "Hello World!")
c.drawString(100, height - 120, "This is a PDF created with reportlab")

# Add a line
c.line(100, height - 140, 400, height - 140)

# Save
c.save()

Create PDF with Multiple Pages

from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
from reportlab.lib.styles import getSampleStyleSheet

doc = SimpleDocTemplate("report.pdf", pagesize=letter)
styles = getSampleStyleSheet()
story = []

# Add content
title = Paragraph("Report Title", styles['Title'])
story.append(title)
story.append(Spacer(1, 12))

body = Paragraph("This is the body of the report. " * 20, styles['Normal'])
story.append(body)
story.append(PageBreak())

# Page 2
story.append(Paragraph("Page 2", styles['Heading1']))
story.append(Paragraph("Content for page 2", styles['Normal']))

# Build PDF
doc.build(story)

Subscripts and Superscripts

IMPORTANT: Never use Unicode subscript/superscript characters (₀₁₂₃₄₅₆₇₈₉, ⁰¹²³⁴⁵⁶⁷⁸⁹) in ReportLab PDFs. The built-in fonts do not include these glyphs, causing them to render as solid black boxes.

Instead, use ReportLab's XML markup tags in Paragraph objects:

from reportlab.platypus import Paragraph
from reportlab.lib.styles import getSampleStyleSheet

styles = getSampleStyleSheet()

# Subscripts: use <sub> tag
chemical = Paragraph("H<sub>2</sub>O", styles['Normal'])

# Superscripts: use <super> tag
squared = Paragraph("x<super>2</super> + y<super>2</super>", styles['Normal'])

For canvas-drawn text (not Paragraph objects), manually adjust font the size and position rather than using Unicode subscripts/superscripts.

Command-Line Tools

pdftotext (poppler-utils)

# Extract text
pdftotext input.pdf output.txt

# Extract text preserving layout
pdftotext -layout input.pdf output.txt

# Extract specific pages
pdftotext -f 1 -l 5 input.pdf output.txt  # Pages 1-5

qpdf

# Merge PDFs
qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf

# Split pages
qpdf input.pdf --pages . 1-5 -- pages1-5.pdf
qpdf input.pdf --pages . 6-10 -- pages6-10.pdf

# Rotate pages
qpdf input.pdf output.pdf --rotate=+90:1  # Rotate page 1 by 90 degrees

# Remove password
qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf

pdftk (if available)

# Merge
pdftk file1.pdf file2.pdf cat output merged.pdf

# Split
pdftk input.pdf burst

# Rotate
pdftk input.pdf rotate 1east output rotated.pdf

Common Tasks

Extract Text from Scanned PDFs

# Requires: uv pip install pytesseract pdf2image
import pytesseract
from pdf2image import convert_from_path

# Convert PDF to images
images = convert_from_path('scanned.pdf')

# OCR each page
text = ""
for i, image in enumerate(images):
    text += f"Page {i+1}:\n"
    text += pytesseract.image_to_string(image)
    text += "\n\n"

print(text)

Add Watermark

from pypdf import PdfReader, PdfWriter

# Create watermark (or load existing)
watermark = PdfReader("watermark.pdf").pages[0]

# Apply to all pages
reader = PdfReader("document.pdf")
writer = PdfWriter()

for page in reader.pages:
    page.merge_page(watermark)
    writer.add_page(page)

with open("watermarked.pdf", "wb") as output:
    writer.write(output)

Extract Images

# Using pdfimages (poppler-utils)
pdfimages -j input.pdf output_prefix

# This extracts all images as output_prefix-000.jpg, output_prefix-001.jpg, etc.

Password Protection

from pypdf import PdfReader, PdfWriter

reader = PdfReader("input.pdf")
writer = PdfWriter()

for page in reader.pages:
    writer.add_page(page)

# Add password
writer.encrypt("userpassword", "ownerpassword")

with open("encrypted.pdf", "wb") as output:
    writer.write(output)

Quick Reference

Task	Best Tool	Command/Code
Merge PDFs	pypdf	writer.add_page(page)
Split PDFs	pypdf	One page per file
Extract text	pdfplumber	page.extract_text()
Extract tables	pdfplumber	page.extract_tables()
Create PDFs	reportlab	Canvas or Platypus
Command line merge	qpdf	qpdf --empty --pages ...
OCR scanned PDFs	pytesseract	Convert to image first
Fill PDF forms	pdf-lib or pypdf (see forms.md)	See forms.md

Next Steps

• For advanced pypdfium2 usage, see reference.md
• For JavaScript libraries (pdf-lib), see reference.md
• If you need to fill out a PDF form, follow the instructions in forms.md
• For troubleshooting guides, see reference.md

---

This skill is created and maintained by Anthropic. Vendored here unmodified except for frontmatter metadata and the case of the reference.md/forms.md links, which upstream writes uppercase; see LICENSE.txt for terms.

---

pptx

---

name: pptx description: "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." license: Proprietary. LICENSE.txt has complete terms metadata: version: "2.1" skill-author: Anthropic, PBC source: https://github.com/anthropics/skills/tree/main/skills/pptx

PPTX creation, editing, and analysis

A .pptx is a ZIP archive of XML files. Choose your approach by task:

Task	Approach
Create a new deck	Write a pptxgenjs script — see gotchas below
Edit an existing deck, or build from a template	unzip → edit ppt/slides/slideN.xml → zip
Read content	markitdown deck.pptx (one block per slide under <!-- Slide number: N --> markers); visual grid: python scripts/thumbnail.py deck.pptx

Scripts

Paths are relative to this skill's directory. Everything else is plain Python, node, or shell.

Script	What it does
scripts/thumbnail.py deck.pptx [prefix]	Labeled grid of every slide, for picking template layouts. .pptx only. Pass prefix — it defaults to thumbnails, which overwrites the grids of any other deck done in the same directory
scripts/add_slide.py unpacked/ slide2.xml [--after slideN.xml]	Duplicate a slide (or a slideLayoutN.xml) with all the package bookkeeping. Also takes a .pptx directly with -o out.pptx
scripts/clean.py unpacked/	Delete slides, media, and rels no longer referenced. Run after <p:sldIdLst> is final
scripts/office/validate.py deck.pptx [--original src.pptx]	Schema, relationship, content-type, chart and slide checks; each failure names its fix. Pass --original for any template-derived deck — it baselines the schema checks against the template, so the template's own XSD errors don't read as yours
scripts/office/soffice.py --headless --convert-to pdf deck.pptx	LibreOffice wrapper — bare soffice hangs in this sandbox

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 LAYOUT_16x9 = 10" × 5.625", not 13.3" wide. Coordinates past the edge are written, not clamped — the shape just isn't on the slide. (LAYOUT_WIDE 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 ROUNDED_RECTANGLE, 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.
• Default charts render bare — no title, no data labels, dated palette. Set showTitle + title, showValue: true + dataLabelPosition, chartColors: [...] from your palette, and quiet the frame (catAxisLabelColor/valAxisLabelColor, valGridLine: { color, size }, catGridLine: { style: "none" }, showLegend: false for a single series).
• On a stacked bar or column chart, dataLabelPosition must be ctr, inEnd, or inBase. outEnd corrupts the file.
• A combo series using secondaryValAxis/secondaryCatAxis needs both valAxes and catAxes on the chart options, two entries each. Without them pptxgenjs writes axis ids it never declares, and PowerPoint discards that chart and reports the file as corrupt. Supplying only valAxes is not enough.
• After writeFile(), run python scripts/office/validate.py deck.pptx. It reports the two chart faults above and the slide-XML defects PowerPoint refuses, and names the fix for each. Fix them in your generator, not by hand-editing the packed XML.
• Never reorder the children of <p:presentation>. pptxgenjs writes <p:notesMasterIdLst> right after <p:sldIdLst> and points both masters at one theme part. PowerPoint reads that happily — move the element and the same deck becomes unopenable.
• Icons: render react-icons to SVG (ReactDOMServer.renderToStaticMarkup), rasterize with sharp at ≥256px, and insert via addImage({ data: "image/png;base64," + buf.toString("base64") }) — the image/png;base64, prefix is required (react-icons, react, react-dom, and sharp are preinstalled — npm install react-icons react react-dom sharp only if a require fails).

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 <p:sldIdLst>, including one you just wrote.
• Never copy a slide file by hand — add_slide.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: add_slide.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 add_slide(layout)), preserve formatting through text_frame.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 <a:p> per list item — never concatenate items into a single paragraph. Copy the sibling <a:pPr> to preserve spacing, and put b="1" on the <a:rPr> of titles, section headers, and inline labels (Status:, Owner:).
• Let bullets inherit from the layout; only add <a:buChar>, <a:buAutoNum> (numbered), or <a:buNone> to override — never a literal • in the text.
• Text with leading or trailing spaces needs xml:space="preserve" on its <a:t>.

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:

Theme	Primary	Secondary	Accent
Midnight Executive	1E2761 (navy)	CADCFC (ice blue)	FFFFFF (white)
Forest & Moss	2C5F2D (forest)	97BC62 (moss)	F5F5F5 (cream)
Coral Energy	F96167 (coral)	F9E795 (gold)	2F3C7E (navy)
Warm Terracotta	B85042 (terracotta)	E7E8D1 (sand)	A7BEAE (sage)
Ocean Gradient	065A82 (deep blue)	1C7293 (teal)	21295C (midnight)
Charcoal Minimal	36454F (charcoal)	F2F2F2 (off-white)	212121 (black)
Teal Trust	028090 (teal)	00A896 (seafoam)	02C39A (mint)
Berry & Cream	6D2E46 (berry)	A26769 (dusty rose)	ECE2D0 (cream)
Sage Calm	84B59F (sage)	69A297 (eucalyptus)	50808E (slate)
Cherry Bold	990011 (cherry)	FCF6F5 (off-white)	2F3C7E (navy)

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.

Element	Size
Slide title	36-44pt bold
Section header	20-24pt bold
Body text	14-16pt
Captions	10-12pt muted

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
• Don't ship text that overflows its shape — if text doesn't fit, reduce font size, split across slides, or enlarge the container; never leave content cut off or spilling past bounds

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.pptx

Check 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 template

If 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-*.jpg

Pass 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; see LICENSE.txt for terms.

---

xlsx

---

name: xlsx description: "Create, edit, analyze, or convert Excel spreadsheets (.xlsx, .xlsm, .xltx) where the workbook file is the primary deliverable. Use for formulas, formatting, financial models, multi-sheet workbooks, and tabular cleanup exported to Excel. Also applies to .csv/.tsv when the user wants spreadsheet output. Do NOT use for Word documents, HTML reports, standalone Python scripts, database pipelines, or Google Sheets API work." allowed-tools: Read Write Edit Bash Grep Glob license: Proprietary. LICENSE.txt has complete terms metadata: version: "2.2" skill-author: Anthropic, PBC adapted-by: K-Dense Inc. source: https://github.com/anthropics/skills/tree/main/skills/xlsx compatibility: Requires Python 3.8+, LibreOffice (soffice on PATH), and gcc only when Unix sockets are restricted

XLSX creation, editing, and analysis

Task	Approach
Create or edit with formulas/formatting	openpyxl — see gotchas below
Bulk data in or out	pandas (read_excel, to_excel)
Quick look at a sheet	markitdown file.xlsx — ## SheetName per sheet; reads .xlsm too. No cell coordinates, so don't plan edits from it
Read a model (formulas and values)	two load_workbook passes — see gotchas

openpyxl, pandas, and markitdown are preinstalled — do not run uv pip install first; write the script and import directly. Only if an import fails (or the markitdown command is missing): uv pip install the missing package.

Script paths below are relative to this skill's directory.

Requirements for every output

• Professional font (Arial, Times New Roman) throughout, unless the user says otherwise.
• Zero formula errors. Never ship while recalc.py reports errors_found. If you think an error predates you, prove it: load the original with data_only=True and look at that cell. An error you introduced looks exactly like one you inherited.
• Use formulas, never hardcoded results. Write sheet['B10'] = '=SUM(B2:B9)', not the Python-computed total. The sheet must recalculate when its inputs change.
• Follow the user's spec literally. Exact tab names, exact column headers, and the formula they spelled out. A redesign that computes something else fails, however elegant.
• Document every assumption and hardcoded number where the reader will see it — a cell comment, or an adjacent cell at a table's end. Cite a real source when one exists (Source: Company 10-K, FY2024, Page 45, Revenue Note, [SEC EDGAR URL]); when the number came from the user, say so plainly.
• A workbook you create for someone to fill in needs a short legend naming which cells to edit, and one example row of realistic values showing the expected format. Never add such a row to a file you were asked to edit.
• Editing an existing file: match its conventions exactly. They override every guideline here. Find its designated input cells first — a distinct font color, fill, or shading marks them — write only there, and leave every existing formula untouched.

Recalculate (mandatory whenever the file contains formulas)

openpyxl writes formulas as strings with no cached values. Until you recalculate, every formula cell reads back as None to anything reading cached values — pandas, load_workbook(data_only=True), and most previewers.

python scripts/recalc.py output.xlsx [timeout_seconds]   # default 30

LibreOffice computes every formula, the file is rewritten in place, and you get JSON: status (success | errors_found), total_formulas, total_errors, and an error_summary naming up to 100 cells per error type (locations_truncated says how many it withheld — trust total_errors, not the length of the list). Fix what it names and run it again. JSON with an error key instead of a status means nothing was recalculated, and only that case exits non-zero — errors_found exits 0, so never treat a clean exit as a clean workbook.

A green recalc proves your formulas evaluate, not that they are right. An off-by-one range or a reference to the wrong row yields a clean, error-free file with wrong numbers. Write 2–3 formulas first and check they pull the values you expect, before building out a grid.

A workbook that links to another file loses those links if you re-save it with openpyxl and then recalculate. Such a formula reads ='[1]Returns Analysis'!$B$2 — the [1] is an index into the workbook's external-reference list, naming a separate file on disk, not a sheet. That file is rarely present here, so the cell's cached value is the only thing holding its data. openpyxl strips that value on save; LibreOffice then has to resolve the reference for real, fails, writes #NAME?, and deletes every link. recalc.py refuses to run in that state — copy those cells' values out of the original before you save over them (--force overrides, and accepts the loss).

Choosing formulas that survive verification

LibreOffice implements fewer functions than Excel, and one it cannot evaluate becomes a literal #NAME? baked into the file you deliver.

• Prefer Excel-2007-era functions — SUMIFS, INDEX, MATCH, IFERROR, SUMPRODUCT — which need no prefix.
• Six post-2007 functions work, but only with an _xlfn. prefix, because openpyxl writes your formula into the XML verbatim and Excel stores post-2007 names prefixed (its UI hides the prefix): _xlfn.TEXTJOIN, _xlfn.CONCAT, _xlfn.IFS, _xlfn.SWITCH, _xlfn.MAXIFS, _xlfn.MINIFS. Written bare, each yields #NAME?.
• Never use XLOOKUP, XMATCH, SORT, FILTER, UNIQUE, or SEQUENCE. The runtime's LibreOffice cannot evaluate them under any prefix. Newer builds do evaluate them, but they are spilling array functions and an openpyxl-written file has no spill metadata, so only the top-left cell of the range gets a value — and recalc.py reports total_errors: 0 on the truncated result. Use INDEX/MATCH for lookups, and sort, filter, and de-duplicate in Python before writing the cells.
• A formula LibreOffice could not parse is written back lowercased — a quick tell beside a #NAME?.

openpyxl gotchas

• Reading a model takes two loads. data_only=True yields cached values with the formulas gone; the default yields formula strings with no values. One pass cannot give you both.
• data_only=True is destructive if you save. That workbook has no formulas left, so saving replaces every one with a literal — permanently.
• data_only=True on a file openpyxl just wrote returns None everywhere — run recalc.py first. (A formula whose result is "" also reads back as None.)
• Merged cells: write the top-left anchor only. Every other cell in the range is a MergedCell whose .value is read-only.
• .xlsm loses its macros unless you pass keep_vba=True to load_workbook.
• A sheet name containing a space must be quoted in a cross-sheet reference: ='Assumptions Inputs'!$B$5. Unquoted, it evaluates to #VALUE!.

Financial models

Unless the user says otherwise, or the existing file already does something else.

Color: blue text (0,0,255) for hardcoded inputs and scenario levers · black for formulas · green (0,128,0) for links to another sheet · red (255,0,0) for links to another file · yellow fill (255,255,0) for key assumptions and cells the user should fill in.

Numbers: currency $#,##0, with the unit named in the header (Revenue ($mm)) · zeros render as -, including in percentages ($#,##0;($#,##0);-) · negatives in parentheses · percentages 0.0%, stored as fractions (0.15 renders 15.0%; storing 15 renders 1500.0%) · valuation multiples 0.0x · years as text ("2024", never 2,024).

Structure: every assumption in its own labeled cell, referenced by the formulas that use it (=B5*(1+$B$6), never =B5*1.05) · formulas consistent across every projection period, since a lone edited cell mid-row is the commonest silent error · guard denominators that can be zero.

Dependencies

openpyxl, pandas, markitdown (pip, preinstalled — install only if an import fails or the command is missing) · LibreOffice (soffice, auto-configured for sandboxed environments via scripts/office/soffice.py)

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This skill is created and maintained by Anthropic. Vendored here unmodified except for frontmatter metadata; see LICENSE.txt for terms.

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ncats-arax

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name: ncats-arax description: Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries. allowed-tools: Read Bash license: MIT compatibility: Requires Python 3.10+ and outbound HTTPS access to arax.transltr.io. The client uses only the Python standard library and needs no API key. Queries and caller metadata may be publicly visible; never submit sensitive or patient-specific content. metadata: version: "1.0" skill-author: neuroepithelial

NCATS ARAX

Use ARAX as a constrained knowledge-graph lookup service. Submit reviewed CURIEs and explicit Biolink types, preserve the exact TRAPI exchange, inspect query-edge bindings and provenance, and treat every returned path as a candidate for subsequent verification.

Read query-contract.md before constructing a query. Read output-schema.md when interpreting saved artifacts, warnings, provenance, or partial results.

Safety boundary

• Use only public, nonsensitive research questions. ARAX status facilities may expose query and caller metadata even when store=false is requested.
• Do not submit patient information, confidential research questions, unpublished compound programs, or proprietary target hypotheses.
• Do not present a returned path as a validated mechanism or clinical recommendation.
• Report a zero as "not returned under these constraints," never as evidence that no relationship exists.
• Describe position as unscored response order, never rank.
• Verify important candidates with literature and authoritative databases separately.

Workflow

1. Normalize free text separately, then review and report the proposed CURIE and category.
2. Choose a typed one-hop query or an exactly two-hop query with both endpoints pinned.
3. Use default RTX-KG2 lookup unless the user explicitly names two to five providers.
4. Acknowledge that the biomedical query is public and choose a new or empty output directory.
5. Run the client once. Do not silently change provider selection or expansion order after a failure or empty result.
6. Inspect summary.json for bounded bindings and provenance and response.json for the exact TRAPI payload.
7. Verify scientifically important paths outside ARAX.

Preflight

Check the production OpenAPI without making a biomedical query:

python skills/ncats-arax/scripts/arax_client.py preflight

The client verifies that the service identifies itself as ARAX, exposes /query, and reports a supported TRAPI version. A nonproduction endpoint or untested TRAPI series requires an explicit override; neither override changes the fixed query shapes or operations.

Normalize an entity

Normalization is review-only and never triggers a graph query:

python skills/ncats-arax/scripts/arax_client.py normalize "primary myelofibrosis" \
  --expected-category biolink:Disease \
  --max-synonyms 10 \
  --acknowledge-public-query \
  --output-dir outputs/normalize-myelofibrosis

Review the canonical identifier, name, category, and synonym preview before using a CURIE. Report all CURIEs and categories regardless of query outcome. A category warning or zero result is a reason to curate the identifier, not to chain automatically to /query.

One-hop lookup

Pin at least one endpoint and type both nodes:

python skills/ncats-arax/scripts/arax_client.py one-hop \
  --subject-id CHEBI:31690 \
  --subject-category biolink:SmallMolecule \
  --predicate biolink:affects \
  --object-id NCBIGene:25 \
  --object-category biolink:Gene \
  --qualifier biolink:object_aspect_qualifier=activity_or_abundance \
  --qualifier biolink:object_direction_qualifier=decreased \
  --acknowledge-public-query \
  --output-dir outputs/imatinib-abl1

Lookup mode is the default and fixes expansion to infores:rtx-kg2. It defaults to 20 results. Use --result-limit N to request 1-50 results; 50 is the hard cap in either mode.

Endpoint-pinned two-hop lookup

Use exactly one typed, unpinned intermediate node:

python skills/ncats-arax/scripts/arax_client.py two-hop \
  --subject-id CHEBI:66901 \
  --subject-category biolink:SmallMolecule \
  --predicate-1 biolink:affects \
  --intermediate-category biolink:Gene \
  --predicate-2 biolink:associated_with \
  --object-id MONDO:0009061 \
  --object-category biolink:Disease \
  --qualifier-1 biolink:object_aspect_qualifier=activity_or_abundance \
  --qualifier-1 biolink:object_direction_qualifier=increased \
  --expand-order right-first \
  --acknowledge-public-query \
  --output-dir outputs/ivacaftor-cystic-fibrosis

Right-first expansion is the default. If an empty result merits another attempt, run a new query explicitly with --expand-order left-first and keep the runs separate.

Selected-provider federation

Federation is explicit and accepts two to five named providers:

python skills/ncats-arax/scripts/arax_client.py one-hop \
  --subject-id CHEBI:31690 \
  --subject-category biolink:SmallMolecule \
  --predicate biolink:affects \
  --object-id NCBIGene:25 \
  --object-category biolink:Gene \
  --mode federated \
  --kp infores:rtx-kg2 \
  --kp infores:molepro \
  --acknowledge-public-query \
  --output-dir outputs/federated-imatinib-abl1

Federation defaults to the hard maximum of 50 results. Provider errors may coexist with useful results; such a run exits 7 after retaining its artifacts and is marked partial.

Inspect saved provenance

Rebuild a bounded summary without network access:

python skills/ncats-arax/scripts/arax_client.py summarize \
  --request outputs/ivacaftor-cystic-fibrosis/request.json \
  --response outputs/ivacaftor-cystic-fibrosis/response.json \
  --format text

The inspector accepts only the same constrained request shapes and fixed operations that the live commands generate. Use --format json for the normalized view on standard output.

Interpret results

• Follow each analysis's query-edge bindings; do not summarize every knowledge-graph edge.
• Preserve the physical edge subject, predicate, object, and qualifier values returned by ARAX. Returned predicates or qualifier aspects may be more specific than the query constraint.
• Inspect all source objects, including primary, aggregator, supporting-data, upstream-resource, and source-record URL fields.
• Treat publication_availability: not_returned as missing metadata, not evidence that no publications exist.
• Treat missing auxiliary-graph references and provider failures as explicit warnings.
• Consult the raw response whenever the bounded summary omits detail or the service response is partial, unfamiliar, or scientifically surprising.

Deliberate exclusions

The client has no raw-query, workflow, operation, overlay, ranking, inference, link-prediction, Pathfinder, ARS, batch, all-provider, three-hop, cache, daemon, SDK, MCP, or natural-language-to-TRAPI surface. Do not work around those limits with direct HTTP calls under this skill.

Official references

• ARAX documentation
• ARAX production OpenAPI
• ARAXi operation documentation
• Translator Reasoner API
• Biolink Model

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waypoint-bio

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name: waypoint-bio description: Use when working with Outpost Bio's open microbiome foundation models - the Waypoint checkpoints (Waypoint-6m, Waypoint-45m, Waypoint-170m), the Atlas pretraining corpus, the Compass eight-task benchmark, or the waypoint CLI from the waypoint-bio package. Covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking a checkpoint on Compass, pretraining a GPT-2 model on taxonomic abundance profiles, and converting MetaPhlAn, Kraken2, QIIME 2, or MGnify abundance tables into waypoint format. license: MIT compatibility: Requires Python 3.10+ with waypoint-bio (pulls torch, transformers, datasets, peft, scikit-learn). Needs network access and a Hugging Face token with access granted to the gated outpost-bio repos. A GPU is strongly recommended for pretraining and benchmarking. metadata: version: "1.0" skill-author: K-Dense Inc. upstream-version: "waypoint-bio 1.0.2 (PyPI); GitHub main 1.0.4" last-reviewed: "2026-08-17" openclaw: primaryEnv: HF_TOKEN envVars: - name: HF_TOKEN required: true description: Hugging Face read token with access to the gated outpost-bio/Waypoint-*, outpost-bio/Atlas, and outpost-bio/Compass repos.

Waypoint: Outpost Bio's Open Microbiome Foundation Models

Overview

Outpost Bio open-sourced three artefacts under Apache 2.0, described in Treloar et al., bioRxiv 2026.05.02.722381:

Artefact	What it is	Hugging Face
Waypoint	GPT-2-style causal LMs over taxonomic tokens, 6M–170M params	outpost-bio/Waypoint-6m, -45m, -170m
Atlas	539,308 microbiome samples scraped from MGnify (485,377 pretrain / 53,931 benchmark)	outpost-bio/Atlas
Compass	Eight downstream tasks over four studies	outpost-bio/Compass

The unifying idea: a microbiome sample is a sentence. Each taxon is one token, tokens are ordered by descending abundance z-score, and the model is trained with next-token prediction. A pretrained checkpoint then supplies sample-level embeddings or a fine-tuning backbone for prediction tasks.

All of it is driven by one CLI, waypoint, with five subcommands: prepare-dataset, embed, finetune, benchmark, pretrain.

When to use

• Embedding 16S/shotgun taxonomic profiles into fixed-size vectors for clustering, visualisation, or a downstream classifier.
• Fine-tuning a Waypoint checkpoint to predict a phenotype, treatment, or continuous readout from community composition.
• Scoring your own microbiome model against Compass so the number is comparable to the paper.
• Pretraining a taxonomic language model on Atlas or on your own corpus.
• Converting profiler output (MetaPhlAn, Kraken2/Bracken, QIIME

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