code-quality
642
character-encoding-validation
Character Encoding Validation
kreuzberg-dev
testing-security
query active
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code-quality
642
style-attribute-sanitization
Style Attribute Sanitization
kreuzberg-dev
testing-security
ide-plugins
642
semantic-html5-element-conversion
Semantic Html5 Element Conversion
kreuzberg-dev
tools
machine-learning
641
add-model
Add a new AI model to the Pipelex inference system. Guides through all required steps: backend TOML configuration (OpenAI, Azure, Anthropic, Google, etc.), kit sync, test profile collections, and fixture regeneration. Use when the user says "add a model", "add GPT-X", "add Claude X", "new model", "register a model", "add Gemini X", "support model X", "add model to backend", or any variation of introducing a new AI model to the inference configuration. Also use when the user mentions a model name that doesn't exist in the backend configs yet and wants to add it.
Pipelex
data-ai
machine-learning
641
test-model
Test an AI model on a specific backend using the Pipelex inference test infrastructure. Handles test profile creation, fixture regeneration, and running the right test class for the model type (LLM, image gen, extract, search). Use when the user says "test model X", "test gpt-5.4 on openai", "test model on gateway", "run inference test for model", "try model X on backend Y", "verify model X works", or any variation of running inference tests against a specific model on a specific backend. Also use when the user mentions testing a model after adding it, or wants to verify a model works end-to-end with real API calls.
Pipelex
data-ai
package-distribution
641
release
Automates the Pipelex release workflow: bumps the version in pyproject.toml, finalizes the CHANGELOG.md Unreleased section, runs quality checks, creates a release/vX.Y.Z branch, commits, pushes, and opens a PR to main. Use when user says "release", "cut a release", "bump version", "prepare a release", "make a release", "ship it", "create release branch", or any variation of shipping a new version of pipelex. The user can optionally provide changelog content inline when invoking the skill (e.g. "/release Added new extract backend"), which will be used as the changelog entry for this version.
Pipelex
development
documents
639
iyeque-pdf-reader
PDF reader skill using PyMuPDF (fitz) for text extraction and metadata retrieval. Supports encrypted PDFs and handles large documents efficiently.
wentorai
content-media
documents
639
md2pdf-export
Convert Markdown files to PDF, PNG, or JPEG using headless Chrome (Puppeteer), following the same methodology as VS Code's Markdown Preview Enhanced extension. Use this skill whenever the user asks to: export/convert/render markdown to PDF or image, generate a printable document from .md files, create publication-ready PDFs from markdown, set up a markdown-to-PDF pipeline, or install/configure the Node.js + Puppeteer + Chrome environment needed for headless rendering. Also trigger when the user mentions "md2pdf", "markdown export", "puppeteer PDF", "headless chrome PDF", "crossnote", "mume", or wants WYSIWYG-quality PDF output from markdown source files. This skill covers both environment setup AND the actual conversion process.
wentorai
content-media
documents
639
ppt-master
Generate editable PPTX from documents using local ppt-master pipeline.
wentorai
content-media
documents
639
survey-sop
Standard operating procedure for research survey and project execution. Covers deep reading methodology, information synthesis, paper evaluation, citation integrity, cross-module coordination (Library↔Tasks↔Workspace↔Monitor), and research project lifecycle (Phase 2-4).
wentorai
content-media
documents
639
writing-sop
Four-phase iterative protocol for academic writing and document production. Covers outline → draft → self-review → polish with Stop Points, material supplementation loops, version comparison, and mechanical self-check. Includes IMRaD, LaTeX, docx/markdown, citation formatting, and md2pdf.
wentorai
content-media
data-analysis
639
coding-sop
Standard operating procedure for research experiments, data analysis, and visualization. Covers Python/R script execution, statistical analysis, data wrangling (pandas/tidyverse), publication-quality figures (matplotlib/plotly/ggplot2), and experiment reproducibility.
wentorai
data-ai
machine-learning
639
plotting-sop
Standard operating procedure for academic figure generation. Four rendering engines: Python (data viz), Mermaid (flowcharts), AI Image via NanoBanana/OpenRouter (complex diagrams), SVG (vector). Includes engine selection decision tree, ReAct self-correction, NanoBanana configuration, environment detection, and academic style rules.
wentorai
data-ai
cms-platforms
639
kai-slide-creator
Generate zero-dependency HTML presentations that run entirely in the browser. Zero npm, no build tools. Includes presentation mode and edit mode.
wentorai
development