ai-training-data-generation
Generate high-quality training datasets from documents, text corpora, and structured content. Use when creating AI training data from dictionaries, documents, or when generating examples for machine learning mo...
Discover Documentation skills, compare source repositories and read the instructions for each workflow.
Generate high-quality training datasets from documents, text corpora, and structured content. Use when creating AI training data from dictionaries, documents, or when generating examples for machine learning mo...
Process large documents (200+ pages) with structure preservation, intelligent parsing, and memory-efficient handling. Use when working with complex formatted documents, multi-level hierarchies, or when you need...
Comprehensive code documentation standards and guidelines for maintaining up-to-date documentation across Python, HTML, CSS, and JavaScript codebases. Use when creating or modifying code to ensure proper docume...
Automatically manage Python virtual environments (.venv) in terminal commands. Always activate .venv before running Python/pip commands. Use when executing Python scripts, installing packages, or running develo...
Process scanned documents and images containing Chuukese text using OCR with specialized post-processing for accent characters and traditional formatting. Use when working with scanned books, documents, or imag...
Assess and validate translation quality between Chuukese and English with cultural context awareness, linguistic accuracy checking, and automated quality metrics. Use when evaluating translation outputs, buildi...
Systematic bug investigation and root cause analysis
Design RESTful APIs following best practices
Review pull requests against team standards and best practices
Generate commit messages following conventional commits with scope detection
Safe code refactoring with step-by-step approach
Generate comprehensive test cases for code
Review code quality, patterns, and best practices
Generate and update technical documentation
Security review checklist for code and infrastructure
Break down features into implementable tasks
Maintains consistency when creating, modifying, reading, reviewing, or working with specifications. Use when user mentions SPEC.md, requirements, or any specification documents to ensure consistent handling. Pr...
Provides guidelines for verifying implementation requirements before creating, editing, or deleting files, and taking appropriate actions when requirements are not met.
When naming Git branches, provide guidelines on the recommended branch naming conventions for the project.
When making a Git commit, provide the subject of the commit and the rules to follow before committing.
Use the Spice CLI to manage Spicepods and interact with the runtime. Use when asked to "run Spice", "query data", "start the runtime", "use spice commands", or "check spice status".
Create and configure Spicepod manifests (spicepod.yaml). Use when asked to "create a spicepod", "configure spicepod.yaml", "set up a Spice app", or "initialize Spice project".
Install Spice.ai OSS CLI and runtime. Use when asked to "install Spice", "set up Spice", or "get started with Spice".
Connect Spice to data sources like PostgreSQL, MySQL, S3, Databricks, Snowflake, and more. Use when asked to "add a dataset", "connect to a database", "load data from S3", or "configure a data source".
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.