About
This skill helps developers create Effect schemas with guaranteed type parity to their domain types. It provides a structured workflow for defining schemas with `satisfies Schema.Schema<Type>` and type equality assertions using `tsafe`. Use it when creating or updating Effect Schema definitions to ensure runtime validation matches compile-time types.
Quick Install
Claude Code
Recommendednpx skills add morgs32/skills -a claude-code/plugin add https://github.com/morgs32/skillsgit clone https://github.com/morgs32/skills.git ~/.claude/skills/effect-schemaCopy and paste this command in Claude Code to install this skill
GitHub Repository
Frequently asked questions
What is the effect-schema skill?
effect-schema is a Claude Skill by morgs32. Skills package instructions and resources that Claude loads on demand, so Claude can perform effect-schema-related tasks without extra prompting.
How do I install effect-schema?
Use the install commands on this page: add effect-schema to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does effect-schema belong to?
effect-schema is in the Other category, tagged ai.
Is effect-schema free to use?
Yes. effect-schema is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
Related Skills
LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
This Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.
This Claude Skill analyzes sports betting markets including spreads, over/unders, and prop bets by examining historical trends and situational statistics to identify value bets. It provides structured markdown output with actionable recommendations for educational purposes. Developers should use this for sports betting analysis tools while noting it's designed for entertainment/education only.
This skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.