Skill UI

A powerful tool for conversational time tracking. Users simply report time spent in plain language (e.g., "read papers 2h, gym 1h"), and the skill parses this into structured rows (Activity, Minutes, Date) within your Notion database. It ensures data integrity by flagging any uncertain details as 'To-confirm' rows and batch-asking questions, preventing data fabrication. Use it for logging daily activities or performing time reviews.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

Train and fine-tune transformer language models with Hugging Face TRL via CLI. Covers SFT, DPO, GRPO, RLOO, KTO and reward model training, plus LoRA adapters, YAML configs and distributed multi-GPU runs.

Score a UI file's design quality 0-100 against StyleSeed's design language, with per-category breakdown, worst offenders, and prioritized fix list. A quantified version of /ss-review for tracking UI quality.

Analyzes a project's StyleSeed engine files, compares them with a pinned upstream revision, and safely updates skills, .cursorrules, DESIGN-LANGUAGE.md, and Golden Rules with explicit user approval. Use for maintaining the design system engine without overwriting custom UI.

This skill automates the creation of comprehensive unit tests across multiple programming languages like Python, JavaScript, and Java. It analyzes source code to identify functions and classes, then generates maintainable test suites with strong coverage, edge case handling, and proper mocking. Ideal for developers seeking consistent test structures and improved code quality without manual writing.

unslop is a powerful command-line interface (CLI) tool designed for post-processing text. It programmatically strips out common and identifiable patterns associated with AI-generated writing. Use it as a final, deterministic pass before publishing documentation, blog posts, or any content derived from large language models (LLMs), ensuring a more natural and human-like tone.

This skill humanizes natural-language memory files like CLAUDE.md, AGENTS.md, and docs by removing AI-isms and adding burstiness. It offers deterministic and LLM modes, preserves code blocks, URLs, paths, and headings, and creates backups. Ideal for cleaning up AI-generated content in configuration files and documentation.

Generate user interface microcopy such as button labels, error messages, empty states, and toast notifications. This skill ensures a casual-but-polite tone, active voice, and plain language to enhance user experience without defining the overall brand voice.

This skill generates appropriate user feedback states for components and pages, including loading skeletons, empty states, error messages, and success toasts. It ensures consistent UI presentation by implementing specific design patterns for data-dependent areas, handling partial or full failures, and providing retry mechanisms. It follows design language references for animations and accessibility, suitable for React-based frontend development workflows.

Guides GitHub Copilot through V language development: installing the toolchain, structuring v.mod projects, building, formatting, vetting, testing, and writing idiomatic V with Option/Result error handling and immutability rules.

Vibers provides a human-in-the-loop code review workflow for AI-generated GitHub projects. Users push code, and the service reviews it against a specification, checks for security issues, and submits a pull request with suggested fixes. It supports various languages and requires adding a collaborator and configuring a GitHub Action to trigger the review process.