Skill UI

Generate comprehensive Chinese interview preparation cheat sheets for ML/LLM topics including mathematical formulas with derivations, from-scratch PyTorch implementations, comparison tables, and 25 high-frequency interview questions across L1/L2/L3 difficulty levels. Supports balanced or max effort modes with cross-model review.

This skill acts as a personal coach to help users become power users of Claude. It activates when users ask to learn or be coached, then runs passively to identify missed optimization opportunities like vague prompts or unused capabilities. It surfaces single high-impact tips per response, offers prompt ratings, and tracks progress without interrupting workflow.

This skill automatically enriches research wiki pages that are left as bare scaffolds after initial ingestion. It reads multiple external sources (like arXiv/alphaxiv) and fills in 10 structured `_TODO._` sections (e.g., Problem/Gap, Key Results, Open Questions) with detailed prose summaries. Use this skill to transform incomplete research scaffolds into a comprehensive, structured knowledge base.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

Privileged applier that lands user-approved meta-optimize patches into the skill corpus. Runs a fresh cross-model jury on each staged diff at landing time, enforces author/reviewer family separation, and refuses any KILL verdict.

This skill utilizes the 2slides API to generate professional, multi-format presentations. It supports creating slides from raw text, summarizing uploaded documents, matching a specific style from a reference image, and adding AI voice narration. Outputs include interactive slide URLs and downloadable PDFs. Ideal for any 'make slides' or 'create a deck' workflow.

This skill is designed to prevent AI coding assistants from exhibiting sycophantic agreement bias. When active, it forces a structured, critical assessment of user claims. The process involves extracting the core claim, assessing it independently with evidence (for/against), and concluding solely based on that assessment, ensuring objective and evidence-based responses.

This advanced AI skill converts raw study materials—such as syllabi, past exam papers, or personal notes—into a structured, prioritized, and difficulty-ranked High Score Roadmap. It covers all key question types (Theory, Numerical, MCQ, Coding, Lab) and optimizes study flow by ordering topics from Easy to Hard. Use it for targeted revision, gap analysis, and predicting high-yield exam questions.

Open Dynamic Workflows (ODW) is an advanced, open-source engine designed to manage complex coding tasks using multiple AI agents. It operates in three stages: planning the task into parallel subtasks, orchestrating various coding agents (like Codex or Antigravity) to work concurrently, and finally, performing an adversarial verification pass to challenge and validate the combined output before merging. It supports multiple model providers and is ideal for structured, high-reliability software development.

A comprehensive studio workflow for generating professional, multilingual dubbing for local audio and video assets. This skill manages the entire process, including media submission, complex localization parameters (target locale, accent, language), real-time status polling, and final artifact download. It handles critical considerations like copyright checks, speaker consent, and preview generation to ensure high-quality, contextually appropriate dubbing across diverse languages.

This meta-skill manages the end-to-end generation of AI-powered short dramas from a given topic. It first drafts a detailed, shot-by-shot shooting script, pausing for user review and adjustments. After confirmation, it generates consistent visual assets (universal character identity and scene compositions) and renders the final video clips. The output includes the fully rendered MP4, subtitles, title cards, and the complete script file.

This meta-skill is designed to orchestrate the creation of complex, multi-step workflows (meta-skills). It should only be used when a user explicitly requests combining, synthesizing, or proposing a new skill that coordinates multiple existing functions. The process includes intent clarification, trigger collision checking, validation, and generating a detailed proposal containing the workflow DAG.