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Found 345 Skills
Restructures existing code to improve readability, maintainability, and performance without changing external behavior. USE WHEN: Restructuring code without changing behavior, extracting methods/classes, removing duplication, applying design patterns, improving code organization, reducing technical debt. DO NOT USE: For bug fixes (use /debugging), for adding tests (use /testing), for new features (implement directly). TRIGGERS: refactor, restructure, rewrite, clean up, simplify, extract, inline, rename, move, split, merge, decompose, modularize, decouple, technical debt, code smell, DRY, SOLID, improve code, modernize, reorganize.
BibiGPT CLI for summarizing videos, audio, and podcasts directly in the terminal. Use when the user wants to summarize a URL (YouTube, Bilibili, podcast, etc.) or check their BibiGPT authentication status. Requires the BibiGPT desktop app installed with an active login session, or a BIBI_API_TOKEN environment variable set.
Fetch and analyze content from one or more URLs using AI (Gemini 2.5 Flash). Use when you have specific URLs and need to extract or summarize their content. Pairs well with `nansen web search` results.
Automatically generate personal weekly reports based on Git commit records and code changes. Use this skill when users request to generate weekly reports, work summaries, or summarize weekly work content. Supports custom time ranges, committer filtering, output formats, and detail levels.
Conduct Exploratory Data Analysis (EDA) using descriptive statistics, visualizations, and data quality checks. Use this skill when the user has a dataset and needs to understand its structure, find patterns, detect anomalies, or prepare data for further analysis — even if they say 'what does this data look like', 'find interesting patterns', 'clean this data', or 'summarize this dataset'.
Finds the most informative session recording linked to an error tracking issue. Use when a user has an error tracking issue ID and wants to watch a replay showing what the user was doing when the error occurred. Ranks linked sessions by recency, activity score, and journey completeness, then summarizes the pre-error context. Replaces blind session picking from potentially hundreds of linked recordings.
This skill should be used when the user asks to "search YouTube", "find videos about", "get a transcript", "download subtitles", "extract audio from YouTube", "scan a channel", "research a topic on YouTube", "get video metadata", "what videos exist about", "download YouTube audio", "YouTube research", "summarize this video", "what is this video about", "pull captions from", "grab the audio from", or provides a YouTube/Vimeo/video URL and wants to extract information from it. Also triggers on "batch download transcripts", "analyze a channel", or any multi-video research workflow.
Create structured documents from conversations, summaries, or content in open formats (markdown, PDF, text). Use when the user requests document creation, report generation, content export, conversation summaries, or structured documentation. Triggers include "create a document", "make a report", "summarize this conversation", "export to PDF/markdown", or any request to formalize content into a document. Works independently or integrates with design-assistant skill for polished visual output.
Automatically collect and summarize daily AI industry news, trends, and hot topics from platforms like GitHub (trending repos), X/Twitter (AI influencers/hashtags), and AI news aggregators. Use this skill when the user asks for "today's AI news", "AI industry updates", "what's trending in AI", or wants a daily digest of AI developments.
Systematic 4-phase codebase exploration: Detect, Explore, Map, Summarize. Use when starting work on an unfamiliar codebase, onboarding to a new project, reviewing a repository for the first time, or building context before debugging or code review. Use for "explore codebase", "what does this project do", "understand architecture", or "onboard me". Do NOT use for modifying files, running applications, performance optimization, or deep domain analysis.
Representative MoE training playbooks by hardware platform and model family. Summarizes rounded throughput bands, parallelism patterns, and common tuning stacks.
Representative MoE training playbooks by hardware platform and model family. Summarizes rounded throughput bands, parallelism patterns, and common tuning stacks.