Total 50,524 skills, AI & Machine Learning has 8481 skills
Showing 12 of 8481 skills
Use when writing, refining, or structuring prompts for AI-powered app features — system prompts, user prompt templates, few-shot examples, chain-of-thought, prompt versioning, and defensive prompting
Translate and dub videos from one language to another, replacing the original audio with TTS while keeping the video intact.
This skill should be used when user wants to access, capture, or reference Claude Code session history. Trigger when user says "capture session", "save session history", or references past/current conversation as a source - whether for saving, extracting, summarizing, or reviewing. This includes any mention of "what we discussed", "today's work", "session history", or when user treats the conversation itself as source material (e.g., "from our conversation").
This skill should be used when the user asks to "콘텐츠 정리", "아티클 요약", "PDF 학습", "영상 정리", "트윗 정리", "digest", "summarize", "정리해줘", or provides a YouTube URL, X/Twitter URL (x.com, twitter.com), webpage URL, or PDF file for analysis. Supports YouTube (transcript), X/Twitter (via fetch-tweet skill), webpage (full content via browser), and PDF (text + image per page). Generates Quiz-First learning with 9 questions across 3 difficulty levels.
Explore and investigate ideas before committing to a change. Trigger: When the orchestrator launches you to think through a feature, investigate the codebase, or clarify requirements.
LLM inference via paid API: OpenAI-compatible chat completions proxied through x402 providers. Supports Kimi K2.5, MiniMax M2.5. Uses x_payment tool for automatic USDC micropayments ($0.001-$0.003/call). Use when: (1) generating text with a specific model, (2) running chat completions through a pay-per-request LLM endpoint, (3) comparing outputs across models.
Heartbeat-driven 7-day BotLearn tutorial reminders — fetches quickstart pages daily, tracks progress, presents tips in the user's language, auto-stops after Day 7.
Google Gemini API for Pro/Flash/Ultra models with 1M token context.
Capture corrections, insights, and patterns as reusable project knowledge. Routes learnings to the right instruction file. Applies kaizen: small improvements, error-proofing, standards work. Auto-invoked when a correction pattern is detected 3+ times. Also use manually when Claude makes a repeated mistake, discovers a non-obvious gotcha, or when you want to persist a workflow preference.
Apply cognitive bias detection whenever the user (or Claude itself) is making an evaluation, recommendation, or decision that could be silently distorted by systematic thinking errors. Triggers on phrases like "I'm pretty sure", "obviously", "everyone agrees", "we already invested so much", "this has always worked", "just one more try", "I knew it", "the data confirms what we thought", "we can't go back now", or when analysis feels suspiciously aligned with what someone wanted to hear. Also trigger proactively when evaluating high-stakes decisions, plans with significant sunk costs, or conclusions that conveniently support the evaluator's existing position. The goal is not to paralyze — it's to flag where reasoning may be compromised so it can be corrected.
Manage MCP servers - discover, analyze, execute tools/prompts/resources. Use for MCP integrations, capability discovery, tool filtering, programmatic execution, or encountering context bloat, server configuration, tool execution errors.
Use when an agent is asked to define, review, or write acceptance criteria for a request or plan. Derives acceptance criteria from the current request context, confirms them with the user, and writes them into the plan file or a standalone acceptance_criteria.md file.