Total 50,402 skills, AI & Machine Learning has 8470 skills
Showing 12 of 8470 skills
dontbesilent Interactive Learning. Break down a topic into a sequential series of learning articles, adjusting the depth, perspective, and pace of the next article based on the user's feedback from the previous one. Triggers: /dbs-learning, /dbs-learn, /interactive-learning, "teach me a topic", "continue the next lesson", "generate the next lesson based on my feedback" Interactive learning workflow. Builds an adaptive sequence of learning articles based on user feedback. Trigger: /dbs-learning, /dbs-learn, "teach me a topic", "continue the next lesson"
Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.
Use this skill when the user's Copilot Studio agent evaluations have come back and they need to interpret scores, diagnose root causes of underperforming test cases, find remediation steps, or analyze patterns to improve their agent. Always use this skill when the user mentions: "eval failed", "why did this fail", "triage", "diagnose failure", "low pass rate", "fix evaluation results", "not passing", "failing test cases", "evaluation results", "improve my eval scores", or any situation where eval scores need interpretation and action.
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AI Configs implementation in five stages: extract prompts, wrap in the AI SDK, add tools, add tracking, add evals/judges. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini) to a managed AI Config, or stage a full hardcoded-to-LaunchDarkly migration.
AI-powered search that aggregates and summarizes results from multiple sources including web, X/Twitter, Reddit, Hacker News, YouTube, ArXiv, and Wikipedia. Use this when you need a synthesized answer or curated links from across the internet and social platforms.
Prevents premature execution on ambiguous requests. Analyzes request clarity using 5W1H decomposition, surfaces hidden assumptions, and generates structured clarifying questions before work begins. Use at the start of any non-trivial task, or when a request could be interpreted multiple ways. Triggers on "뭘 원하는건지", "요구사항 정리", "clarify", "what exactly", "scope", "requirements", "정확히 뭘", "before we start".
Skill Map Viewer. Scans all installed skills and renders a visual overview — you can check the name, version, description, and category at a glance. This tool is triggered when the user says 'skills', '技能', '技能地图', 'skill map', '我有哪些技能', '看看技能', '列出技能', 'list skills'. It also activates when the user asks about available or installed skills.
Paper Workflow: Read papers and create reading cards in one go. Accepts one or more arXiv links, paper URLs, PDFs, or paper titles. For each paper, it runs ljg-paper (generates org-format analysis) followed by ljg-card -l (generates long-form reading card PNG). Trigger this workflow when the user says '论文流', 'paper flow', '读论文并做卡片', '论文卡片', or provides multiple papers and requires both analysis and reading cards.
Agent skill for planner - invoke with $agent-planner
Route audio, video, transcript, subtitle, and edit-prep requests into the right media-understanding workflow before execution. Use this when the user wants transcription, subtitle generation, beat mapping, B-roll planning, or edit-ready outputs and the first question is which skill and model chain should run.
Information Question Generator. Given an article, paper, or book, extract its core viewpoints into Q-A pairs — Questions get straight to the point, no textbook-style phrasing; Answers are concise and clear, with formalized conclusions and complete logical chains. As readers follow the Q chain, each Answer drives home a key point, reproducing the author's entire reasoning process. Activate when the user says '问答', 'Q&A', 'QA', '提问', '抽取问题', '/ljg-qa', or shares an article, paper, or book and requests Q-A extraction. This tool triggers when the user wants ideas extracted not as a summary but as a sequence of incisive questions paired with answers. NOT FOR FAQ generation, glossary creation, or comprehension quizzes — this is intellectual scaffolding, not a study aid.
Create and manage agent graphs — directed graphs of AI Configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.