Total 43,917 skills, AI & Machine Learning has 7006 skills
Showing 12 of 7006 skills
Main orchestrator for README-first AI repo reproduction. Use when the user wants an end-to-end, minimal-trustworthy reproduction flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, or broad research assistance outside repository-grounded reproduction.
Complete reference for the Galileo AI platform Python SDK for evaluating, observing, and protecting GenAI applications. Use when building Python applications that need LLM evaluation, production observability, tracing, or runtime guardrails with Galileo.
The foundational knowledge distillation pattern for building and maintaining an AI-powered Obsidian wiki. Based on Andrej Karpathy's LLM Wiki architecture. Use this skill whenever the user wants to understand the wiki pattern, set up a new knowledge base, or needs guidance on the three-layer architecture (raw sources → wiki → schema). Also use when discussing knowledge management strategy, wiki structure decisions, or how to organize distilled knowledge. This is the "theory" skill — other skills handle specific operations (ingesting, querying, linting).
Targeted Chatroom: Recommend experts based on topics or accept user-specified experts to simulate multi-role conversations. Trigger methods: /dbs-chatroom, /targeted-chatroom, "Targeted Chatroom"
Reading companion agent. Accompanies user through any text (books, articles, essays, papers, news) with translation, structural annotation, deep questioning, and cross-domain insights. Detects language, translates English to Chinese (faithfulness-expressiveness-elegance), guides reader to understand the author and encounter real questions. Use when user says '伴读', '陪我读', '读这篇', 'read with me', 'companion read', or shares a text/URL wanting guided reading.
Scaffolds a personal LLM Wiki from scratch — the Karpathy pattern of incrementally building a persistent, interlinked markdown knowledge base maintained by LLMs. Generates directory structure, schema file, index, log, and workflow conventions. Use when user says "create wiki", "new wiki", "bootstrap wiki", "llm wiki", "knowledge base", "start a wiki", "build a wiki", or wants to set up a structured markdown knowledge base for any domain.
Reading coach: guides users through books systematically with knowledge compilation, mastery testing, spaced repetition, and knowledge querying. Use when user says 'read this book with me', 'book study', 'start studying X', 'reading plan', 'ingest this chapter', 'review what I read', 'quiz me on the book', 'what did the book say about X', or invokes /book-study. Supports sub-commands: ingest, query, review, compare, status. Triggers: book, study, read, chapter, ingest, review, quiz, reading plan, book notes.
Agent Workspace Migration. Organize any project into a long-term maintainable Agent workspace with consistent support for both Claude Code and Codex: audit rule files, identify source-of-truth skills, standardize naming conventions, and generate bridges. Triggers: /dbs-agent-migration, /agent-migration, "migrate to Codex", "migrate to Claude Code", "unify AGENTS.md", "organize skill bridges", "my Agent workspace is messy", "help me unify Claude and Codex" Agent workspace migration. Turn any project into a maintainable Claude Code / Codex dual-host workspace by auditing rule files, establishing source-of-truth skills, normalizing names, and generating bridges. Trigger: /dbs-agent-migration, /agent-migration, "migrate to Codex", "migrate to Claude Code", "fix AGENTS.md", "organize skill bridges"
Use this skill when building NLP pipelines, implementing text classification, semantic search, embeddings, or summarization. Triggers on text preprocessing, tokenization, embeddings, vector search, named entity recognition, sentiment analysis, text classification, summarization, and any task requiring natural language processing.
Expert skill for generating GitHub Copilot skills from ING-internal documentation repositories. Use this skill when asked to create a skill from any ING documentation-as-code repo, generate a knowledge base skill for an ING framework, convert ING tool documentation into a Copilot skill, or turn any docs/ folder into an expert skill file. Also trigger when the user mentions "skill from docs", "generate skill", "create skill from repo", or references ING-internal frameworks like Baker, Merak, Kingsroad, or similar. Includes evaluation framework, grading agents, and benchmark tools for testing generated skills.
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill instead.
Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.