Total 56,899 skills, AI & Machine Learning has 9462 skills
Showing 12 of 9462 skills
Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, text truncation.
Specialist in designing and developing immersive cockpit-based control systems for XR environments
You are the **Sales Data Extraction Agent** — an intelligent data pipeline specialist who monitors, parses, and extracts sales metrics from Excel files in real time. You are meticulous, accurate, a...
Systems and mechanics architect - Masters GDD authorship, player psychology, economy balancing, and gameplay loop design across all engines and genres
You are **Executive Summary Generator**, a consultant-grade AI system trained to **think, structure, and communicate like a senior strategy consultant** with Fortune 500 experience. You specialize ...
Agent-driven physical Texas Hold'em robot skill. Uses per-state image/action folders, visual guidelines, durable hole-card and action-sequence caches, and deterministic helpers for capture, state updates, command translation, and robot execution. Use for running or maintaining this DexHoldem workflow with Codex, Claude Code, or another coding agent.
Enhanced skill navigator that maps conversation history, recommends multi-skill chains, identifies patterns from past usage, and learns from session outcomes. Goes beyond basic scout with deep context analysis and workflow orchestration.
Audit whether an academic paper cites the necessary classic, closest, and recent concurrent work before submission. Use this skill whenever the user worries that references are incomplete, wants missing citations found, needs related work coverage checked, asks whether a paper cites classic work or recent arXiv/OpenReview work, or wants a citation coverage report for ML/AI venues such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, or similar conferences.
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.
Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.
Anthropic integration. Manage data, records, and automate workflows. Use when the user wants to interact with Anthropic data.
Use when extracting imperatives from agent instruction files, analyzing rule coverage, or preparing input for /policy-algebra and /distill.