Total 56,871 skills, AI & Machine Learning has 9458 skills
Showing 12 of 9458 skills
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 ...
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.
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.
Novel content polishing and optimization, suitable for user requests such as "Help me polish this novel", "Improve the writing style", "Optimize chapter rhythm", "Enhance this highlight", "Make dialogues more natural", "Make this passage more engaging", "Optimize novel writing style", "Adjust chapter rhythm", "Make dialogues more realistic", "Help me revise this content", "Polish novel", "Optimize highlights", "Improve writing style", "Make this passage more immersive", etc. It provides 3 levels of polishing, focusing on optimization of writing style and content, supporting special optimizations such as style adaptation, rhythm tightening, highlight enhancement, dialogue optimization, etc. **Polished results directly modify the chapters/ directory, and automatic backups are made to .sumeru/write/original/ before modification**. **Sub-Agents are used for parallel processing during batch polishing, with each Agent responsible for a maximum of 3 chapters**
Use when your agent or environment is broken — wrong answers, errors, timeouts, tool failures, or CLI issues. Reads traces and logs to diagnose root causes. Also checks prerequisites when the CLI itself isn't working. Triggers on: "agent not working", "wrong answer", "agent error", "tool call failing", "debug agent", "check logs", "read traces", "broken", "500 error", "424 error", "model access denied", "command not found", "stuck in DELETING", "maxVms exceeded", "cold start diagnosis", "cold start slow", "agentcore create error", "create failed", "exit code 7", "connection refused local dev". Not for deploy failures — use agents-deploy. Not for performance tuning without errors — use agents-optimize. Not for VPC configuration — use agents-build. Not for observability setup or missing logs — use agents-optimize.
Find implementable ML training recipes from papers, datasets, docs, and code. Use when the user wants to fine-tune, train, reproduce, or choose a practical ML method, dataset, hyperparameter setup, or benchmark recipe.
vLLM Ascend plugin for LLM inference serving on Huawei Ascend NPU. Use for offline batch inference, API server deployment, quantization inference (with msmodelslim quantized models), tensor/pipeline parallelism for distributed serving, and OpenAI-compatible API endpoints. Supports Qwen, DeepSeek, GLM, LLaMA models with Ascend-optimized kernels.
Expert prompt engineering for Seedance 2.0. Use when the user wants to generate a video with multimodal assets (images, videos, audio) and needs the best possible prompt.
Generate AI images, videos, music, and audio from the terminal via muapi.ai — supports 100+ models including Flux, Midjourney v7, Kling 3.0, Veo3, and Suno V5