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Found 1,199 Skills
Use when creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cycle to prompt engineering using subagents for isolated testing
Build Model Context Protocol servers and implementations. Creates protocol-compliant tools and integrations for AI-powered applications.
Optimize a prompt through a critique-compress pipeline with semantic equivalence verification at each stage. Applies think-critically to improve the prompt, then compress-prompt to reduce it, validating that behavior is preserved after each transformation.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for...
Claude-Codex-Gemini tri-model orchestration via ask-codex + ask-gemini, then Claude synthesizes results
Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative GPT-5.4 review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.
Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files
Analyse agent execution to find wasted tool calls, wrong turns, and blind alleys. Optimise agents to reach their goal in the fewest turns, tokens, and least time. Recommend harness/model changes — never apply without user approval.
Token-saving terse mode — no filler, no narration, just results
昇腾（Ascend）推理生态开源代码仓库智能问答专家旨在为 vLLM、vLLM-Ascend、MindIE-LLM、MindIE-SD、MindIE-Motor、MindIE-Turbo 以及 msModelSlim (MindStudio-ModelSlim) 等仓库提供专家级且易于理解的解释。在处理昇腾（Ascend）推理生态相关项目的用户询问时，务必触发此技能（Skill），可解答使用方法、部署流程、支持模型、支持特性、系统架构、配置管理、调试、测试、故障排查、性能优化、定制开发、源码解析以及其他技术问题。支持中英文双语回复，并可借助 deepwiki MCP 工具检索仓库知识库，生成具备上下文感知且基于证据的回答。Ascend inference ecosystem open-source code repository intelligent question-and-answer (Q&A) expert. Provide expert-level yet comprehensible explanations for repositories such as vLLM, vLLM-Ascend, MindIE-LLM, MindIE-SD, MindIE-Motor, MindIE-Turbo, and msModelSlim (MindStudio-ModelSlim). Use this skill when addressing user inquiries related to these Ascend inference ecosystem projects, including topics such as usage, deployment process, supported models, supported features, system architecture, configuration management, debugging, testing, troubleshooting, performance optimization, custom development, source code analysis, and any other technical issues about these projects. Support responses in both Chinese and English. Use deepwiki MCP tools to query repository knowledge bases and generate context-aware, evidence-based responses.
Spawn specialized sub-agents with context handoff for complex multi-phase tasks. Enables expertise delegation within a session with automatic context merging and depth limiting to prevent infinite loops.
LangGraph-based agent framework for consistent tool calling with automatic tool loops. Use when you need reliable multi-step task execution with OpenAI-compatible providers (Z.AI/GLM-5, OpenRouter, Groq, DeepSeek, Ollama).