Total 58,116 skills, AI & Machine Learning has 9659 skills
Showing 12 of 9659 skills
AI image generation and editing using Google Gemini models (Nano Banana). Use when the user asks to generate an image, create an image, edit an image, or references "nano banana", "nanobanana", or "gemini image". Supports text-to-image, image editing, multi-image references, and 1K/2K/4K resolution.
Enable and configure Moltbot/Clawdbot memory search for persistent context. Use when setting up memory, fixing "goldfish brain," or helping users configure memorySearch in their config. Covers MEMORY.md, daily logs, and vector search setup.
Dispatch background AI worker agents to execute tasks via checklist-based plans.
Identify and eliminate AI-generated traces in Chinese text to make articles more natural and human-like. AI writing feature detection based on Chinese context, including clichés, over-modification, mechanical structures, etc. Referenced the Wikipedia "Signs of AI writing" guide and localized it for Chinese context.
Build correct, consistent Agent Skills (create/update/delete/add content) using validated templates, safe defaults, and cross-skill consistency checks. Works across common agent CLIs that load skills from Markdown folders. Triggers: "new skill", "create skill", "build skill", "update skill", "delete skill", "add to skill", "skill template", "skill validation", "skill builder".
Use when challenging ideas, plans, decisions, or proposals using structured critical reasoning. Invoke to play devil's advocate, run a pre-mortem, red team, or audit evidence and assumptions.
Guide for creating MCP servers that enhance LLM reasoning through structured processes, persistence, and workflow guidance. Use when building MCP servers for structured thinking, journaling, memory systems, or other cognitive enhancement patterns.
Use this skill when someone wants to learn GitHub Copilot CLI from scratch. Offers interactive step-by-step tutorials with separate Developer and Non-Developer tracks, plus on-demand Q&A. Just say "start tutorial" or ask a question! Note: This skill targets GitHub Copilot CLI specifically and uses CLI-specific tools (ask_user, sql, fetch_copilot_cli_documentation).
balancing accuracy with token efficiency.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.