Total 56,150 skills, AI & Machine Learning has 9353 skills
Showing 12 of 9353 skills
기업교육 컨설턴트 리더를 위한 Claude Code 학습 튜터. "/cc-tutor", "/learn", "학습", "튜터" 요청에 사용.
AI image generation Creative Director powered by Google Gemini Nano Banana models. Use this skill for ANY request involving image creation, editing, visual asset production, or creative direction. Triggers on: generate an image, create a photo, edit this picture, design a logo, make a banner, visual for my anything, and all /banana commands. Handles text-to-image, image editing, multi-turn creative sessions, batch workflows, and brand presets.
Use when implementing speech-to-text, audio transcription, real-time streaming STT, audio intelligence features, or voice AI using AssemblyAI APIs or SDKs. Use when user mentions AssemblyAI, voice agents, transcription, speaker diarization, PII redaction of audio, LLM Gateway for audio understanding, or applying LLMs to transcripts. Also use when building voice agents with LiveKit or Pipecat that need speech-to-text, or when the user is working with any audio/video processing pipeline that could benefit from transcription, even if they don't mention AssemblyAI by name.
Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
Orchestrates end-to-end software development using the addyosmani/agent-skills framework. Guides the user through define → plan → build → verify → review → ship phases, spawns subagents for each step, tracks state persistently, and never loses focus on workflow completion. Use when the user says "let's build X", "help me implement X", "walk me through X", or wants structured multi-phase dev guidance. Also triggers when a task is clearly non-trivial and would benefit from phased execution.
Ultra-compressed communication mode. Talk like a caveman to reduce token usage by about 75%. Full technical accuracy is maintained. Intensity levels: 3 tiers - Polite, Normal (default), Extreme. Activate by saying "Caveman Mode", "Shorten", "Be Concise", "Save Tokens", or using /genshijin.
This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of belief-based agent reasoning.
Apply mechanism design (reverse game theory) to engineer incentive-compatible rules for allocation problems. Use this skill when the user needs to design auctions, voting systems, or matching markets, or when evaluating whether a proposed mechanism satisfies incentive compatibility and individual rationality constraints.
Evaluate the performance of Triton operators on Ascend NPU. It is used when users need to analyze operator performance bottlenecks, collect and compare operator performance using msprof/msprof op, diagnose Memory-Bound/Compute-Bound bottlenecks, measure hardware utilization metrics, and generate performance evaluation reports.
AscendC Operator End-to-End Development Orchestrator. Used when users need to develop new operators, implement custom operators, or complete the full process from requirements to testing. Keywords: operator development, end-to-end, full process, workflow orchestration, new operator creation.
Generate 3D CGI and rendered video prompts for Seedance 2.0 (Higgsfield). Use this when users want 3D rendering, CGI, Pixar-style, Unreal Engine, photorealistic 3D, computer-generated, or digitally rendered video content. Trigger words: 3D animation, CGI, rendering, Blender, Unreal Engine, octane render, ray tracing, volume, subsurface scattering, physically based rendering, or any 3D/CG video request. Use this even if the user only says "make it look 3D" or describes a rendering aesthetic.
Independence-validated parallel fleet that runs each worker (claude -p or codex exec) in its own git worktree. Use when tasks touch non-overlapping files and you need merge-safe isolation (each worker on its own branch). For DAG-ordered one-shot workers with budgets, use dag-fleet. For headless iteration with a reviewer loop, use iterative-fleet.