Total 50,313 skills, AI & Machine Learning has 8452 skills
Showing 12 of 8452 skills
AI design workflow with DESIGN.md, anti-patterns, and optional Stitch MCP
Watch a tutorial, demo, or walkthrough video and generate a Claude Code skill from it. Extracts the workflow, commands, tools, and patterns demonstrated and produces a SKILL.md with implementation. Supports Loom, YouTube, and local files.
Selects a base model and fine-tuning technique (SFT, DPO, or RLVR) for the user's use case by querying SageMaker Hub. Use when the user asks which model or technique to use, wants to start fine-tuning, or mentions a model name or family (e.g., "Llama", "Mistral") — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, validates technique compatibility, and confirms selections.
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.
Generates a Jupyter notebook that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, and RLVR trainers, including RLVR Lambda reward function creation.
Use when reporting progress in autonomous loop iterations. Triggers at the end of every autonomous loop iteration, when the autonomous-loop skill completes a BUILD phase, when progress reporting is needed for monitoring or exit evaluation, or when producing machine-parseable RALPH_STATUS blocks with exit signal protocol.
This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.
스마일게이트 업무 도구(Slack, Jira, Confluence, BISKIT, API Docs)를 Claude Code에 연결하는 설정 가이드. 비개발자도 따라할 수 있도록 단계별로 안내한다. "커넥터", "connector", "MCP 설정", "jira 연결", "confluence 연결", "slack 연결", "biskit 연결", "비스킷 연결", "apidocs 연결", "api docs 연결" 요청에 사용.
Create a new skill, and automatically initialize the plugin structure if needed
Optimize agent skills to reduce context bloat while preserving answer coverage. Use when: (1) A skill's SKILL.md body exceeds ~250 lines or duplicates its references/ files (2) A skill's YAML description is verbose or triggers false positives from sibling skills (3) Planning or executing a body/reference split for a skill (4) Auditing skill token efficiency
Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop