character-generator
Generate complete elizaOS character configurations with personality, knowledge, and plugin setup. Triggers when user asks to "create character", "generate agent config", or "build elizaOS character"
Generate complete elizaOS character configurations with personality, knowledge, and plugin setup. Triggers when user asks to "create character", "generate agent config", or "build elizaOS character"
Guide for defining and using Claude subagents effectively. Use when (1) creating new subagent types, (2) learning how to delegate work to specialized subagents, (3) improving subagent delegation prompts, (4) understanding subagent orchestration patterns, or (5) debugging ineffective subagent usage.
Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. Finds best parameter configurations to maximize performance. Use when asked to "tune hyperparameters" or "optimize model".
Use when specifying or implementing LangGraph applications - from architecture planning and specification writing to actual code implementation. Also use for designing agent workflows or learning LangGraph patterns. This is a comprehensive guide for building AI agents with LangGraph, covering core concepts, architecture patterns, memory management, tool integration, and advanced features.
Build stateful AI agents and agentic workflows with LangGraph in Python. Covers tool-using agents with LLM-tool loops, branching workflows, conversation memory, human-in-the-loop oversight, and production monitoring. Use when - (1) building agents that use tools and loop until task complete, (2) creating multi-step workflows with conditional branches, (3) adding persistence/memory across turns with checkpointers, (4) implementing human approval with interrupt(), (5) debugging via time-travel or LangSmith. Covers StateGraph, nodes, edges, add_conditional_edges, MessagesState, thread_id, Command objects, and ToolMessage handling. Examples include chatbots, calculator agents, and structured workflows.
End-to-end Lucid Agent creation, testing, and deployment pipeline
Exports FiftyOne datasets to standard formats (COCO, YOLO, VOC, CVAT, CSV, etc.). Use when converting datasets, exporting for training, creating archives, or sharing data in specific formats.
Complete and score a learning episode to extract patterns and update heuristics. Use when finalizing a task to enable pattern extraction and future learning.
ML 모델 파일 서버 간 동기화. "모델 동기화", "모델 배포", "rsync 모델", "서버로 전송" 요청 시 활성화됩니다.
Imports datasets into FiftyOne with automatic format detection. Supports all media types (images, videos, point clouds), label formats (COCO, YOLO, VOC, KITTI), and multimodal grouped datasets. Use when importing datasets, loading autonomous driving data, or creating grouped datasets.
Setup machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to "setup experiment tracking" or "initialize MLflow".
Neural networks, CNNs, RNNs, Transformers with TensorFlow and PyTorch. Use for image classification, NLP, sequence modeling, or complex pattern recognition.
Optimize deep learning models using Adam, SGD, and learning rate scheduling to improve accuracy and reduce training time. Use when asked to "optimize deep learning model" or "improve model performance".
ML-based variable imputation for survey data - used in policyengine-us-data to fill missing values
CRITICAL: MUST run for EVERY message. Detects agent, complexity, AND model automatically. Always runs FIRST.
Evaluate model predictions against ground truth using COCO, Open Images, or custom protocols. Use when computing mAP, precision, recall, confusion matrices, or analyzing TP/FP/FN examples for detection, classification, segmentation, or regression tasks.
Supervised/unsupervised learning, model selection, evaluation, and scikit-learn. Use for building classification, regression, or clustering models.
Process and analyze survey data to extract insights, identify patterns, and generate actionable recommendations
Create interactive Chart.js visualizations for DST data analysis. Use when generating charts, creating visual reports, building dashboards, or displaying trends from database tables.
This skill calculates key financial ratios and metrics from financial statement data for investment analysis
Create comprehensive social media analytics reports with insights and recommendations