creative-thinking-for-research
Applies cognitive science frameworks for creative thinking to generate genuinely novel research directions in computer science and AI.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
141 skills found
Applies cognitive science frameworks for creative thinking to generate genuinely novel research directions in computer science and AI.
Connect your AI agent to the Hugging Face Hub via MCP. Search models, datasets, and papers, manage repos, run cloud compute jobs, and invoke Gradio Spaces as functional AI tools.
AI-driven GitHub Actions automation featuring swarm-based workflow orchestration, intelligent CI/CD pipeline management, and autonomous repository maintenance.
A specialized decision-making agent for complex architectural choices, task planning, and error resolution within the orchestration system.
Generate publication-quality figures, charts, and LaTeX tables from experiment data for academic papers.
An AI-powered sales assistant that transforms business scenarios into optimized prompts, automatically generating high-quality emails, proposals, and analysis reports without requiring prompt engineering skills.
Analyze and identify codebase patterns (naming, architecture, testing) to maintain consistency and enforce standards during development.
Guided statistical analysis with test selection, assumption checking, power analysis, and APA-formatted reporting for academic and experimental research.
PyTorch Lightning skill for scalable deep learning: automates model training, multi-GPU orchestration, data pipelines, and distributed training strategies like DDP, FSDP, and DeepSpeed.
Process and generate multimedia with Google Gemini. Analyze audio, images, videos, and PDFs with high-context windows. Supports transcription, visual QA, OCR, and AI-driven image creation.
A Notion-based tracking system for tweet performance to enable data-driven content experimentation using reinforcement learning principles.
Perform cohort analysis on user engagement data. Identify retention trends, feature adoption rates, churn patterns, and generate actionable research recommendations through quantitative data analysis.