ai-ml-skills
27 ai & machine learning skills. Trigger: ML experiments, model training, deep learning, NLP, computer vision. Design: covers frameworks, benchmarks, paper reproduction, and AI research workflows.
Best use case
ai-ml-skills is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
27 ai & machine learning skills. Trigger: ML experiments, model training, deep learning, NLP, computer vision. Design: covers frameworks, benchmarks, paper reproduction, and AI research workflows.
Teams using ai-ml-skills should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-ml/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-ml-skills Compares
| Feature / Agent | ai-ml-skills | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
27 ai & machine learning skills. Trigger: ML experiments, model training, deep learning, NLP, computer vision. Design: covers frameworks, benchmarks, paper reproduction, and AI research workflows.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AI & Machine Learning — 27 Skills Select the skill matching the user's need, then `read` its SKILL.md. | Skill | Description | |-------|-------------| | [ai-agent-papers-guide](./ai-agent-papers-guide/SKILL.md) | Curated 2024-2026 AI agent research papers collection | | [ai-model-benchmarking](./ai-model-benchmarking/SKILL.md) | Benchmark AI models across 60+ academic evaluation suites and metrics | | [anomaly-detection-papers-guide](./anomaly-detection-papers-guide/SKILL.md) | Industrial anomaly detection methods and benchmark papers | | [autonomous-agents-papers-guide](./autonomous-agents-papers-guide/SKILL.md) | Daily-updated collection of autonomous AI agent papers | | [computer-vision-guide](./computer-vision-guide/SKILL.md) | Apply computer vision research methods, models, and evaluation tools | | [deep-learning-papers-guide](./deep-learning-papers-guide/SKILL.md) | Annotated deep learning paper implementations with code walkthroughs | | [dl-transformer-finetune](./dl-transformer-finetune/SKILL.md) | Build transformer fine-tuning plans for classification and generation | | [domain-adaptation-papers-guide](./domain-adaptation-papers-guide/SKILL.md) | Comprehensive collection of domain adaptation research papers | | [generative-ai-guide](./generative-ai-guide/SKILL.md) | Curated guide to generative AI covering LLMs and diffusion models | | [graph-learning-papers-guide](./graph-learning-papers-guide/SKILL.md) | Conference papers on graph neural networks and graph learning | | [huggingface-api](./huggingface-api/SKILL.md) | Search and discover ML models, datasets, and Spaces on Hugging Face | | [huggingface-inference-guide](./huggingface-inference-guide/SKILL.md) | Run NLP and CV model inference via Hugging Face free-tier API | | [keras-deep-learning](./keras-deep-learning/SKILL.md) | Build and debug deep learning models with Keras and TensorFlow backend | | [kolmogorov-arnold-networks-guide](./kolmogorov-arnold-networks-guide/SKILL.md) | Papers and tutorials on KAN learnable activation networks | | [llm-evaluation-guide](./llm-evaluation-guide/SKILL.md) | Evaluate and benchmark large language models for research applications | | [llm-from-scratch-guide](./llm-from-scratch-guide/SKILL.md) | Build a ChatGPT-like LLM from scratch using PyTorch step by step | | [ml-pipeline-guide](./ml-pipeline-guide/SKILL.md) | Build and deploy reproducible production ML pipelines for research | | [nlp-toolkit-guide](./nlp-toolkit-guide/SKILL.md) | NLP analysis with perplexity scoring, burstiness, and entropy metrics | | [npcpy-research-guide](./npcpy-research-guide/SKILL.md) | All-in-one Python library for NLP, agents, and knowledge graphs | | [prompt-engineering-research](./prompt-engineering-research/SKILL.md) | Systematic prompt engineering methods for AI-assisted academic research workf... | | [pytorch-guide](./pytorch-guide/SKILL.md) | Avoid common PyTorch mistakes and apply robust training patterns | | [pytorch-lightning-guide](./pytorch-lightning-guide/SKILL.md) | PyTorch Lightning framework for scalable model training and research | | [reinforcement-learning-guide](./reinforcement-learning-guide/SKILL.md) | Reinforcement learning fundamentals, algorithms, and research | | [responsible-ai-guide](./responsible-ai-guide/SKILL.md) | Resources for trustworthy, fair, and ethical AI research | | [tensorflow-guide](./tensorflow-guide/SKILL.md) | TensorFlow best practices for tf.function, GPU memory, and deployment | | [transformer-architecture-guide](./transformer-architecture-guide/SKILL.md) | Guide to Transformer architectures for NLP and computer vision | | [vmas-simulator-guide](./vmas-simulator-guide/SKILL.md) | Vectorized multi-agent reinforcement learning simulator |
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