claio0128 ML Researcher
Overview
This skill enables high-signal academic research and practical experimentation. It bridges the gap between theoretical understanding (papers) and hands-on application (code/data).
Core Capabilities & Workflow
1. Problem Definition & Real-World Impact
Identify the core challenge and practical applications.
- Problem Statement: What fundamental gap/problem is this model or method trying to solve?
- Real-Life Examples: Provide 2-3 concrete scenarios where this technology is applied (e.g., "Automating complex medical document analysis" or "Providing spatial awareness for autonomous warehouse robots").
2. Trend Discovery & Initial Research
Identify the most recent or influential paper/trend on a specific ML topic.
- Workflow: Search topic (ArXiv/Scholar) → Rank by citation/impact/recency → Identify the current "state-of-the-art" (SOTA).
3. Historical Tracing & Milestones
Trace the development of the topic through its "Related Work" and key references.
- Milestones: Identify 3-5 key papers that represent significant shifts in the field.
- Novelty Analysis: For each milestone, explicitly state how it claimed novelty over previous work.
4. Benchmark & Evaluation Analysis
Identify the "Gold Standard" datasets and metrics used to judge performance in this field.
- Datasets: Catalog the most common training and evaluation datasets (e.g., MMMU for VLM, RT-X for VLA).
- Metrics: Identify the standard evaluation metrics (e.g., Accuracy, FID, Success Rate, Perplexity).
5. Competing & Alternative Solutions
Identify and compare different approaches to the same problem.
- Search: Find alternative architectures or algorithms.
- Analysis: Create a Pros/Cons comparison table for each solution.
6. Code & Data Discovery
Locate the "source of truth" for implementation and training data.
- GitHub/HuggingFace: Find official repositories, model weights, and datasets.
7. Practical "Try-out" Workflow
Generate actionable instructions for running the model or algorithm.
- Environment Setup: Requirements, dependencies, and hardware needs.
- Execution: Provide a minimal Python script or shell command for a "toy" demo.
8. Report Generation & Persistence
Synthesize findings into a structured report using the DEEP_DIVE_TEMPLATE.md.
- Persistence: ALWAYS save the final report as a Markdown file in the
./research/directory.
Resources
references/
- DEEP_DIVE_TEMPLATE.md: Updated template with sections for Problem Definition and Real-World Examples.
- BENCHMARK_GUIDE.md: A quick-reference guide for ML benchmarks across domains.
When to use this skill
- When the user asks to "deep dive" into a specific ML topic or paper.
- When you need to understand the "lineage" or "benchmarking standards" of a model.
- When comparing different architectures or training methods.
- When the user wants to "try out" a new model and needs setup instructions.
- When the user wants to document ML research results in the workspace.