skills/cs-research-methodology/SKILL.md
Conduct a literature review and develop a CS research proposal. Use when asked to review a research area, find gaps in existing work, and propose a novel research contribution. The output is a research proposal identifying an assumption to challenge (the "bit flip") and how to validate it.
npx skillsauth add sundial-org/skills cs-research-methodologyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Framework for investigating problems by identifying assumptions and proposing alternatives.
Every research contribution follows this pattern:
What do existing approaches take for granted?
→ See references/framing.md for process and examples.
When does the assumption break down?
→ See references/landscape.md for mapping approaches.
"Current approaches assume X. Instead, Y."
→ See references/prioritization.md for focusing investigation.
What evidence would be convincing?
→ See references/validation.md for evaluation design.
development
Data visualization design based on Stanford CS448B. Use for: (1) choosing chart types, (2) selecting visual encodings, (3) critiquing visualizations, (4) building D3.js visualizations, (5) designing interactions/animations, (6) choosing colors, (7) visualizing networks, (8) visualizing text. Covers Bertin, Mackinlay, Cleveland & McGill.
testing
Guidelines for creating high-quality datasets for LLM post-training (SFT/DPO/RLHF). Use when preparing data for fine-tuning, evaluating data quality, or designing data collection strategies.
development
Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, vision-language models, and both high-level Cookbook patterns and low-level API usage.
data-ai
Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.