add-resource
Add new learning resources (books, articles, courses, papers) to the appropriate resources.md file. Use when user mentions adding, saving, or tracking learning materials.
Best use case
add-resource is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Add new learning resources (books, articles, courses, papers) to the appropriate resources.md file. Use when user mentions adding, saving, or tracking learning materials.
Teams using add-resource 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/add-resource/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How add-resource Compares
| Feature / Agent | add-resource | 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?
Add new learning resources (books, articles, courses, papers) to the appropriate resources.md file. Use when user mentions adding, saving, or tracking learning materials.
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
# Add Resource When the user wants to add a learning resource to their repository: ## Instructions 1. Determine which topic folder the resource belongs to: - foundations/ - Math, statistics, algorithms, systems - data-analytics/ - EDA, visualization, SQL, data wrangling, business analytics - machine-learning/ - Traditional ML, supervised/unsupervised learning - deep-learning/ - Neural networks, transformers, CNNs, etc. - ml-system-design/ - System design for ML applications - ai-engineering/ - LLMs, agents, RAG, prompt engineering - productionization/ - MLOps, deployment, monitoring - software-engineering/ - Best practices, design patterns - ai-productivity/ - AI-powered tools (ChatGPT, Claude, Cursor, Copilot, etc.) - interview-prep/ - Interview-specific materials 2. Read the current resources.md file in that folder 3. Add the resource in consistent format: ```markdown - [Title](link) - Author/Source - Brief description of what it covers ``` 4. Organize entries: - Group by type (Books, Articles, Courses, Papers, etc.) if multiple types exist - Within each type, maintain alphabetical order by title - If the file is empty, start with a simple list ## Examples ### Book ```markdown - [Designing Data-Intensive Applications](https://dataintensive.net/) - Martin Kleppmann - Deep dive into distributed systems, storage, and processing ``` ### Course ```markdown - [CS229: Machine Learning](https://cs229.stanford.edu/) - Stanford - Andrew Ng's classic ML course covering fundamentals ``` ### Article ```markdown - [Attention Is All You Need](https://arxiv.org/abs/1706.03762) - Vaswani et al. - Original transformer architecture paper ``` ## Edge Cases - If unclear which folder: Ask the user or suggest the most relevant one - If resource fits multiple topics: Add to primary topic and note cross-reference - If resources.md doesn't exist yet: Create it with proper header
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