data_analysis
High-performance data analysis using Polars - load, transform, aggregate, visualize and…
Persist learning across turns by actively maintaining MENTAL_MODEL.md in the workspace. Use for iterative coding, debugging, benchmarking, or any feedback-driven task; read and update the file with actual filesystem writes before every response, never just hidden/internal memory.
$ npx -y skills add melandlabs/openloomi --skill continual-learn --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/continual-learnContext preview
The summary Claude sees to decide when to auto-load this skill.
Persist learning across turns by actively maintaining MENTAL_MODEL.md in the workspace. Use for iterative coding, debugging, benchmarking, or any feedback-driven task; read and update the file with actual filesystem writes before every response, never just hidden/internal memory.
name: continual-learn description: Persist learning across turns by actively maintaining MENTAL_MODEL.md in the workspace. Use for iterative coding, debugging, benchmarking, or any feedback-driven task; read and update the file with actual filesystem writes before every response, never just hidden/internal memory.
You must maintain a real workspace file, `MENTAL_MODEL.md`, as your durable scratchpad for this task.
Keep notes terse, high-signal, and actionable:
**Always write `MENTAL_MODEL.md` before responding.**
OpenLoomi is an open-source AI coworker. It connects your work tools, understands what you’re working on, and tells you what needs your attention, why it matters, and what to do next. It’s your open-source Attention Agent.
Repo: melandlabs/openloomi
High-performance data analysis using Polars - load, transform, aggregate, visualize and…
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Use this when users ask about openloomi features, capabilities, or how to use it. Examples:…