numpy-structured
Structured arrays allow ndarrays to contain data with different types in named "fields," mimicking C structs. They are used primarily for interfacing with binary data from external sources and interpr
npx skills add majiayu000/claude-skill-registry --skill numpy-structured-cuba6112-skillfactory-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
--- name: numpy-structured description: Structured and record arrays for C-interoperability, binary blob interpretation, and multi-field tabular data handling. Triggers: structured array, record array, compound dtype, multi-field index. --- ## Overview Structured arrays allow ndarrays to contain data with different types in named "fields," mimicking C structs. They are used primarily for interfacing with binary data from external sources and interpreting complex memory layouts without converting to high-level objects like Pandas DataFrames. ## When to Use - Interpreting binary blobs or file headers from C/C++ applications. - Storing tabular data where each row has multiple related attributes (e.g., ID, Timestamp, Value). - Mapping hardware-aligned buffers to named fields for easier access. - Performing multi-field updates on a shared data buffer. ## Decision Tree 1. Need to map names to columns in a single array buffer? - Use a structured array with a compound `dtype`. 2. Working with binary data from a file? - Use `arr.view(dtype=your_struct_dtype)` to interpret bytes without copying. 3. Selecting multiple fields? - Use a list of names `arr[['id', 'name']]`. This returns a view. #
- Overview
- When to Use
- Decision Tree
- Workflows
- Non-Obvious Insights
- Evidence
- Scripts
- Dependencies
- References
What does the numpy-structured skill do?
Structured arrays allow ndarrays to contain data with different types in named "fields," mimicking C structs. They are used primarily for interfacing with binary data from external sources and interpr
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill numpy-structured-cuba6112-skillfactory-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.