菜单
Scan a data directory and produce structured report with file counts, sizes, and directory tree
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
Author a new agent Skill (a SKILL.md directory in the open Agent Skills format) from the Anthropic template. Use when the user wants to create a skill, scaffold a SKILL.md, package a repeatable workflow as a reusable skill, turn instructions into a skill, or capture a procedure so the agent can auto-invoke it later. Produces a valid SKILL.md (name + description frontmatter, optional scripts/ and references/) and saves it into the project's skills directory.
Convert VASP DFT calculations (slab or bulk) to ISAAC AI-ready records (v1.05). Handles IrOx surface slabs and ternary oxide bulk DOS calculations on NERSC Perlmutter.
Use this skill when working with the python modules `m3dc1_tools.py`, `m3dc1_plots.py`, `hdf5.py` and the codes created from the functions within. Triggers when working with M3D-C1 simulation data or repackaging general HDF5 files.
Convert XGC plasma turbulence simulation data (ADIOS2 BP5 format) into GNN-ready npz files and a PyTorch Dataset for AI/surrogate model training. Use when the user asks to preprocess XGC data, create training datasets from XGC simulations, or prepare fusion simulation data for machine learning.
| name | scan-directory |
| description | Scan a data directory and produce structured report with file counts, sizes, and directory tree |
| executable | dsagt-run --code scan-directory -- python codes/scan-directory/scripts/scan_directory.py |
| parameters | {"directory":{"type":"string","required":true,"cli":"positional","description":"Path to directory to scan"},"max_depth":{"type":"integer","required":false,"default":5,"cli":"--max-depth","description":"Maximum directory depth to traverse"},"top_n":{"type":"integer","required":false,"default":20,"cli":"--top-n","description":"Number of largest files to list"}} |
Scan a data directory and produce a structured report with file counts, sizes, and directory tree. Use this as your first step when exploring a new dataset to understand its layout before deciding how to process it.
python tools/scan_directory.py <directory> [--max_depth N] [--top_n N]
| Parameter | Required | Default | Description |
|---|---|---|---|
directory | yes | — | Path to directory to scan |
max_depth | no | 5 | Maximum directory depth to traverse |
top_n | no | 20 | Number of largest files to list |
python tools/scan_directory.py /data/raw --max_depth 3 --top_n 10
Prints a JSON report containing:
max_depth