القائمة
Scan a data directory and produce structured report with file counts, sizes, and directory tree
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
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.
استنادا إلى تصنيف SOC المهني
| 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