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NanoData

NanoData contains 11 collected skills from oNya685, with repository-level occupation coverage and site-owned skill detail pages.

skills collected
11
Stars
1
updated
2026-03-17
Forks
0
Occupation coverage
3 occupation categories · 100% classified
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Skills in this repository

clawhub
software-developers

Search and install agent skills from ClawHub, the public skill registry.

2026-03-17
cron
software-developers

Schedule reminders and recurring tasks.

2026-03-17
data-cleaning
data-scientists-152051

End-to-end data preprocessing pipeline to transform raw files (CSV, Excel, Parquet) into AI-Ready assets. Use this skill when the user requests to clean data, handle missing values, fix data types, remove duplicates, standardize formats, or prepare datasets for model training. Includes automatic profiling and dataset card generation.

2026-03-17
data-to-text
data-scientists-152051

Convert structured data (CSV/Parquet/Excel) into semantic, readable text documents or experimental reports for LLM fine-tuning. Use when the user wants to transform tabular data into natural language narratives, generate training corpus, or create AI-Ready text documents from datasets. Output is Markdown format ready for further processing.

2026-03-17
github
software-developers

Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.

2026-03-17
multimodal-augmentation
data-scientists-152051

Generate images for existing SFT datasets (JSON/JSONL) to build multimodal fine-tuning data; use when you need to add image prompts, descriptions, and saved image files for each QA pair or conversation, via SubAgents + image_generate tool.

2026-03-17
scientific-data-parser
data-scientists-152051

Parse specialized scientific data formats (e.g., HDF5, NetCDF, FITS, mzML, CIF, PDB) or unfamiliar raw data files. Use when the user wants to process complex scientific files into AI-Ready formats.

2026-03-17
sft-dataset
data-scientists-152051

Transform AI-Ready documents (Markdown) into LLM fine-tuning datasets using Easy Dataset. Use when the user wants to generate SFT training data from existing documents, create QA pairs from text, or prepare datasets for model fine-tuning. Requires Easy Dataset service running.

2026-03-17
summarize
software-developers

Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).

2026-03-17
tmux
network-and-computer-systems-administrators

Remote-control tmux sessions for interactive CLIs by sending keystrokes and scraping pane output.

2026-03-17
weather
software-developers

Get current weather and forecasts (no API key required).

2026-03-17