modelscope-dataset-explorer
Skill4 starsChanged 8 months agoApache-2.0View on GitHub
Explore ModelScope datasets from plain-language inspection requests using metadata lookup, selective download, and local profiling.
In this repository15 other files it gives its agents.
- agents/AGENTS.mdAGENTS.md
- modelscope-cliSkill
- modelscope-datasetsSkill
- modelscope-evaluationSkill
- gradioSkill
- modelscope-jobsSkill
- modelscope-local-trainerSkill
- modelscope-local-vision-trainerSkill
- modelscope-mcpSkill
- modelscope-model-trainerSkill
- modelscope-paper-publisherSkill
- modelscope-tool-builderSkill
- modelscope-trackingSkill
- modelscope-vision-trainerSkill
- transformers-jsSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Use this skill when the user wants to understand an existing dataset before training, cleaning, or publication.
- Distinctive: line 15
Start from metadata and only download files when inspection really needs local access.
- Distinctive: line 22
Run local profiling or SQL queries.
- Distinctive: line 41
Never dump sensitive rows into logs.
- Distinctive: line 42
Never download the whole dataset when metadata or a filtered subset is enough.
- Distinctive: line 43
Never recommend a dataset without mentioning real caveats.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
ModelScope Dataset Explorer
Use this skill when the user wants to understand an existing dataset before training, cleaning, or publication.
Request Style
- Accept requests such as:
Find a Chinese instruction dataset for SFT.检查这个数据集的列、切分和样本质量。
- Start from metadata and only download files when inspection really needs local access.
Workflow
- Resolve the target dataset from the request.
- Inspect metadata first.
- Download only the smallest useful subset.
- Run local profiling or SQL queries.
- Return concrete findings, not generic praise.
What To Report
- dataset id
- split layout
- schema summary
- file formats
- representative samples when safe
- concrete risks such as duplicates, imbalance, missing fields, or license ambiguity
Tools
- ModelScope metadata APIs or CLI download
- DuckDB for parquet/csv/jsonl profiling
Guardrails
- Never dump sensitive rows into logs.
- Never download the whole dataset when metadata or a filtered subset is enough.
- Never recommend a dataset without mentioning real caveats.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

