Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/synthetic-sciences/openscience/hugging-face-datasetsnpx skills add synthetic-sciences/openscience --skill hugging-face-datasetsgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/hugging-face-datasets)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/hugging-face-datasets"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/hugging-face-datasets.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00049 | $0.04153 |
| Opus 5 | $0.00024 | $0.02076 |
| Sonnet 5 | $0.00010 | $0.00831 |
| Haiku 4.5 | $0.00005 | $0.00415 |
Grade A, and why
hugging-face-datasets scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 561 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This skill provides tools to manage datasets on the Hugging Face Hub with a focus on creation, configuration, content management, and SQL-based data manipulation. It is designed to complement the existing Hugging Face MCP server by providing dataset editing and querying capabilities.
Integration with HF MCP Server
- Use HF MCP Server for: Dataset discovery, search, and metadata retrieval
- Use This Skill for: Dataset creation, content editing, SQL queries, data transformation, and structured data formatting
Version
2.1.0
Dependencies
This skill uses PEP 723 scripts with inline dependency management
Scripts auto-install requirements when run with: uv run scripts/script_name.py
- uv (Python package manager)
- Getting Started: See "Usage Instructions" below for PEP 723 usage
Core Capabilities
1. Dataset Lifecycle Management
- Initialize: Create new dataset repositories with proper structure
- Configure: Store detailed configuration including system prompts and metadata
- Stream Updates: Add rows efficiently without downloading entire datasets
2. SQL-Based Dataset Querying (NEW)
Query any Hugging Face dataset using DuckDB SQL via scripts/sql_manager.py:
- Direct Queries: Run SQL on datasets using the
hf://protocol - Schema Discovery: Describe dataset structure and column types
- Data Sampling: Get random samples for exploration
- Aggregations: Count, histogram, unique values analysis
- Transformations: Filter, join, reshape data with SQL
- Export & Push: Save results locally or push to new Hub repos
3. Multi-Format Dataset Support
Supports diverse dataset types through template system:
- Chat/Conversational: Chat templating, multi-turn dialogues, tool usage examples
- Text Classification: Sentiment analysis, intent detection, topic classification
- Question-Answering: Reading comprehension, factual QA, knowledge bases
- Text Completion: Language modeling, code completion, creative writing
- Tabular Data: Structured data for regression/classification tasks
- Custom Formats: Flexible schema definition for specialized needs
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/diverse_training_examples.json 17 KB
- examples/system_prompt_template.txt 5.3 KB
- examples/training_examples.json 6.7 KB
- scripts/dataset_manager.py 18 KB runs code
- scripts/sql_manager.py 28 KB runs code
- templates/chat.json 2.2 KB
- templates/classification.json 1.9 KB
- templates/completion.json 2.6 KB
- templates/custom.json 2.4 KB
- templates/qa.json 2.6 KB
- templates/tabular.json 2.5 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 561 lines · 49 tokens per session scan A 5e9143dc9be2
hugging-face-datasets is a skill published in the GitHub repository synthetic-sciences/openscience (3,432 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 4,153 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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