AI Engineering Hub is a learning and project repository covering large language models, retrieval-augmented generation, AI agents, and related applications. Beginners, practitioners, and researchers use its tutorials and projects to learn AI engineering and build working systems. The catalogue entries are examples of the skills, plugins, and agent resources included with it.
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 skills add patchy631/ai-engineering-hub --skill hugging-face-datasetsgit clone --depth 1 https://github.com/patchy631/ai-engineering-hubWrote 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/patchy631/ai-engineering-hub/hugging-face-datasets)<a href="https://agentmods.dev/skills/patchy631/ai-engineering-hub/hugging-face-datasets"><img src="https://agentmods.dev/badge/skills/patchy631/ai-engineering-hub/hugging-face-datasets/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/patchy631/ai-engineering-hub/hugging-face-datasets"><img src="https://agentmods.dev/badge/skills/patchy631/ai-engineering-hub/hugging-face-datasets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 63 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 5.1 | $0.00049 | $0.03998 |
| Opus 5.5 | $0.00020 | $0.01599 |
| Sonnet 5.5 | $0.00010 | $0.00800 |
| Haiku 4.5 | $0.00005 | $0.00400 |
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 1mo ago.
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.
Copies of this mod
3 near-identical copies found in the catalogue:
- hugging-face-datasets — 91% identical, 1,094 lines differ
- hugging-face-datasets — 88% identical, 1,082 lines differ
- hugging-face-datasets — 86% identical, 1,110 lines differ
How it starts
The opening of the file, as written. The whole thing — 541 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
- huggingface_hub
- duckdb (for SQL queries)
- datasets (for pushing query results to Hub)
- json (built-in)
- time (built-in)
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 18 KB
- examples/system_prompt_template.txt 5.5 KB
- examples/training_examples.json 6.8 KB
- scripts/dataset_manager.py 19 KB runs code
- scripts/sql_manager.py 29 KB runs code
- templates/chat.json 2.3 KB
- templates/classification.json 1.9 KB
- templates/completion.json 2.7 KB
- templates/custom.json 2.4 KB
- templates/qa.json 2.7 KB
- templates/tabular.json 2.6 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.
- 1mo ago First seen · 541 lines · 49 tokens per session scan A 94142a9dd019
hugging-face-datasets is a skill published in the GitHub repository patchy631/ai-engineering-hub (38,409 stars, last pushed 10d ago), licensed MIT. It adds 49 tokens to every session and 3,998 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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