Overview workflow skill. Use this skill when the user needs Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Overview workflow skill. Use this skill when the user needs Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/hugging-face-datasets from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
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.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Integration with HF MCP Server, 1. Dataset Lifecycle Management, 2. SQL-Based Dataset Querying (NEW), 3. Multi-Format Dataset Support, 4. Quality Assurance Features, SQL Query Syntax.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
You need to create, configure, or update datasets on the Hugging Face Hub.
You want SQL-style querying, transformation, or export flows over Hub datasets.
You are managing dataset content and metadata directly rather than only searching existing datasets.
Use when the request clearly matches the imported source intent: Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP....
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Use when provenance needs to stay visible in the answer, PR, or review packet.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: 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
Examples
Example 1: Ask for the upstream workflow directly
Use @hugging-face-datasets-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @hugging-face-datasets-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @hugging-face-datasets-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @hugging-face-datasets-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Imported Usage Notes
Imported: Quick Start
# Query a dataset
uv run scripts/sql_manager.py query \
--dataset "cais/mmlu" \
--sql "SELECT * FROM data WHERE subject='nutrition' LIMIT 10"# Get dataset schema
uv run scripts/sql_manager.py describe --dataset "cais/mmlu"# Sample random rows
uv run scripts/sql_manager.py sample --dataset "cais/mmlu" --n 5
# Count rows with filter
uv run scripts/sql_manager.py count --dataset "cais/mmlu" --where"subject='nutrition'"
Imported: Python API Usage
from sql_manager import HFDatasetSQL
sql = HFDatasetSQL()
# Query
results = sql.query("cais/mmlu", "SELECT * FROM data WHERE subject='nutrition' LIMIT 10")
# Get schema
schema = sql.describe("cais/mmlu")
# Sample
samples = sql.sample("cais/mmlu", n=5, seed=42)
# Count
count = sql.count("cais/mmlu", where="subject='nutrition'")
# Histogram
dist = sql.histogram("cais/mmlu", "subject")
# Filter and transform
results = sql.filter_and_transform(
"cais/mmlu",
select="subject, COUNT(*) as cnt",
group_by="subject",
order_by="cnt DESC",
limit=10
)
# Push to Hub
url = sql.push_to_hub(
"cais/mmlu",
"username/nutrition-subset",
sql="SELECT * FROM data WHERE subject='nutrition'",
private=True
)
# Export locally
sql.export_to_parquet("cais/mmlu", "output.parquet", sql="SELECT * FROM data LIMIT 100")
sql.close()
Imported: TRAINING EXAMPLE STRUCTURE
[Specify exact JSON schema for chat templating]
Imported: EXAMPLE CATEGORIES
[List development workflows, debugging scenarios, data management tasks]
### Example Categories & Templates
The skill includes diverse training examples beyond just MCP usage:
**Available Example Sets:**
- `training_examples.json` - MCP tool usage examples (debugging, project setup, database analysis)
- `diverse_training_examples.json` - Broader scenarios including:
- **Educational Chat** - Explaining programming concepts, tutorials
- **Git Workflows** - Feature branches, version control guidance
- **Code Analysis** - Performance optimization, architecture review
- **Content Generation** - Professional writing, creative brainstorming
- **Codebase Navigation** - Legacy code exploration, systematic analysis
- **Conversational Support** - Problem-solving, technical discussions
**Using Different Example Sets:**
```bash
# Add MCP-focused examples
uv run scripts/dataset_manager.py add_rows --repo_id "your-username/dataset-name" \
--rows_json "$(cat examples/training_examples.json)"
# Add diverse conversational examples
uv run scripts/dataset_manager.py add_rows --repo_id "your-username/dataset-name" \
--rows_json "$(cat examples/diverse_training_examples.json)"
# Mix both for comprehensive training data
uv run scripts/dataset_manager.py add_rows --repo_id "your-username/dataset-name" \
--rows_json "$(jq -s '.[0] + .[1]' examples/training_examples.json examples/diverse_training_examples.json)"
Commands Reference
List Available Templates:
uv run scripts/dataset_manager.py list_templates
Quick Setup (Recommended):
uv run scripts/dataset_manager.py quick_setup --repo_id "your-username/dataset-name" --template classification
Manual Setup:
# Initialize repository
uv run scripts/dataset_manager.py init --repo_id "your-username/dataset-name" [--private]
# Configure with system prompt
uv run scripts/dataset_manager.py config --repo_id "your-username/dataset-name" --system_prompt "Your prompt here"# Add data with validation
uv run scripts/dataset_manager.py add_rows \
--repo_id "your-username/dataset-name" \
--template qa \
--rows_json '[{"question": "What is AI?", "answer": "Artificial Intelligence..."}]'
View Dataset Statistics:
uv run scripts/dataset_manager.py stats --repo_id "your-username/dataset-name"
Error Handling
Repository exists: Script will notify and continue with configuration
Invalid JSON: Clear error message with parsing details
Network issues: Automatic retry for transient failures
Token permissions: Validation before operations begin
Combined Workflow Examples
Imported: Example 1: Create Training Subset from Existing Dataset
# 1. Explore the source dataset
uv run scripts/sql_manager.py describe --dataset "cais/mmlu"
uv run scripts/sql_manager.py histogram --dataset "cais/mmlu" --column "subject"# 2. Query and create subset
uv run scripts/sql_manager.py query \
--dataset "cais/mmlu" \
--sql "SELECT * FROM data WHERE subject IN ('nutrition', 'anatomy', 'clinical_knowledge')" \
--push-to "username/mmlu-medical-subset" \
--private
Imported: Example 2: Transform and Reshape Data
# Transform MMLU to QA format with correct answers extracted
uv run scripts/sql_manager.py query \
--dataset "cais/mmlu" \
--sql "SELECT question, choices[answer] as correct_answer, subject FROM data" \
--push-to "username/mmlu-qa-format"
Imported: Example 3: Merge Multiple Dataset Splits
# Export multiple splits and combine
uv run scripts/sql_manager.py export \
--dataset "cais/mmlu" \
--split"*" \
--output "mmlu_all.parquet"
Imported: Example 4: Quality Filtering
# Filter for high-quality examples
uv run scripts/sql_manager.py query \
--dataset "squad" \
--sql "SELECT * FROM data WHERE LENGTH(context) > 500 AND LENGTH(question) > 20" \
--push-to "username/squad-filtered"
Imported: Example 5: Create Custom Training Dataset
# 1. Query source data
uv run scripts/sql_manager.py export \
--dataset "cais/mmlu" \
--sql "SELECT question, subject FROM data WHERE subject='nutrition'" \
--output "nutrition_source.jsonl" \
--format jsonl
# 2. Process with your pipeline (add answers, format, etc.)# 3. Push processed data
uv run scripts/dataset_manager.py init --repo_id "username/nutrition-training"
uv run scripts/dataset_manager.py add_rows \
--repo_id "username/nutrition-training" \
--template qa \
--rows_json "$(cat processed_data.json)"
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
[Detail requirements for realistic scenarios, progressive complexity, proper tool usage]
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Imported Operating Notes
Imported: QUALITY GUIDELINES
[Detail requirements for realistic scenarios, progressive complexity, proper tool usage]
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/hugging-face-datasets, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: 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
Imported: 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
Text Completion: Language modeling, code completion, creative writing
Tabular Data: Structured data for regression/classification tasks
Custom Formats: Flexible schema definition for specialized needs
Imported: 4. Quality Assurance Features
JSON Validation: Ensures data integrity during uploads
Batch Processing: Efficient handling of large datasets
Error Recovery: Graceful handling of upload failures and conflicts
Usage Instructions
The skill includes two Python scripts that use PEP 723 inline dependency management:
All paths are relative to the directory containing this SKILL.md
file.
Scripts are run with: uv run scripts/script_name.py [arguments]
scripts/dataset_manager.py - Dataset creation and management
scripts/sql_manager.py - SQL-based dataset querying and transformation
Prerequisites
uv package manager installed
HF_TOKEN environment variable must be set with a Write-access token
SQL Dataset Querying (sql_manager.py)
Query, transform, and push Hugging Face datasets using DuckDB SQL. The hf:// protocol provides direct access to any public dataset (or private with token).
Imported: SQL Query Syntax
Use data as the table name in your SQL - it gets replaced with the actual hf:// path:
-- Basic selectSELECT*FROM data LIMIT 10-- FilteringSELECT*FROM data WHERE subject='nutrition'-- AggregationsSELECT subject, COUNT(*) as cnt FROM data GROUPBY subject ORDERBY cnt DESC-- Column selection and transformationSELECT question, choices[answer] AS correct_answer FROM data
-- Regex matchingSELECT*FROM data WHERE regexp_matches(question, 'nutrition|diet')
-- String functionsSELECT regexp_replace(question, '\n', '') AS cleaned FROM data
Imported: Common Operations
1. Explore Dataset Structure
# Get schema
uv run scripts/sql_manager.py describe --dataset "cais/mmlu"# Get unique values in column
uv run scripts/sql_manager.py unique --dataset "cais/mmlu" --column "subject"# Get value distribution
uv run scripts/sql_manager.py histogram --dataset "cais/mmlu" --column "subject" --bins 20
2. Filter and Transform
# Complex filtering with SQL
uv run scripts/sql_manager.py query \
--dataset "cais/mmlu" \
--sql "SELECT subject, COUNT(*) as cnt FROM data GROUP BY subject HAVING cnt > 100"# Using transform command
uv run scripts/sql_manager.py transform \
--dataset "cais/mmlu" \
--select"subject, COUNT(*) as cnt" \
--group-by "subject" \
--order-by "cnt DESC" \
--limit 10
3. Create Subsets and Push to Hub
# Query and push to new dataset
uv run scripts/sql_manager.py query \
--dataset "cais/mmlu" \
--sql "SELECT * FROM data WHERE subject='nutrition'" \
--push-to "username/mmlu-nutrition-subset" \
--private
# Transform and push
uv run scripts/sql_manager.py transform \
--dataset "ibm/duorc" \
--config "ParaphraseRC" \
--select"question, answers" \
--where"LENGTH(question) > 50" \
--push-to "username/duorc-long-questions"
4. Export to Local Files
# Export to Parquet
uv run scripts/sql_manager.py export \
--dataset "cais/mmlu" \
--sql "SELECT * FROM data WHERE subject='nutrition'" \
--output "nutrition.parquet" \
--format parquet
# Export to JSONL
uv run scripts/sql_manager.py export \
--dataset "cais/mmlu" \
--sql "SELECT * FROM data LIMIT 100" \
--output "sample.jsonl" \
--format jsonl
5. Working with Dataset Configs/Splits
# Specify config (subset)
uv run scripts/sql_manager.py query \
--dataset "ibm/duorc" \
--config "ParaphraseRC" \
--sql "SELECT * FROM data LIMIT 5"# Specify split
uv run scripts/sql_manager.py query \
--dataset "cais/mmlu" \
--split"test" \
--sql "SELECT COUNT(*) FROM data"# Query all splits
uv run scripts/sql_manager.py query \
--dataset "cais/mmlu" \
--split"*" \
--sql "SELECT * FROM data LIMIT 10"
6. Raw SQL with Full Paths
For complex queries or joining datasets:
uv run scripts/sql_manager.py raw --sql "
SELECT a.*, b.*
FROM 'hf://datasets/dataset1@~parquet/default/train/*.parquet' a
JOIN 'hf://datasets/dataset2@~parquet/default/train/*.parquet' b
ON a.id = b.id
LIMIT 100
"
Imported: HF Path Format
DuckDB uses the hf:// protocol to access datasets:
# Use HF MCP tools to find existing datasets
search_datasets("conversational AI training")
get_dataset_details("username/dataset-name")
2. Creation (Use This Skill):
# Initialize new dataset
uv run scripts/dataset_manager.py init --repo_id "your-username/dataset-name" [--private]
# Configure with detailed system prompt
uv run scripts/dataset_manager.py config --repo_id "your-username/dataset-name" --system_prompt "$(cat system_prompt.txt)"
3. Content Management (Use This Skill):
# Quick setup with any template
uv run scripts/dataset_manager.py quick_setup \
--repo_id "your-username/dataset-name" \
--template classification
# Add data with template validation
uv run scripts/dataset_manager.py add_rows \
--repo_id "your-username/dataset-name" \
--template qa \
--rows_json "$(cat your_qa_data.json)"
Template-Based Data Structures
1. Chat Template (--template chat)
{"messages":[{"role":"user","content":"Natural user request"},{"role":"assistant","content":"Response with tool usage"},{"role":"tool","content":"Tool response","tool_call_id":"call_123"}],"scenario":"Description of use case","complexity":"simple|intermediate|advanced"}
{"text":"Input text to be classified","label":"classification_label","confidence":0.95,"metadata":{"domain":"technology","language":"en"}}
3. QA Template (--template qa)
{"question":"What is the question being asked?","answer":"The complete answer","context":"Additional context if needed","answer_type":"factual|explanatory|opinion","difficulty":"easy|medium|hard"}
4. Completion Template (--template completion)
{"prompt":"The beginning text or context","completion":"The expected continuation","domain":"code|creative|technical|conversational","style":"description of writing style"}
You are an AI assistant expert at using MCP tools effectively.
#### Imported: MCP SERVER DEFINITIONS
[Define available servers and tools]
#### Imported: Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
