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Designed to work alongside HF MCP server for comprehensive dataset workflows.","aicreator350\u002FaurionOFFICIELLE","aicreator350","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F217751428?v=4","01M731QVHB22WCM6PDX5VFN1RV","01M731QVJ0TAD058M1SW9MY548",0,"MIT","https:\u002F\u002Fgithub.com\u002Faicreator350\u002FaurionOFFICIELLE\u002Fblob\u002Fmain\u002F.claude\u002F_archive\u002Fskills-batch2\u002Fhugging-face-datasets\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: hugging-face-datasets\ndescription: Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs\u002Fsystem prompts, streaming row updates, and SQL-based dataset querying\u002Ftransformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.\n---\n\n# Overview\nThis 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.\n\n## Integration with HF MCP Server\n- **Use HF MCP Server for**: Dataset discovery, search, and metadata retrieval\n- **Use This Skill for**: Dataset creation, content editing, SQL queries, data transformation, and structured data formatting\n\n# Version\n2.1.0\n\n# Dependencies\n- huggingface_hub\n- duckdb (for SQL queries)\n- datasets (for pushing query results to Hub)\n- json (built-in)\n- time (built-in)\n\n# Core Capabilities\n\n## 1. Dataset Lifecycle Management\n- **Initialize**: Create new dataset repositories with proper structure\n- **Configure**: Store detailed configuration including system prompts and metadata\n- **Stream Updates**: Add rows efficiently without downloading entire datasets\n\n## 2. SQL-Based Dataset Querying (NEW)\nQuery any Hugging Face dataset using DuckDB SQL via `scripts\u002Fsql_manager.py`:\n- **Direct Queries**: Run SQL on datasets using the `hf:\u002F\u002F` protocol\n- **Schema Discovery**: Describe dataset structure and column types\n- **Data Sampling**: Get random samples for exploration\n- **Aggregations**: Count, histogram, unique values analysis\n- **Transformations**: Filter, join, reshape data with SQL\n- **Export & Push**: Save results locally or push to new Hub repos\n\n## 3. Multi-Format Dataset Support\nSupports diverse dataset types through template system:\n- **Chat\u002FConversational**: Chat templating, multi-turn dialogues, tool usage examples\n- **Text Classification**: Sentiment analysis, intent detection, topic classification\n- **Question-Answering**: Reading comprehension, factual QA, knowledge bases\n- **Text Completion**: Language modeling, code completion, creative writing\n- **Tabular Data**: Structured data for regression\u002Fclassification tasks\n- **Custom Formats**: Flexible schema definition for specialized needs\n\n## 4. Quality Assurance Features\n- **JSON Validation**: Ensures data integrity during uploads\n- **Batch Processing**: Efficient handling of large datasets\n- **Error Recovery**: Graceful handling of upload failures and conflicts\n\n# Usage Instructions\n\nThe skill includes two Python scripts:\n- `scripts\u002Fdataset_manager.py` - Dataset creation and management\n- `scripts\u002Fsql_manager.py` - SQL-based dataset querying and transformation\n\n### Prerequisites\n- `huggingface_hub` library: `uv add huggingface_hub`\n- `duckdb` library (for SQL): `uv add duckdb`\n- `datasets` library (for pushing): `uv add datasets`\n- `HF_TOKEN` environment variable must be set with a Write-access token\n- Activate virtual environment: `source .venv\u002Fbin\u002Factivate`\n\n---\n\n# SQL Dataset Querying (sql_manager.py)\n\nQuery, transform, and push Hugging Face datasets using DuckDB SQL. The `hf:\u002F\u002F` protocol provides direct access to any public dataset (or private with token).\n\n## Quick Start\n\n```bash\n# Query a dataset\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data WHERE subject='nutrition' LIMIT 10\"\n\n# Get dataset schema\npython scripts\u002Fsql_manager.py describe --dataset \"cais\u002Fmmlu\"\n\n# Sample random rows\npython scripts\u002Fsql_manager.py sample --dataset \"cais\u002Fmmlu\" --n 5\n\n# Count rows with filter\npython scripts\u002Fsql_manager.py count --dataset \"cais\u002Fmmlu\" --where \"subject='nutrition'\"\n```\n\n## SQL Query Syntax\n\nUse `data` as the table name in your SQL - it gets replaced with the actual `hf:\u002F\u002F` path:\n\n```sql\n-- Basic select\nSELECT * FROM data LIMIT 10\n\n-- Filtering\nSELECT * FROM data WHERE subject='nutrition'\n\n-- Aggregations\nSELECT subject, COUNT(*) as cnt FROM data GROUP BY subject ORDER BY cnt DESC\n\n-- Column selection and transformation\nSELECT question, choices[answer] AS correct_answer FROM data\n\n-- Regex matching\nSELECT * FROM data WHERE regexp_matches(question, 'nutrition|diet')\n\n-- String functions\nSELECT regexp_replace(question, '\\n', '') AS cleaned FROM data\n```\n\n## Common Operations\n\n### 1. Explore Dataset Structure\n```bash\n# Get schema\npython scripts\u002Fsql_manager.py describe --dataset \"cais\u002Fmmlu\"\n\n# Get unique values in column\npython scripts\u002Fsql_manager.py unique --dataset \"cais\u002Fmmlu\" --column \"subject\"\n\n# Get value distribution\npython scripts\u002Fsql_manager.py histogram --dataset \"cais\u002Fmmlu\" --column \"subject\" --bins 20\n```\n\n### 2. Filter and Transform\n```bash\n# Complex filtering with SQL\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT subject, COUNT(*) as cnt FROM data GROUP BY subject HAVING cnt > 100\"\n\n# Using transform command\npython scripts\u002Fsql_manager.py transform \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --select \"subject, COUNT(*) as cnt\" \\\n  --group-by \"subject\" \\\n  --order-by \"cnt DESC\" \\\n  --limit 10\n```\n\n### 3. Create Subsets and Push to Hub\n```bash\n# Query and push to new dataset\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data WHERE subject='nutrition'\" \\\n  --push-to \"username\u002Fmmlu-nutrition-subset\" \\\n  --private\n\n# Transform and push\npython scripts\u002Fsql_manager.py transform \\\n  --dataset \"ibm\u002Fduorc\" \\\n  --config \"ParaphraseRC\" \\\n  --select \"question, answers\" \\\n  --where \"LENGTH(question) > 50\" \\\n  --push-to \"username\u002Fduorc-long-questions\"\n```\n\n### 4. Export to Local Files\n```bash\n# Export to Parquet\npython scripts\u002Fsql_manager.py export \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data WHERE subject='nutrition'\" \\\n  --output \"nutrition.parquet\" \\\n  --format parquet\n\n# Export to JSONL\npython scripts\u002Fsql_manager.py export \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data LIMIT 100\" \\\n  --output \"sample.jsonl\" \\\n  --format jsonl\n```\n\n### 5. Working with Dataset Configs\u002FSplits\n```bash\n# Specify config (subset)\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"ibm\u002Fduorc\" \\\n  --config \"ParaphraseRC\" \\\n  --sql \"SELECT * FROM data LIMIT 5\"\n\n# Specify split\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --split \"test\" \\\n  --sql \"SELECT COUNT(*) FROM data\"\n\n# Query all splits\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --split \"*\" \\\n  --sql \"SELECT * FROM data LIMIT 10\"\n```\n\n### 6. Raw SQL with Full Paths\nFor complex queries or joining datasets:\n```bash\npython scripts\u002Fsql_manager.py raw --sql \"\n  SELECT a.*, b.* \n  FROM 'hf:\u002F\u002Fdatasets\u002Fdataset1@~parquet\u002Fdefault\u002Ftrain\u002F*.parquet' a\n  JOIN 'hf:\u002F\u002Fdatasets\u002Fdataset2@~parquet\u002Fdefault\u002Ftrain\u002F*.parquet' b\n  ON a.id = b.id\n  LIMIT 100\n\"\n```\n\n## Python API Usage\n\n```python\nfrom sql_manager import HFDatasetSQL\n\nsql = HFDatasetSQL()\n\n# Query\nresults = sql.query(\"cais\u002Fmmlu\", \"SELECT * FROM data WHERE subject='nutrition' LIMIT 10\")\n\n# Get schema\nschema = sql.describe(\"cais\u002Fmmlu\")\n\n# Sample\nsamples = sql.sample(\"cais\u002Fmmlu\", n=5, seed=42)\n\n# Count\ncount = sql.count(\"cais\u002Fmmlu\", where=\"subject='nutrition'\")\n\n# Histogram\ndist = sql.histogram(\"cais\u002Fmmlu\", \"subject\")\n\n# Filter and transform\nresults = sql.filter_and_transform(\n    \"cais\u002Fmmlu\",\n    select=\"subject, COUNT(*) as cnt\",\n    group_by=\"subject\",\n    order_by=\"cnt DESC\",\n    limit=10\n)\n\n# Push to Hub\nurl = sql.push_to_hub(\n    \"cais\u002Fmmlu\",\n    \"username\u002Fnutrition-subset\",\n    sql=\"SELECT * FROM data WHERE subject='nutrition'\",\n    private=True\n)\n\n# Export locally\nsql.export_to_parquet(\"cais\u002Fmmlu\", \"output.parquet\", sql=\"SELECT * FROM data LIMIT 100\")\n\nsql.close()\n```\n\n## HF Path Format\n\nDuckDB uses the `hf:\u002F\u002F` protocol to access datasets:\n```\nhf:\u002F\u002Fdatasets\u002F{dataset_id}@{revision}\u002F{config}\u002F{split}\u002F*.parquet\n```\n\nExamples:\n- `hf:\u002F\u002Fdatasets\u002Fcais\u002Fmmlu@~parquet\u002Fdefault\u002Ftrain\u002F*.parquet`\n- `hf:\u002F\u002Fdatasets\u002Fibm\u002Fduorc@~parquet\u002FParaphraseRC\u002Ftest\u002F*.parquet`\n\nThe `@~parquet` revision provides auto-converted Parquet files for any dataset format.\n\n## Useful DuckDB SQL Functions\n\n```sql\n-- String functions\nLENGTH(column)                    -- String length\nregexp_replace(col, '\\n', '')     -- Regex replace\nregexp_matches(col, 'pattern')    -- Regex match\nLOWER(col), UPPER(col)           -- Case conversion\n\n-- Array functions  \nchoices[0]                        -- Array indexing (0-based)\narray_length(choices)             -- Array length\nunnest(choices)                   -- Expand array to rows\n\n-- Aggregations\nCOUNT(*), SUM(col), AVG(col)\nGROUP BY col HAVING condition\n\n-- Sampling\nUSING SAMPLE 10                   -- Random sample\nUSING SAMPLE 10 (RESERVOIR, 42)   -- Reproducible sample\n\n-- Window functions\nROW_NUMBER() OVER (PARTITION BY col ORDER BY col2)\n```\n\n---\n\n# Dataset Creation (dataset_manager.py)\n\n### Recommended Workflow\n\n**1. Discovery (Use HF MCP Server):**\n```python\n# Use HF MCP tools to find existing datasets\nsearch_datasets(\"conversational AI training\")\nget_dataset_details(\"username\u002Fdataset-name\")\n```\n\n**2. Creation (Use This Skill):**\n```bash\n# Initialize new dataset\npython scripts\u002Fdataset_manager.py init --repo_id \"your-username\u002Fdataset-name\" [--private]\n\n# Configure with detailed system prompt\npython scripts\u002Fdataset_manager.py config --repo_id \"your-username\u002Fdataset-name\" --system_prompt \"$(cat system_prompt.txt)\"\n```\n\n**3. Content Management (Use This Skill):**\n```bash\n# Quick setup with any template\npython scripts\u002Fdataset_manager.py quick_setup \\\n  --repo_id \"your-username\u002Fdataset-name\" \\\n  --template classification\n\n# Add data with template validation\npython scripts\u002Fdataset_manager.py add_rows \\\n  --repo_id \"your-username\u002Fdataset-name\" \\\n  --template qa \\\n  --rows_json \"$(cat your_qa_data.json)\"\n```\n\n### Template-Based Data Structures\n\n**1. Chat Template (`--template chat`)**\n```json\n{\n  \"messages\": [\n    {\"role\": \"user\", \"content\": \"Natural user request\"},\n    {\"role\": \"assistant\", \"content\": \"Response with tool usage\"},\n    {\"role\": \"tool\", \"content\": \"Tool response\", \"tool_call_id\": \"call_123\"}\n  ],\n  \"scenario\": \"Description of use case\",\n  \"complexity\": \"simple|intermediate|advanced\"\n}\n```\n\n**2. Classification Template (`--template classification`)**\n```json\n{\n  \"text\": \"Input text to be classified\",\n  \"label\": \"classification_label\",\n  \"confidence\": 0.95,\n  \"metadata\": {\"domain\": \"technology\", \"language\": \"en\"}\n}\n```\n\n**3. QA Template (`--template qa`)**\n```json\n{\n  \"question\": \"What is the question being asked?\",\n  \"answer\": \"The complete answer\",\n  \"context\": \"Additional context if needed\",\n  \"answer_type\": \"factual|explanatory|opinion\",\n  \"difficulty\": \"easy|medium|hard\"\n}\n```\n\n**4. Completion Template (`--template completion`)**\n```json\n{\n  \"prompt\": \"The beginning text or context\",\n  \"completion\": \"The expected continuation\",\n  \"domain\": \"code|creative|technical|conversational\",\n  \"style\": \"description of writing style\"\n}\n```\n\n**5. Tabular Template (`--template tabular`)**\n```json\n{\n  \"columns\": [\n    {\"name\": \"feature1\", \"type\": \"numeric\", \"description\": \"First feature\"},\n    {\"name\": \"target\", \"type\": \"categorical\", \"description\": \"Target variable\"}\n  ],\n  \"data\": [\n    {\"feature1\": 123, \"target\": \"class_a\"},\n    {\"feature1\": 456, \"target\": \"class_b\"}\n  ]\n}\n```\n\n### Advanced System Prompt Template\n\nFor high-quality training data generation:\n```text\nYou are an AI assistant expert at using MCP tools effectively.\n\n## MCP SERVER DEFINITIONS\n[Define available servers and tools]\n\n## TRAINING EXAMPLE STRUCTURE\n[Specify exact JSON schema for chat templating]\n\n## QUALITY GUIDELINES\n[Detail requirements for realistic scenarios, progressive complexity, proper tool usage]\n\n## EXAMPLE CATEGORIES\n[List development workflows, debugging scenarios, data management tasks]\n```\n\n### Example Categories & Templates\n\nThe skill includes diverse training examples beyond just MCP usage:\n\n**Available Example Sets:**\n- `training_examples.json` - MCP tool usage examples (debugging, project setup, database analysis)\n- `diverse_training_examples.json` - Broader scenarios including:\n  - **Educational Chat** - Explaining programming concepts, tutorials\n  - **Git Workflows** - Feature branches, version control guidance\n  - **Code Analysis** - Performance optimization, architecture review\n  - **Content Generation** - Professional writing, creative brainstorming\n  - **Codebase Navigation** - Legacy code exploration, systematic analysis\n  - **Conversational Support** - Problem-solving, technical discussions\n\n**Using Different Example Sets:**\n```bash\n# Add MCP-focused examples\npython scripts\u002Fdataset_manager.py add_rows --repo_id \"your-username\u002Fdataset-name\" \\\n  --rows_json \"$(cat examples\u002Ftraining_examples.json)\"\n\n# Add diverse conversational examples\npython scripts\u002Fdataset_manager.py add_rows --repo_id \"your-username\u002Fdataset-name\" \\\n  --rows_json \"$(cat examples\u002Fdiverse_training_examples.json)\"\n\n# Mix both for comprehensive training data\npython scripts\u002Fdataset_manager.py add_rows --repo_id \"your-username\u002Fdataset-name\" \\\n  --rows_json \"$(jq -s '.[0] + .[1]' examples\u002Ftraining_examples.json examples\u002Fdiverse_training_examples.json)\"\n```\n\n### Commands Reference\n\n**List Available Templates:**\n```bash\npython scripts\u002Fdataset_manager.py list_templates\n```\n\n**Quick Setup (Recommended):**\n```bash\npython scripts\u002Fdataset_manager.py quick_setup --repo_id \"your-username\u002Fdataset-name\" --template classification\n```\n\n**Manual Setup:**\n```bash\n# Initialize repository\npython scripts\u002Fdataset_manager.py init --repo_id \"your-username\u002Fdataset-name\" [--private]\n\n# Configure with system prompt\npython scripts\u002Fdataset_manager.py config --repo_id \"your-username\u002Fdataset-name\" --system_prompt \"Your prompt here\"\n\n# Add data with validation\npython scripts\u002Fdataset_manager.py add_rows \\\n  --repo_id \"your-username\u002Fdataset-name\" \\\n  --template qa \\\n  --rows_json '[{\"question\": \"What is AI?\", \"answer\": \"Artificial Intelligence...\"}]'\n```\n\n**View Dataset Statistics:**\n```bash\npython scripts\u002Fdataset_manager.py stats --repo_id \"your-username\u002Fdataset-name\"\n```\n\n### Error Handling\n- **Repository exists**: Script will notify and continue with configuration\n- **Invalid JSON**: Clear error message with parsing details\n- **Network issues**: Automatic retry for transient failures\n- **Token permissions**: Validation before operations begin\n\n---\n\n# Combined Workflow Examples\n\n## Example 1: Create Training Subset from Existing Dataset\n```bash\n# 1. Explore the source dataset\npython scripts\u002Fsql_manager.py describe --dataset \"cais\u002Fmmlu\"\npython scripts\u002Fsql_manager.py histogram --dataset \"cais\u002Fmmlu\" --column \"subject\"\n\n# 2. Query and create subset\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data WHERE subject IN ('nutrition', 'anatomy', 'clinical_knowledge')\" \\\n  --push-to \"username\u002Fmmlu-medical-subset\" \\\n  --private\n```\n\n## Example 2: Transform and Reshape Data\n```bash\n# Transform MMLU to QA format with correct answers extracted\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT question, choices[answer] as correct_answer, subject FROM data\" \\\n  --push-to \"username\u002Fmmlu-qa-format\"\n```\n\n## Example 3: Merge Multiple Dataset Splits\n```bash\n# Export multiple splits and combine\npython scripts\u002Fsql_manager.py export \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --split \"*\" \\\n  --output \"mmlu_all.parquet\"\n```\n\n## Example 4: Quality Filtering\n```bash\n# Filter for high-quality examples\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"squad\" \\\n  --sql \"SELECT * FROM data WHERE LENGTH(context) > 500 AND LENGTH(question) > 20\" \\\n  --push-to \"username\u002Fsquad-filtered\"\n```\n\n## Example 5: Create Custom Training Dataset\n```bash\n# 1. Query source data\npython scripts\u002Fsql_manager.py export \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT question, subject FROM data WHERE subject='nutrition'\" \\\n  --output \"nutrition_source.jsonl\" \\\n  --format jsonl\n\n# 2. Process with your pipeline (add answers, format, etc.)\n\n# 3. Push processed data\npython scripts\u002Fdataset_manager.py init --repo_id \"username\u002Fnutrition-training\"\npython scripts\u002Fdataset_manager.py add_rows \\\n  --repo_id \"username\u002Fnutrition-training\" \\\n  --template qa \\\n  --rows_json \"$(cat processed_data.json)\"\n```\n","# Overview\nThis 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.\n\n## Integration with HF MCP Server\n- **Use HF MCP Server for**: Dataset discovery, search, and metadata retrieval\n- **Use This Skill for**: Dataset creation, content editing, SQL queries, data transformation, and structured data formatting\n\n# Version\n2.1.0\n\n# Dependencies\n- huggingface_hub\n- duckdb (for SQL queries)\n- datasets (for pushing query results to Hub)\n- json (built-in)\n- time (built-in)\n\n# Core Capabilities\n\n## 1. Dataset Lifecycle Management\n- **Initialize**: Create new dataset repositories with proper structure\n- **Configure**: Store detailed configuration including system prompts and metadata\n- **Stream Updates**: Add rows efficiently without downloading entire datasets\n\n## 2. SQL-Based Dataset Querying (NEW)\nQuery any Hugging Face dataset using DuckDB SQL via `scripts\u002Fsql_manager.py`:\n- **Direct Queries**: Run SQL on datasets using the `hf:\u002F\u002F` protocol\n- **Schema Discovery**: Describe dataset structure and column types\n- **Data Sampling**: Get random samples for exploration\n- **Aggregations**: Count, histogram, unique values analysis\n- **Transformations**: Filter, join, reshape data with SQL\n- **Export & Push**: Save results locally or push to new Hub repos\n\n## 3. Multi-Format Dataset Support\nSupports diverse dataset types through template system:\n- **Chat\u002FConversational**: Chat templating, multi-turn dialogues, tool usage examples\n- **Text Classification**: Sentiment analysis, intent detection, topic classification\n- **Question-Answering**: Reading comprehension, factual QA, knowledge bases\n- **Text Completion**: Language modeling, code completion, creative writing\n- **Tabular Data**: Structured data for regression\u002Fclassification tasks\n- **Custom Formats**: Flexible schema definition for specialized needs\n\n## 4. Quality Assurance Features\n- **JSON Validation**: Ensures data integrity during uploads\n- **Batch Processing**: Efficient handling of large datasets\n- **Error Recovery**: Graceful handling of upload failures and conflicts\n\n# Usage Instructions\n\nThe skill includes two Python scripts:\n- `scripts\u002Fdataset_manager.py` - Dataset creation and management\n- `scripts\u002Fsql_manager.py` - SQL-based dataset querying and transformation\n\n### Prerequisites\n- `huggingface_hub` library: `uv add huggingface_hub`\n- `duckdb` library (for SQL): `uv add duckdb`\n- `datasets` library (for pushing): `uv add datasets`\n- `HF_TOKEN` environment variable must be set with a Write-access token\n- Activate virtual environment: `source .venv\u002Fbin\u002Factivate`\n\n---\n\n# SQL Dataset Querying (sql_manager.py)\n\nQuery, transform, and push Hugging Face datasets using DuckDB SQL. The `hf:\u002F\u002F` protocol provides direct access to any public dataset (or private with token).\n\n## Quick Start\n\n```bash\n# Query a dataset\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data WHERE subject='nutrition' LIMIT 10\"\n\n# Get dataset schema\npython scripts\u002Fsql_manager.py describe --dataset \"cais\u002Fmmlu\"\n\n# Sample random rows\npython scripts\u002Fsql_manager.py sample --dataset \"cais\u002Fmmlu\" --n 5\n\n# Count rows with filter\npython scripts\u002Fsql_manager.py count --dataset \"cais\u002Fmmlu\" --where \"subject='nutrition'\"\n```\n\n## SQL Query Syntax\n\nUse `data` as the table name in your SQL - it gets replaced with the actual `hf:\u002F\u002F` path:\n\n```sql\n-- Basic select\nSELECT * FROM data LIMIT 10\n\n-- Filtering\nSELECT * FROM data WHERE subject='nutrition'\n\n-- Aggregations\nSELECT subject, COUNT(*) as cnt FROM data GROUP BY subject ORDER BY cnt DESC\n\n-- Column selection and transformation\nSELECT question, choices[answer] AS correct_answer FROM data\n\n-- Regex matching\nSELECT * FROM data WHERE regexp_matches(question, 'nutrition|diet')\n\n-- String functions\nSELECT regexp_replace(question, '\\n', '') AS cleaned FROM data\n```\n\n## Common Operations\n\n### 1. Explore Dataset Structure\n```bash\n# Get schema\npython scripts\u002Fsql_manager.py describe --dataset \"cais\u002Fmmlu\"\n\n# Get unique values in column\npython scripts\u002Fsql_manager.py unique --dataset \"cais\u002Fmmlu\" --column \"subject\"\n\n# Get value distribution\npython scripts\u002Fsql_manager.py histogram --dataset \"cais\u002Fmmlu\" --column \"subject\" --bins 20\n```\n\n### 2. Filter and Transform\n```bash\n# Complex filtering with SQL\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT subject, COUNT(*) as cnt FROM data GROUP BY subject HAVING cnt > 100\"\n\n# Using transform command\npython scripts\u002Fsql_manager.py transform \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --select \"subject, COUNT(*) as cnt\" \\\n  --group-by \"subject\" \\\n  --order-by \"cnt DESC\" \\\n  --limit 10\n```\n\n### 3. Create Subsets and Push to Hub\n```bash\n# Query and push to new dataset\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data WHERE subject='nutrition'\" \\\n  --push-to \"username\u002Fmmlu-nutrition-subset\" \\\n  --private\n\n# Transform and push\npython scripts\u002Fsql_manager.py transform \\\n  --dataset \"ibm\u002Fduorc\" \\\n  --config \"ParaphraseRC\" \\\n  --select \"question, answers\" \\\n  --where \"LENGTH(question) > 50\" \\\n  --push-to \"username\u002Fduorc-long-questions\"\n```\n\n### 4. Export to Local Files\n```bash\n# Export to Parquet\npython scripts\u002Fsql_manager.py export \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data WHERE subject='nutrition'\" \\\n  --output \"nutrition.parquet\" \\\n  --format parquet\n\n# Export to JSONL\npython scripts\u002Fsql_manager.py export \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data LIMIT 100\" \\\n  --output \"sample.jsonl\" \\\n  --format jsonl\n```\n\n### 5. Working with Dataset Configs\u002FSplits\n```bash\n# Specify config (subset)\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"ibm\u002Fduorc\" \\\n  --config \"ParaphraseRC\" \\\n  --sql \"SELECT * FROM data LIMIT 5\"\n\n# Specify split\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --split \"test\" \\\n  --sql \"SELECT COUNT(*) FROM data\"\n\n# Query all splits\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --split \"*\" \\\n  --sql \"SELECT * FROM data LIMIT 10\"\n```\n\n### 6. Raw SQL with Full Paths\nFor complex queries or joining datasets:\n```bash\npython scripts\u002Fsql_manager.py raw --sql \"\n  SELECT a.*, b.* \n  FROM 'hf:\u002F\u002Fdatasets\u002Fdataset1@~parquet\u002Fdefault\u002Ftrain\u002F*.parquet' a\n  JOIN 'hf:\u002F\u002Fdatasets\u002Fdataset2@~parquet\u002Fdefault\u002Ftrain\u002F*.parquet' b\n  ON a.id = b.id\n  LIMIT 100\n\"\n```\n\n## Python API Usage\n\n```python\nfrom sql_manager import HFDatasetSQL\n\nsql = HFDatasetSQL()\n\n# Query\nresults = sql.query(\"cais\u002Fmmlu\", \"SELECT * FROM data WHERE subject='nutrition' LIMIT 10\")\n\n# Get schema\nschema = sql.describe(\"cais\u002Fmmlu\")\n\n# Sample\nsamples = sql.sample(\"cais\u002Fmmlu\", n=5, seed=42)\n\n# Count\ncount = sql.count(\"cais\u002Fmmlu\", where=\"subject='nutrition'\")\n\n# Histogram\ndist = sql.histogram(\"cais\u002Fmmlu\", \"subject\")\n\n# Filter and transform\nresults = sql.filter_and_transform(\n    \"cais\u002Fmmlu\",\n    select=\"subject, COUNT(*) as cnt\",\n    group_by=\"subject\",\n    order_by=\"cnt DESC\",\n    limit=10\n)\n\n# Push to Hub\nurl = sql.push_to_hub(\n    \"cais\u002Fmmlu\",\n    \"username\u002Fnutrition-subset\",\n    sql=\"SELECT * FROM data WHERE subject='nutrition'\",\n    private=True\n)\n\n# Export locally\nsql.export_to_parquet(\"cais\u002Fmmlu\", \"output.parquet\", sql=\"SELECT * FROM data LIMIT 100\")\n\nsql.close()\n```\n\n## HF Path Format\n\nDuckDB uses the `hf:\u002F\u002F` protocol to access datasets:\n```\nhf:\u002F\u002Fdatasets\u002F{dataset_id}@{revision}\u002F{config}\u002F{split}\u002F*.parquet\n```\n\nExamples:\n- `hf:\u002F\u002Fdatasets\u002Fcais\u002Fmmlu@~parquet\u002Fdefault\u002Ftrain\u002F*.parquet`\n- `hf:\u002F\u002Fdatasets\u002Fibm\u002Fduorc@~parquet\u002FParaphraseRC\u002Ftest\u002F*.parquet`\n\nThe `@~parquet` revision provides auto-converted Parquet files for any dataset format.\n\n## Useful DuckDB SQL Functions\n\n```sql\n-- String functions\nLENGTH(column)                    -- String length\nregexp_replace(col, '\\n', '')     -- Regex replace\nregexp_matches(col, 'pattern')    -- Regex match\nLOWER(col), UPPER(col)           -- Case conversion\n\n-- Array functions  \nchoices[0]                        -- Array indexing (0-based)\narray_length(choices)             -- Array length\nunnest(choices)                   -- Expand array to rows\n\n-- Aggregations\nCOUNT(*), SUM(col), AVG(col)\nGROUP BY col HAVING condition\n\n-- Sampling\nUSING SAMPLE 10                   -- Random sample\nUSING SAMPLE 10 (RESERVOIR, 42)   -- Reproducible sample\n\n-- Window functions\nROW_NUMBER() OVER (PARTITION BY col ORDER BY col2)\n```\n\n---\n\n# Dataset Creation (dataset_manager.py)\n\n### Recommended Workflow\n\n**1. Discovery (Use HF MCP Server):**\n```python\n# Use HF MCP tools to find existing datasets\nsearch_datasets(\"conversational AI training\")\nget_dataset_details(\"username\u002Fdataset-name\")\n```\n\n**2. Creation (Use This Skill):**\n```bash\n# Initialize new dataset\npython scripts\u002Fdataset_manager.py init --repo_id \"your-username\u002Fdataset-name\" [--private]\n\n# Configure with detailed system prompt\npython scripts\u002Fdataset_manager.py config --repo_id \"your-username\u002Fdataset-name\" --system_prompt \"$(cat system_prompt.txt)\"\n```\n\n**3. Content Management (Use This Skill):**\n```bash\n# Quick setup with any template\npython scripts\u002Fdataset_manager.py quick_setup \\\n  --repo_id \"your-username\u002Fdataset-name\" \\\n  --template classification\n\n# Add data with template validation\npython scripts\u002Fdataset_manager.py add_rows \\\n  --repo_id \"your-username\u002Fdataset-name\" \\\n  --template qa \\\n  --rows_json \"$(cat your_qa_data.json)\"\n```\n\n### Template-Based Data Structures\n\n**1. Chat Template (`--template chat`)**\n```json\n{\n  \"messages\": [\n    {\"role\": \"user\", \"content\": \"Natural user request\"},\n    {\"role\": \"assistant\", \"content\": \"Response with tool usage\"},\n    {\"role\": \"tool\", \"content\": \"Tool response\", \"tool_call_id\": \"call_123\"}\n  ],\n  \"scenario\": \"Description of use case\",\n  \"complexity\": \"simple|intermediate|advanced\"\n}\n```\n\n**2. Classification Template (`--template classification`)**\n```json\n{\n  \"text\": \"Input text to be classified\",\n  \"label\": \"classification_label\",\n  \"confidence\": 0.95,\n  \"metadata\": {\"domain\": \"technology\", \"language\": \"en\"}\n}\n```\n\n**3. QA Template (`--template qa`)**\n```json\n{\n  \"question\": \"What is the question being asked?\",\n  \"answer\": \"The complete answer\",\n  \"context\": \"Additional context if needed\",\n  \"answer_type\": \"factual|explanatory|opinion\",\n  \"difficulty\": \"easy|medium|hard\"\n}\n```\n\n**4. Completion Template (`--template completion`)**\n```json\n{\n  \"prompt\": \"The beginning text or context\",\n  \"completion\": \"The expected continuation\",\n  \"domain\": \"code|creative|technical|conversational\",\n  \"style\": \"description of writing style\"\n}\n```\n\n**5. Tabular Template (`--template tabular`)**\n```json\n{\n  \"columns\": [\n    {\"name\": \"feature1\", \"type\": \"numeric\", \"description\": \"First feature\"},\n    {\"name\": \"target\", \"type\": \"categorical\", \"description\": \"Target variable\"}\n  ],\n  \"data\": [\n    {\"feature1\": 123, \"target\": \"class_a\"},\n    {\"feature1\": 456, \"target\": \"class_b\"}\n  ]\n}\n```\n\n### Advanced System Prompt Template\n\nFor high-quality training data generation:\n```text\nYou are an AI assistant expert at using MCP tools effectively.\n\n## MCP SERVER DEFINITIONS\n[Define available servers and tools]\n\n## TRAINING EXAMPLE STRUCTURE\n[Specify exact JSON schema for chat templating]\n\n## QUALITY GUIDELINES\n[Detail requirements for realistic scenarios, progressive complexity, proper tool usage]\n\n## EXAMPLE CATEGORIES\n[List development workflows, debugging scenarios, data management tasks]\n```\n\n### Example Categories & Templates\n\nThe skill includes diverse training examples beyond just MCP usage:\n\n**Available Example Sets:**\n- `training_examples.json` - MCP tool usage examples (debugging, project setup, database analysis)\n- `diverse_training_examples.json` - Broader scenarios including:\n  - **Educational Chat** - Explaining programming concepts, tutorials\n  - **Git Workflows** - Feature branches, version control guidance\n  - **Code Analysis** - Performance optimization, architecture review\n  - **Content Generation** - Professional writing, creative brainstorming\n  - **Codebase Navigation** - Legacy code exploration, systematic analysis\n  - **Conversational Support** - Problem-solving, technical discussions\n\n**Using Different Example Sets:**\n```bash\n# Add MCP-focused examples\npython scripts\u002Fdataset_manager.py add_rows --repo_id \"your-username\u002Fdataset-name\" \\\n  --rows_json \"$(cat examples\u002Ftraining_examples.json)\"\n\n# Add diverse conversational examples\npython scripts\u002Fdataset_manager.py add_rows --repo_id \"your-username\u002Fdataset-name\" \\\n  --rows_json \"$(cat examples\u002Fdiverse_training_examples.json)\"\n\n# Mix both for comprehensive training data\npython scripts\u002Fdataset_manager.py add_rows --repo_id \"your-username\u002Fdataset-name\" \\\n  --rows_json \"$(jq -s '.[0] + .[1]' examples\u002Ftraining_examples.json examples\u002Fdiverse_training_examples.json)\"\n```\n\n### Commands Reference\n\n**List Available Templates:**\n```bash\npython scripts\u002Fdataset_manager.py list_templates\n```\n\n**Quick Setup (Recommended):**\n```bash\npython scripts\u002Fdataset_manager.py quick_setup --repo_id \"your-username\u002Fdataset-name\" --template classification\n```\n\n**Manual Setup:**\n```bash\n# Initialize repository\npython scripts\u002Fdataset_manager.py init --repo_id \"your-username\u002Fdataset-name\" [--private]\n\n# Configure with system prompt\npython scripts\u002Fdataset_manager.py config --repo_id \"your-username\u002Fdataset-name\" --system_prompt \"Your prompt here\"\n\n# Add data with validation\npython scripts\u002Fdataset_manager.py add_rows \\\n  --repo_id \"your-username\u002Fdataset-name\" \\\n  --template qa \\\n  --rows_json '[{\"question\": \"What is AI?\", \"answer\": \"Artificial Intelligence...\"}]'\n```\n\n**View Dataset Statistics:**\n```bash\npython scripts\u002Fdataset_manager.py stats --repo_id \"your-username\u002Fdataset-name\"\n```\n\n### Error Handling\n- **Repository exists**: Script will notify and continue with configuration\n- **Invalid JSON**: Clear error message with parsing details\n- **Network issues**: Automatic retry for transient failures\n- **Token permissions**: Validation before operations begin\n\n---\n\n# Combined Workflow Examples\n\n## Example 1: Create Training Subset from Existing Dataset\n```bash\n# 1. Explore the source dataset\npython scripts\u002Fsql_manager.py describe --dataset \"cais\u002Fmmlu\"\npython scripts\u002Fsql_manager.py histogram --dataset \"cais\u002Fmmlu\" --column \"subject\"\n\n# 2. Query and create subset\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT * FROM data WHERE subject IN ('nutrition', 'anatomy', 'clinical_knowledge')\" \\\n  --push-to \"username\u002Fmmlu-medical-subset\" \\\n  --private\n```\n\n## Example 2: Transform and Reshape Data\n```bash\n# Transform MMLU to QA format with correct answers extracted\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT question, choices[answer] as correct_answer, subject FROM data\" \\\n  --push-to \"username\u002Fmmlu-qa-format\"\n```\n\n## Example 3: Merge Multiple Dataset Splits\n```bash\n# Export multiple splits and combine\npython scripts\u002Fsql_manager.py export \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --split \"*\" \\\n  --output \"mmlu_all.parquet\"\n```\n\n## Example 4: Quality Filtering\n```bash\n# Filter for high-quality examples\npython scripts\u002Fsql_manager.py query \\\n  --dataset \"squad\" \\\n  --sql \"SELECT * FROM data WHERE LENGTH(context) > 500 AND LENGTH(question) > 20\" \\\n  --push-to \"username\u002Fsquad-filtered\"\n```\n\n## Example 5: Create Custom Training Dataset\n```bash\n# 1. Query source data\npython scripts\u002Fsql_manager.py export \\\n  --dataset \"cais\u002Fmmlu\" \\\n  --sql \"SELECT question, subject FROM data WHERE subject='nutrition'\" \\\n  --output \"nutrition_source.jsonl\" \\\n  --format jsonl\n\n# 2. Process with your pipeline (add answers, format, etc.)\n\n# 3. 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Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter\u002Fexit of components, animate list reorder, implement directional (forward\u002Fback) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.","https:\u002F\u002Fgithub.com\u002Faicreator350\u002FaurionOFFICIELLE\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-react-view-transitions\u002FSKILL.md",[],[],{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":18},"01M74CA364D4EVDXZZQRVK1Z73","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002Faicreator350\u002FaurionOFFICIELLE\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-react-best-practices\u002FSKILL.md",[],[],{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":18},"01M74C9YQZX1GVS18V0AYCYPZE","vercel-composition-patterns","React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.","https:\u002F\u002Fgithub.com\u002Faicreator350\u002FaurionOFFICIELLE\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-composition-patterns\u002FSKILL.md",[],[],{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":18},"01M74C9SEYD6F764Q1JACZX3A6","vercel-cli-with-tokens","Deploy and manage projects on Vercel using token-based authentication. Use when working with Vercel CLI using access tokens rather than interactive login — e.g. \"deploy to vercel\", \"set up vercel\", \"add environment variables to vercel\".","https:\u002F\u002Fgithub.com\u002Faicreator350\u002FaurionOFFICIELLE\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-cli-with-tokens\u002FSKILL.md",[],[]]