data-to-text

name	data-to-text
description	Convert structured data (CSV/Parquet/Excel) into semantic, readable text documents or experimental reports for LLM fine-tuning. Use when the user wants to transform tabular data into natural language narratives, generate training corpus, or create AI-Ready text documents from datasets. Output is Markdown format ready for further processing.

Data-to-Text Transformation

Transform structured tabular data into semantic, LLM-friendly text documents.

Core Directives

1. Semantic Inference First. Before converting, understand what each column represents. Infer meaning from column names, values, and context.
2. Natural Language Templates. Design text templates that connect data points into coherent narratives, not just key-value dumps.
3. Handle Missing Gracefully. Replace NaN with contextually appropriate placeholders (e.g., "未记录", "N/A", "unknown").

Workflow

Phase 1: Profile & Semantic Inference

1. Run data_profile(file_path) to understand the schema and sample data.
2. For each column, infer its semantic meaning:
   • temp_c → "Temperature in Celsius"
   • exp_id → "Experiment Identifier"
   • result_mg → "Result measurement in milligrams"
3. Identify the document type: Is this an experimental log? A survey response? A time-series record?

Phase 2: Template Design

Design a natural language template for a single row. The template should:

• Use complete sentences with proper grammar
• Connect related fields logically
• Handle optional/missing fields gracefully
• Maintain consistent tone and style

Example Template:

## 实验 {exp_id}

**日期**: {date}
**操作员**: {operator}

在温度 {temp_c}°C 条件下，进行了 {reaction_type} 反应。
反应持续 {duration_min} 分钟，最终产率为 {yield_pct}%。

**备注**: {notes}

Phase 3: Script Generation

1. Read the template: read_file("skills/data-to-text/scripts/template_to_text.py")
2. Modify the ROW_TEMPLATE string with your designed template
3. Update the column mapping in the script
4. Run via python_exec

Phase 4: Execute & Verify

1. Execute the script using python_exec
2. If errors occur, analyze the traceback and fix
3. Verify the output .md file exists and contains properly formatted text

Phase 5: Quality Check

Review the generated document:

• Are all records converted?
• Is the language natural and readable?
• Are missing values handled appropriately?
• Is the format consistent?

Output Format

Default output: {source_filename}_corpus.md

Each row becomes a section in the Markdown document, separated by horizontal rules or headers.

Next Steps (Optional)

The generated Markdown document is AI-Ready and can be used for various purposes:

• RAG/Search: Index the documents for retrieval-augmented generation
• Knowledge Base: Build domain-specific knowledge repositories
• Content Analysis: Perform NLP analysis on the structured narratives
• LLM Fine-tuning: Convert to SFT training data (see below)

For LLM Fine-tuning

If you want to convert the AI-Ready documents into supervised fine-tuning (SFT) datasets:

→ Use the sft-dataset skill to:

• Upload documents to Easy Dataset
• Generate question-answer pairs using AI
• Export in Alpaca or ShareGPT format

This is an optional workflow step—only proceed if your goal is to create training data for fine-tuning LLMs.

Complete Example

Input: experiments.csv (100 rows of experimental records)

1. data_profile("experiments.csv")
   → Understand schema: exp_id, date, temp_c, reaction_type, yield_pct, notes

2. Design template:
   ## 实验 {exp_id}
   日期: {date}
   在 {temp_c}°C 下进行 {reaction_type} 反应...

3. python_exec(template_script)
   → Generate experiments_corpus.md

4. Verify output:
   - 100 experiment reports
   - Natural language format
   - Ready for SFT processing

Output: experiments_corpus.md (AI-Ready document)