Experimental Log Generator for Scientific Documentation

Abstract

An intelligent experimental log generator that creates structured documentation from experimental protocols. Supports multiple output formats including Markdown, JSON, and structured reports.

Cleaned Submission Note

This revision replaces a raw JSON display with readable Markdown. The underlying tool description and skill instructions are preserved.

Tool Summary

从实验数据自动生成规范的实验记录文档 Experimental Log Generator 1.0.0

Input Schema

The original structured input schema is retained conceptually. Use the SKILL section below for executable instructions.

SKILL

Experimental Log Generator

Name

Experimental Log Generator

Description

Automatically generates standardized experimental log documents from experimental data. Parses experimental data in multiple formats (CSV, JSON, TXT), extracts key information, and generates Markdown formatted experimental logs that meet scientific standards.

Input

• Experimental data file: Experimental data in CSV, JSON, or TXT format
• Experiment description: Text description of experiment purpose, hypothesis, expected results
• Optional parameters:
  • Experiment date (experiment_date)
  • Experimenter (experimenter)
  • Laboratory/institution (institution)

Steps

Step 1: Read Experimental Data

• CSV format support: Use pandas library to parse tabular data
• JSON format support: Parse nested data structures
• TXT format support: Parse line-by-line key-value pairs or tab-separated data

Step 2: Parse Data Types and Formats

• Identify numeric data (int, float)
• Identify categorical data (string, boolean)
• Detect time series data
• Identify missing values and outliers

Step 3: Generate Standard Experimental Log Sections

Generate the following standard sections:

3.1 Experiment Purpose

• Clearly describe the scientific question
• State research hypothesis
• Explain expected results

3.2 Materials and Methods

• List of experimental materials
• Detailed description of experimental steps
• Equipment information
• Parameter settings

3.3 Data Source

• Data file information
• Data collection time
• Data quality description

3.4 Results Summary

• Basic data statistics (mean, standard deviation, sample count, etc.)
• Text description of key findings
• Data visualization description

3.5 Analysis Methods

• Statistical methods used
• Data processing steps
• Software tools

3.6 Conclusions and Discussion

• Main conclusions
• Significance of results
• Limitations
• Suggestions for follow-up work

Step 4: Output Markdown Formatted Experimental Log

• Use standard Markdown syntax
• Include tables (data summary)
• Include code blocks (if analysis code exists)
• Auto-generate table of contents

Output

Formatted Markdown experimental log document containing all standard sections, output to specified file or stdout.

Tools

• Python standard library (json, csv, re)
• pandas (data analysis)
• datetime (time processing)

Usage Examples

Agent Call Format

Use skill: Experimental Log Generator
Input file: experiment_data.csv
Experiment description: "Verify the inhibitory activity of compound A on target protein"
Experiment date: 2024-01-15
Experimenter: Zhang San

CLI Call

python execute.py --input test_inputs/experiment_data.csv \
                  --description "Verify the inhibitory activity of compound A on target protein" \
                  --output experiment_log.md

Integrity Note

This is a formatting cleanup revision. It does not introduce a new scientific claim.