Meta Baseline Generator

Source: https://github.com/aipoch/medical-research-skills

Meta-Analysis Baseline Generator

This skill generates a standardized "Baseline Characteristics" section for meta-analysis papers, including a descriptive text summary and a formatted Markdown table.

When to Use

• Use this skill when you need generates a meta-analysis baseline characteristics section (text + table) from raw data. supports chinese and english. use when the user provides baseline data and wants a formatted results section in a reproducible workflow.
• Use this skill when a academic writing task needs a packaged method instead of ad-hoc freeform output.
• Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
• Use this skill when scripts/text_processor.py is the most direct path to complete the request.
• Use this skill when you need the meta-baseline-generator package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: Generates a meta-analysis baseline characteristics section (text + table) from raw data. Supports Chinese and English. Use when the user provides baseline data and wants a formatted results section.
• Packaged executable path(s): scripts/text_processor.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260316/scientific-skills/Academic Writing/meta-baseline-generator"
python -m py_compile scripts/text_processor.py
python scripts/text_processor.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/text_processor.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/text_processor.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Workflow

1. Gather Inputs: Ensure you have the following from the user:

   • title: The title of the meta-analysis.
   • baseline_information: The raw baseline data (JSON, text, etc.).
   • language: The target output language ("Chinese" or "English").

2. Generate Text Description (LLM):

   • Use the "Text Description Generation" prompt in references/prompts.md.
   • Input: title, baseline_information, language.
   • Output: A paragraph describing the study characteristics.

3. Generate Markdown Table (LLM):

   • Use the "Markdown Table Generation" prompt in references/prompts.md.
   • Input: baseline_information, language.
   • Output: A Markdown table wrapped in curly braces (e.g., { | Table | }).

4. Process and Combine (Script):

   • Run scripts/text_processor.py to format the final output.
   • The script performs the following deterministic operations:
     • Inserts (Table 1) before the last punctuation of the text description.
     • Cleans markdown code fences from the table output.
     • Adds the standard table title and headers.
   • Execution:

     import sys
     sys.path.append('scripts')
     from text_processor import process_content
     
     final_result = process_content(
         text_description=step2_output, 
         raw_table=step3_output, 
         language=language
     )
     print(final_result)

5. Output: Present the final_result to the user.