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

Meta-Analysis Funnel Plot Generator

This skill generates a standardized meta-analysis result section based on a funnel plot image, statistical data, and a title. It orchestrates LLM generation for descriptions and tables, then uses a Python script to assemble the final report.

When to Use

• Use this skill when you need generates a meta-analysis results section description for funnel plots, including statistical tables (egger's, begg's, trim & fill) and figure legends. supports english and chinese outputs. use when user provides a funnel plot image and statistics and wants a formatted report 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/main.py is the most direct path to complete the request.
• Use this skill when you need the meta-results-funnel-plot-generator package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: Generates a Meta-analysis results section description for funnel plots, including statistical tables (Egger's, Begg's, Trim & Fill) and figure legends. Supports English and Chinese outputs. Use when user provides a funnel plot image and statistics and wants a formatted report.
• Packaged executable path(s): scripts/main.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

See ## Usage above for related details.

cd "20260316/scientific-skills/Academic Writing/meta-results-funnel-plot-generator"
python -m py_compile scripts/main.py
python scripts/main.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/main.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/main.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.

Usage

Trigger this skill when the user provides:

1. Funnel Plot Image: The visual plot.
2. Statistics: Text containing statistical data (Egger's test, Begg's test, Trim & Fill).
3. Title/Outcome: Context for the analysis.
4. Language: "Chinese" or "English".

Workflow

1. Generate Description: Use LLM to describe the funnel plot (symmetry, outliers) based on the image and stats.
2. Generate Tables: Use LLM to format the provided statistics into three specific Markdown tables:
   • Egger's test (Bias assessment)
   • Begg's test
   • Trim and Fill method
3. Assemble Report: Run scripts/main.py to:
   • Clean LLM outputs (remove markdown fences).

• Insert figure reference "(Figure 3)" or "(Figure 3)" into the description. * Combine Description, Image Placeholder, Figure Legend, and Tables into the final output.

Quality Rules

• Language: Output must match the requested language (Chinese/English).
• Structure: The final output must strictly follow the order: Description -> Figure -> Legend -> Tables.
• Formatting: Tables must be standard Markdown.

Reference

See prompts.md for the LLM prompts used in this workflow.

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If execution fails, report the failure point, summarize what can still be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of meta-results-funnel-plot-generator and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

meta-results-funnel-plot-generator only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.