meta-results-forest-plot-analyzer

name	meta-results-forest-plot-analyzer
description	Analyzes forest plots for meta-analysis, generating detailed descriptions and formatting figure legends in Chinese or English. Use when the user wants to interpret a forest plot image, describe its statistical significance (heterogeneity, p-value), and format the output with s...
license	MIT
author	AIPOCH

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

When to Use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.

Key Features

• Scope-focused workflow aligned to: Analyzes forest plots for meta-analysis, generating detailed descriptions and formatting figure legends in Chinese or English. Use when the user wants to interpret a forest plot image, describe its statistical significance (heterogeneity, p-value), and format the output with specific figure legends.
• Packaged executable path(s): scripts/format_result.py plus 1 additional script(s).
• 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-forest-plot-analyzer"
python -m py_compile scripts/format_result.py
python scripts/format_result.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/format_result.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/format_result.py with additional helper scripts under scripts/.
• 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.

Validation Shortcut

Run this minimal command first to verify the supported execution path:

python scripts/validate_skill.py --help

Usage

1. Analyze Image: The skill first uses a Vision LLM to describe the forest plot.
2. Format Output: The skill then runs a script to insert citation markers and append figure legends.

Workflow

1. Image Analysis (Vision LLM)

The model analyzes the provided forest plot image along with optional metadata (title, statistics, outcome name).

Prompt Guidelines:

• Describe the forest plot in detail (>300 words).
• Include heterogeneity (I²), P-value, and effect sizes.
• Mention the number of studies and sample sizes if visible.
• Conclude on the statistical significance.
• Language: Strictly follow the requested language (Chinese or English).

2. Output Formatting (Script)

Run scripts/format_result.py to finalize the text.

Formatting Rules:

• Citation: Inserts (Figure 2) before the last punctuation mark of the description.
• Header: Adds **Forest Plot** (English) .
• Footer: Appends a placeholder for the image and the figure legend:
  • English: **Figure 2 Forest plot of the pooled effect size**

Examples

User Input:

"Analyze this forest plot. Title: 'Effect of X on Y'. Statistics: I2=50%. Language: English."

Process:

1. LLM generates description: "... The heterogeneity was moderate (I²=50%). .. The results were significant."
2. Script formats it:

   Forest Plot

   ... The results were significant(Figure 2).

   {insert your image here}

   Figure 2 Forest plot of the pooled effect size

When Not to Use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Required Inputs

• A clearly specified task goal aligned with the documented scope.
• All required files, identifiers, parameters, or environment variables before execution.
• Any domain constraints, formatting requirements, and expected output destination if applicable.

Output Contract

• Return a structured deliverable that is directly usable without reformatting.
• If a file is produced, prefer a deterministic output name such as meta_results_forest_plot_analyzer_result.md unless the skill documentation defines a better convention.
• Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.

Validation and Safety Rules

• Validate required inputs before execution and stop early when mandatory fields or files are missing.
• Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
• Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
• Keep the output safe, reproducible, and within the documented scope at all times.

Failure Handling

• If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
• If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
• If partial output is returned, label it clearly and identify which checks could not be completed.

Input Validation

This skill accepts requests that match the documented purpose of meta-results-forest-plot-analyzer 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-forest-plot-analyzer only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Quick Validation

Run this minimal verification path before full execution when possible:

python scripts/format_result.py --help

Expected output format:

Result file: meta_results_forest_plot_analyzer_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any

Deterministic Output Rules

• Use the same section order for every supported request of this skill.
• Keep output field names stable and do not rename documented keys across examples.
• If a value is unavailable, emit an explicit placeholder instead of omitting the field.

Completion Checklist

• Confirm all required inputs were present and valid.
• Confirm the supported execution path completed without unresolved errors.
• Confirm the final deliverable matches the documented format exactly.
• Confirm assumptions, limitations, and warnings are surfaced explicitly.