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Agent skill

Meta Radial Plot

by aipoch in aipoch/medical-research-skills

Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis.

MITAuto-check passedData & Analytics

Install Meta Radial Plot

skills CLI
$ npx skills add aipoch/medical-research-skills --skill meta-radial-plot -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills meta-radial-plot --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Data Analysis/meta-radial-plot' .claude/skills/meta-radial-plot && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
meta-radial-plot
GitHub stars
2k
Token cost
~2.4k tokens
SKILL.md length
969 words
Files
4 (incl. scripts)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis.

  • Works in 3 steps: Validate Input Data → Execute R Script → Output Results
  • Tasks that involve CSV and tabular files
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 11 more sections
  • Runs R and Python scripts from its folder; calls python

What it does

Meta Radial Plot is an agent skill from aipoch/medical-research-skills. Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis. Visually assess heterogeneity across studies by displaying the relationship between standardized effect sizes and precision. Input: Meta-analysis data in CSV format; Output: Radial plot PNG and data CSV.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `meta-radial-plot_audit_result_v2.json` and `scripts/radial_plot_backup.py`).

It sits in Data & Analytics, covering CSV and tabular files. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Tasks that involve CSV and tabular files

Example prompts

  • “/meta-radial-plot”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Validate Input Data
  2. Execute R Script
  3. Output Results

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (R and Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Meta Radial Plot loads about 2.4k tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 969 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 969 words, ~2,352 tokens.

Download SKILL.mdSave it as .claude/skills/meta-radial-plot/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
meta-radial-plot
description
Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis. Visually assess heterogeneity across studies by displaying the relationship between standardized effect sizes and precision. Input: Meta-analysis data in CSV format; Output: Radial plot PNG and data CSV.
license
MIT
author
AIPOCH

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

Radial Plot Generation (Radial Plot / Galbraith Plot)

You are a Meta-analysis chart plotting assistant. Users provide Meta-analysis data, and you are responsible for calling R scripts to generate radial plots for heterogeneity analysis.

Important: Do not repeat the content of this instruction document to the user. Only output the user-visible content specified in the workflow.

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: "Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis. Visually assess heterogeneity across studies by displaying the relationship between standardized effect sizes and precision. Input: Meta-analysis data in CSV format; Output: Radial plot PNG and data CSV.".
  • Packaged executable path(s): scripts/radial_plot_backup.py.
  • 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

bash
cd "20260316/scientific-skills/Data Analytics/meta-radial-plot"
python -m py_compile scripts/radial_plot_backup.py
python scripts/radial_plot_backup.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/radial_plot_backup.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/radial_plot_backup.py.
  • 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.

Radial Plot Explanation

The radial plot (also called Radial Plot or Galbraith Plot) is a diagnostic graph for assessing heterogeneity in Meta-analysis:

  • X-axis: Precision (Precision = 1/SE, the reciprocal of standard error)
  • Y-axis: Standardized effect size (z = Effect / SE)

Plot Elements:

  • Scatter points: Each point represents one study
  • Regression line: A regression line passing through the origin, with slope equal to the pooled effect size
  • 95% confidence band: Dashed lines on both sides of the regression line, representing the 95% confidence interval

Plot Interpretation:

  • If no heterogeneity: All points should fall within the 95% confidence band, distributed along the regression line
  • If heterogeneity present: Points will scatter outside the confidence band, deviating from the regression line
  • High-precision studies (on the right): Have greater impact on the pooled result
  • Studies deviating from regression line: May be sources of heterogeneity

Comparison with Funnel Plot:

  • Radial plot eliminates the effect of sample size differences through standardization
  • Easier to identify studies inconsistent with the overall effect
  • Symmetry is easier to judge

Data Format Requirements

Depending on data type, the CSV file must contain different columns:

Binary (Dichotomous Data)
Column NameDescription
studyStudy name
group1_EventsNumber of events in treatment group
group1_sample_sizeTotal sample size in treatment group
group2_EventsNumber of events in control group
group2_sample_sizeTotal sample size in control group
Continuity (Continuous Data)
Column NameDescription
studyStudy name
group1_sample_sizeSample size in treatment group
group1_MeanMean in treatment group
group1_SDStandard deviation in treatment group
group2_sample_sizeSample size in control group
group2_MeanMean in control group
group2_SDStandard deviation in control group
Survival (Survival Data)
Column NameDescription
studyStudy name
group1_HRHazard ratio
group1_95%Lower CI95% confidence interval lower bound
group1_95%Upper CI95% confidence interval upper bound
Show full SKILL.md (375 more words)Show less

Workflow

Step 1: Validate Input Data
  1. Read the CSV file provided by the user
  2. Check necessary columns based on data type
  3. Validate data integrity (minimum 3 studies required)
Step 2: Execute R Script

Call the command:

bash
Rscript scripts/radial_plot.R "<csv_path>" "<type>" "<outcome_name>" "<output_dir>"

Parameter description:

  • csv_path: Absolute path to the input CSV file
  • type: Data type (Binary / Continuity / Survival)
  • outcome_name: Outcome indicator name (optional)
  • output_dir: Output directory (optional)
Step 3: Output Results

On success, output:

═══════════════════════════════════════════
Radial Plot Generation Complete
═══════════════════════════════════════════

【Outcome Indicator】 {outcome_name}
【Data Type】 {type}
【Included Studies】 {n} studies

【Heterogeneity Statistics】
• I² = {I2}%
• Tau² = {tau2}
• Q = {Q}, df = {df}, P = {pval_Q}

【Pooled Effect Size】
• {effect_name} = {value} [{lower}; {upper}]

【Output Files】
• Radial Plot: {output_dir}/{type}_radial_{outcome}.png
• Data Table: {output_dir}/{type}_radial_{outcome}.csv

【Heterogeneity Analysis】
• Studies within 95% confidence band: {n_in} studies ({pct_in}%)
• Studies outside 95% confidence band: {n_out} studies ({pct_out}%)

【Studies Outside Confidence Band】(if any)
Study                Precision        z-value         Deviation Direction
─────────────────────────────────────────────────────
Smith 2020          5.23        2.85        Above
...

【Conclusion】
{Heterogeneity assessment based on analysis results}

═══════════════════════════════════════════

R Script Dependencies

The following R packages need to be installed:

  • meta
  • metafor
  • ggplot2
  • ggrepel (optional, for label positioning to avoid overlaps)

If the user's environment is missing these packages, prompt them to run:

r
install.packages(c("meta", "metafor", "ggplot2", "ggrepel"))

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_radial_plot_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.

Quick Validation

Run this minimal verification path before full execution when possible:

bash
python scripts/radial_plot_backup.py --help

Expected output format:

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

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts) in scientific-skills/Data Analysis/meta-radial-plot of aipoch/medical-research-skills.

  • SKILL.md
  • meta-radial-plot_audit_result_v2.json
  • scripts/radial_plot.R
  • scripts/radial_plot_backup.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

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Categories

Questions about Meta Radial Plot

What does Meta Radial Plot do?

Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis. Meta Radial Plot is an agent skill from aipoch/medical-research-skills. Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis.

When should I use Meta Radial Plot?

Meta Radial Plot fits situations like: tasks that involve CSV and tabular files.

How do I install Meta Radial Plot in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill meta-radial-plot -a claude-code`. Or copy the skill folder (scientific-skills/Data Analysis/meta-radial-plot in aipoch/medical-research-skills) into .claude/skills/meta-radial-plot in your project. Claude Code loads it when a task matches its description.

How do I install Meta Radial Plot in Codex?

Run `npx skills add aipoch/medical-research-skills --skill meta-radial-plot -a codex`. Or copy the skill folder (scientific-skills/Data Analysis/meta-radial-plot in aipoch/medical-research-skills) into .agents/skills/meta-radial-plot in your project. Codex loads it when a task matches its description.

Can I use Meta Radial Plot in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill meta-radial-plot -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/meta-radial-plot, .gemini/skills/meta-radial-plot, .github/skills/meta-radial-plot and .opencode/skills/meta-radial-plot in your project.

What does Meta Radial Plot need to run?

Going by SKILL.md and its folder, Meta Radial Plot needs R and Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Meta Radial Plot access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Meta Radial Plot safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Meta Radial Plot use?

Meta Radial Plot is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Meta Radial Plot use?

About 2.4k tokens (SKILL.md is roughly 9.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Meta Radial Plot?

Skills that share tags, products or a category with Meta Radial Plot: Excel and CSV Data Analysis (bytedance/deer-flow, 83k stars), CSV Data Summarizer (coffeefuelbump/csv-data-summarizer-claude-skill, 468 stars), Exploratory Data Analysis (Oleafly/Oleafly, 205 stars) and Football Match Report (ricardoherediaj/football-analytics-tutorials, 119 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Meta Radial Plot?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,973 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.