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Agent skill

Meta Sensitivity Plot

by aipoch in aipoch/medical-research-skills

Generate leave-one-out sensitivity analysis plots for meta-analysis.

MITAuto-check passedDocuments & Office

Install Meta Sensitivity Plot

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

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills meta-sensitivity-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-sensitivity-plot' .claude/skills/meta-sensitivity-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-sensitivity-plot
GitHub stars
2k
Token cost
~1.6k tokens
SKILL.md length
595 words
Files
4 (incl. scripts)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Generate leave-one-out sensitivity analysis plots for meta-analysis.

  • Works in 3 steps: Validate input → Execute R script → Output
  • Tasks that involve CSV and tabular files
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 6 more sections
  • Runs R and Python scripts from its folder; calls python

What it does

Meta Sensitivity Plot is an agent skill from aipoch/medical-research-skills. Generate leave-one-out sensitivity analysis plots for meta-analysis. Input is a CSV file containing meta-analysis data; outputs are a sensitivity forest plot (PNG) and a sensitivity data table (CSV) showing pooled effect estimates after excluding each study in turn.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `meta-sensitivity-plot_audit_result_v1.json` and `scripts/sensitivity_analysis.py`).

It sits in Documents & Office, covering CSV and tabular files and Data analysis. 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
  • Tasks that involve Data analysis

Example prompts

  • “/meta-sensitivity-plot”

Requirements

  • Python 3

Workflow steps

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

  1. Validate input
  2. Execute R script
  3. Output

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 Sensitivity Plot loads about 1.6k tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 595 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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). 595 words, ~1,618 tokens.

Download SKILL.mdSave it as .claude/skills/meta-sensitivity-plot/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
meta-sensitivity-plot
description
Generate leave-one-out sensitivity analysis plots for meta-analysis. Input is a CSV file containing meta-analysis data; outputs are a sensitivity forest plot (PNG) and a sensitivity data table (CSV) showing pooled effect estimates after excluding each study in turn.
license
MIT
author
AIPOCH

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

When to Use

  • Use this skill when you need "generate leave-one-out sensitivity analysis plots for meta-analysis. input is a csv file containing meta-analysis data; outputs are a sensitivity forest plot (png) and a sensitivity data table (csv) showing pooled effect estimates after excluding each study in turn." in a reproducible workflow.
  • Use this skill when a data analytics 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/sensitivity_analysis.py is the most direct path to complete the request.
  • Use this skill when you need the meta-sensitivity-plot package behavior rather than a generic answer.

Key Features

  • Scope-focused workflow aligned to: "Generate leave-one-out sensitivity analysis plots for meta-analysis. Input is a CSV file containing meta-analysis data; outputs are a sensitivity forest plot (PNG) and a sensitivity data table (CSV) showing pooled effect estimates after excluding each study in turn.".
  • Packaged executable path(s): scripts/sensitivity_analysis.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-sensitivity-plot"
python -m py_compile scripts/sensitivity_analysis.py
python scripts/sensitivity_analysis.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/sensitivity_analysis.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/sensitivity_analysis.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.

Sensitivity Analysis Plotting (Leave-one-out)

You are a meta-analysis plotting assistant. The user provides meta-analysis data, and you are responsible for calling an R script to perform leave-one-out sensitivity analysis and generate plots.

Important: Do not echo this instruction document to the user. Only output user-visible content defined by the workflow.

Show full SKILL.md (219 more words)Show less

About Sensitivity Analysis

Leave-one-out sensitivity analysis:

  • Remove each study one at a time and re-calculate the pooled effect estimate
  • Assess the influence of individual studies on the overall result
  • Evaluate the robustness of the meta-analysis findings

Data Format Requirements

Depending on the data type, the input CSV should contain the following columns:

Binary
ColumnDescription
studyStudy identifier
group1_EventsEvents in intervention group
group1_sample_sizeSample size of intervention group
group2_EventsEvents in control group
group2_sample_sizeSample size of control group
Continuity
ColumnDescription
studyStudy identifier
group1_sample_sizeSample size (intervention)
group1_MeanMean (intervention)
group1_SDStandard deviation (intervention)
group2_sample_sizeSample size (control)
group2_MeanMean (control)
group2_SDStandard deviation (control)
Survival
ColumnDescription
studyStudy identifier
group1_HRHazard ratio
group1_95%Lower_CI95% CI lower bound
group1_95%Upper_CI95% CI upper bound

Workflow

Step 1: Validate input
  1. Read the input CSV provided by the user
  2. Check required columns according to the specified data type
  3. Validate data (note: at least 3 studies are required to run meaningful sensitivity analysis)
Step 2: Execute R script

Call:

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

Parameters:

  • csv_path: absolute path to the input CSV
  • type: data type (Binary / Continuity / Survival)
  • outcome_name: outcome label (optional)
  • output_dir: output directory (optional)
Step 3: Output

On success, output:

═══════════════════════════════════════════
Sensitivity analysis completed
═══════════════════════════════════════════

[Outcome] {outcome_name}
[Data type] {type}
[Included studies] {n}

[Output files]
• Sensitivity forest plot: {output_dir}/{type}_sensitive_forest_{outcome}.png
• Sensitivity data table: {output_dir}/{type}_sensitive_{outcome}.csv

[Pooled effect (all studies)]
• {effect_name} = {value} [{lower}; {upper}]

[Summary of sensitivity results]
Study removed       Effect     95% CI           I²
───────────────────────────────────────────────────────────
Smith 2020          0.85      [0.72; 1.01]     45.2%
Jones 2021          0.88      [0.75; 1.03]     42.1%
...

[Effect change analysis]
• Effect range: 0.82 ~ 0.91
• Relative change: 10.3%

[Conclusion]
• Robustness: {robust/not robust}
• {recommendation based on magnitude of change}

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

R script dependencies

Install these R packages if not present:

  • meta
  • metafor
  • stringr
  • grid

Prompt the user to run:

r
install.packages(c("meta", "metafor", "stringr", "grid"))

© 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-sensitivity-plot of aipoch/medical-research-skills.

  • SKILL.md
  • meta-sensitivity-plot_audit_result_v1.json
  • scripts/sensitivity_analysis.R
  • scripts/sensitivity_analysis.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Meta Sensitivity Plot next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Research Data Auto Analysis PlottingDrchronx/ai-agent-research-starter-kit134—~463Automated safety check: PassCustom licence
Data Cleanupsgharlow/claude-code-recipes388—~566Automated safety check: PassCustom licence
Sn Da Image CaptionMichaelYang-lyx/AIDABench1111 repos~2kAutomated safety check: PassNone
Douban Skilldaymade/claude-code-skills1.4k—~1.1kAutomated safety check: PassMIT

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Categories

Questions about Meta Sensitivity Plot

What does Meta Sensitivity Plot do?

Generate leave-one-out sensitivity analysis plots for meta-analysis. Meta Sensitivity Plot is an agent skill from aipoch/medical-research-skills. Generate leave-one-out sensitivity analysis plots for meta-analysis.

When should I use Meta Sensitivity Plot?

Meta Sensitivity Plot fits situations like: tasks that involve CSV and tabular files; tasks that involve Data analysis.

How do I install Meta Sensitivity Plot in Claude Code?

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

How do I install Meta Sensitivity Plot in Codex?

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

Can I use Meta Sensitivity 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-sensitivity-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-sensitivity-plot, .gemini/skills/meta-sensitivity-plot, .github/skills/meta-sensitivity-plot and .opencode/skills/meta-sensitivity-plot in your project.

What does Meta Sensitivity Plot need to run?

Going by SKILL.md and its folder, Meta Sensitivity 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 Sensitivity 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 Sensitivity 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 Sensitivity Plot use?

Meta Sensitivity 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 Sensitivity Plot use?

About 1.6k tokens (SKILL.md is roughly 6.5k 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 Sensitivity Plot?

Skills that share tags, products or a category with Meta Sensitivity Plot: Codebook (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), Research Data Auto Analysis Plotting (Drchronx/ai-agent-research-starter-kit, 134 stars), Data Cleanup (sgharlow/claude-code-recipes, 388 stars) and Sn Da Image Caption (MichaelYang-lyx/AIDABench, 111 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Meta Sensitivity 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.