New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Meta Forest Continuous Plot

by aipoch in aipoch/medical-research-skills

Generate forest plots for meta-analysis of continuous data. An agent skill from aipoch/medical-research-skills.

MITAuto-check passedDocuments & Office

Install Meta Forest Continuous Plot

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

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills meta-forest-continuous-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-forest-continuous-plot' .claude/skills/meta-forest-continuous-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-forest-continuous-plot
GitHub stars
2k
Token cost
~1.8k tokens
SKILL.md length
759 words
Files
6 (incl. scripts)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Generate forest plots for meta-analysis of continuous data. An agent skill from aipoch/medical-research-skills.

  • 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 10 more sections
  • Runs Python and R scripts from its folder; calls python

What it does

Meta Forest Continuous Plot is an agent skill from aipoch/medical-research-skills. Generate forest plots for meta-analysis of continuous data. Input a CSV file containing study names, means, standard deviations, and sample sizes for experimental and control groups. Output forest plot PNG and data table CSV.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts (for example `meta-forest-continuous-plot_audit_result_v2.json`, `scripts/README.md` and `scripts/convert_data.py`).

It sits in Documents & Office, covering CSV and tabular files and Experimental design. 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 Experimental design

Example prompts

  • “/meta-forest-continuous-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 4 files in scripts/ (Python and R), 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 Forest Continuous Plot loads about 1.8k tokens when it runs. Until then it costs about 63 tokens; SKILL.md has 759 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~63
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

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). 759 words, ~1,797 tokens.

Download SKILL.mdSave it as .claude/skills/meta-forest-continuous-plot/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
meta-forest-continuous-plot
description
Generate forest plots for meta-analysis of continuous data. Input a CSV file containing study names, means, standard deviations, and sample sizes for experimental and control groups. Output forest plot PNG and data table CSV.
license
MIT
author
AIPOCH

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

Continuous Data Forest Plot Generation

You are a meta-analysis chart generation assistant. Users provide continuous data (means/standard deviations), and you are responsible for calling R scripts to generate forest plots.

Important: Do not repeat the content of this instruction document to users. Only output user-visible content defined 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 forest plots for meta-analysis of continuous data. Input a CSV file containing study names, means, standard deviations, and sample sizes for experimental and control groups. Output forest plot PNG and data table CSV.".
  • Packaged executable path(s): scripts/convert_data.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

bash
cd "20260316/scientific-skills/Data Analytics/meta-forest-continuous-plot"
python -m py_compile scripts/convert_data.py
python scripts/convert_data.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/convert_data.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/convert_data.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.

Data Format Requirements

Users need to provide a CSV file containing the following columns:

Column NameDescriptionExample
studyStudy identifier (author + year)Smith 2020
outcome_newOutcome measure nameBlood Pressure
group1_sample_sizeIntervention group sample size50
group1_MeanIntervention group mean120.5
group1_SDIntervention group standard deviation15.2
group2_sample_sizeControl group sample size48
group2_MeanControl group mean135.8
group2_SDControl group standard deviation18.3

Workflow

Step 1: Validate Input Data
  1. Read the CSV file provided by the user
  2. Check if all required columns are present
  3. Validate data integrity (at least 2 studies, reasonable values)

If data is problematic, prompt the user to correct and resubmit.

Step 2: Execute R Script

Call command:

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

Parameter descriptions:

  • csv_path: Absolute path to the input CSV file
  • outcome_name: Name of the outcome measure (optional, extracted from data by default)
  • output_dir: Output directory (optional, defaults to current directory)
Show full SKILL.md (303 more words)Show less
Step 3: Output Results

On successful completion, output:

═══════════════════════════════════════════
Forest Plot Generation Completed
═══════════════════════════════════════════

【Outcome Measure】{outcome_name}
【Number of Studies】{n}

【Output Files】
• Forest Plot: {output_dir}/Continuity_forest_{outcome}.png
• Data Table: {output_dir}/Continuity_forest_{outcome}.csv

【Pooled Effect Size】
• SMD = {value} [{lower}; {upper}]
• P-value = {p_value}

【Heterogeneity】
• I² = {I2}%
• Tau² = {tau2}
• Q-test P-value = {pval_Q}

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

R Script Dependencies

The following R packages are required:

  • meta
  • metafor
  • grid
  • stringr

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

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

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_forest_continuous_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/convert_data.py --help

Expected output format:

text
Result file: meta_forest_continuous_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 5 other files (scripts) in scientific-skills/Data Analysis/meta-forest-continuous-plot of aipoch/medical-research-skills.

  • SKILL.md
  • meta-forest-continuous-plot_audit_result_v2.json
  • scripts/README.md
  • scripts/convert_data.py
  • scripts/forest_continuous.R
  • scripts/forest_continuous.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Meta Forest Continuous 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.

Meta Forest Continuous Plot compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Meta Forest Continuous Plot this skillaipoch/medical-research-skills2k—~1.8kAutomated safety check: PassMIT
Module Authoringdna-seq/just-dna-lite141—~4.8kAutomated safety check: NotesAGPL-3.0
Vdjdb Extractantigenomics/vdjdb-db157—~1.2kAutomated safety check: PassCustom licence
Nwb ConversionK-Dense-AI/scientific-agent-skills48k1 repos~1.9kAutomated safety check: PassMIT
Auditing Part11 Trailsmaziyarpanahi/openmed5.5k—~2.2kAutomated safety check: PassApache-2.0
Generate CodebookAperivue/medsci-skills329—~1.1kAutomated safety check: PassMIT

Similar skills

  • Module Authoring

    dna-seq/just-dna-lite

    Author, resolve, compile and publish a just-dna annotation module — the spec directory layout, the CSV column contracts and vocabularies, the enrich→compile pipeline, and the checks that decide…

    141 GitHub stars~4.8k tokensUpdated 9 days ago
    Documents & OfficeAuto-check: notes

  • Vdjdb Extract

    antigenomics/vdjdb-db

    Extract TCR:pMHC specificity records from raw submission sources - supplementary XLS/CSV tables, PDF manuscripts, 10x Genomics contig and clonotype files, AIRR Rearrangement TSVs, Adaptive ImmunoSEQ…

    157 GitHub stars~1.2k tokensUpdated today
    Documents & OfficeAuto-check passed

  • Nwb Conversion

    K-Dense-AI/scientific-agent-skills

    Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema…

    48k GitHub starsUsed in 1 repo~1.9k tokens
    Documents & OfficeAuto-check passed

  • Auditing Part11 Trails

    maziyarpanahi/openmed

    Generates and verifies 21 CFR Part 11-style audit trails — who/what/when, electronic signatures, and tamper-evidence — for OpenMed pipelines in GxP and clinical-trial (GCP) settings.

    5.5k GitHub stars~2.2k tokensUpdated today
    Documents & OfficeAuto-check passed

  • Generate Codebook

    Aperivue/medsci-skills

    A skill your agent uses when a tabular dataset (CSV, Excel, Parquet, Stata, SAS) needs a data dictionary.

    329 GitHub stars~1.1k tokensUpdated 2 days ago
    Documents & OfficeAuto-check passed

  • Marker Dominance Mapper

    ClawBio/ClawBio

    Deterministic marker-dominance region mapping from local spot-count CSVs

    1.2k GitHub stars~1.6k tokensUpdated today
    Documents & OfficeAuto-check passed

More from aipoch/medical-research-skills

All 567 skills in this repo

  • Academic Poster Generator

    aipoch/medical-research-skills

    Complete workflow for generating academic research posters from PDF literature; use when you need to extract paper content from PDFs and produce a LaTeX-based poster…

    2k GitHub stars~2.2k tokensUpdated 21 days ago
    Auto-check passed

  • Diagnostic Study Quality Assessment Quadas

    aipoch/medical-research-skills

    Analyzes clinical diagnostic accuracy studies for bias using the QUADAS-2 tool.

    2k GitHub stars~1.4k tokensUpdated 21 days ago
    Auto-check passed

  • Exploratory Data Analysis

    aipoch/medical-research-skills

    Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.

    2k GitHub stars~3.7k tokensUpdated 21 days ago
    Auto-check passed

  • Iso Certification

    aipoch/medical-research-skills

    A toolkit for preparing ISO 13485:2016 certification documentation for medical device QMS.

    2k GitHub stars~1.8k tokensUpdated 21 days ago
    Auto-check passed

  • Journal Skills

    aipoch/medical-research-skills

    Recommends target journals for manuscript submission by analyzing the paper topic/abstract and the journal distribution of similar PubMed literature; use when users ask for journal…

    2k GitHub stars~1.7k tokensUpdated 21 days ago
    Auto-check passed

  • Latex Posters

    aipoch/medical-research-skills

    Creates academic-poster writing packages for LaTeX using beamerposter, tikzposter, or baposter.

    2k GitHub stars~1.3k tokensUpdated 21 days ago
    Auto-check passed

Categories

Questions about Meta Forest Continuous Plot

What does Meta Forest Continuous Plot do?

Generate forest plots for meta-analysis of continuous data. An agent skill from aipoch/medical-research-skills. Meta Forest Continuous Plot is an agent skill from aipoch/medical-research-skills. Generate forest plots for meta-analysis of continuous data.

When should I use Meta Forest Continuous Plot?

Meta Forest Continuous Plot fits situations like: tasks that involve CSV and tabular files; tasks that involve Experimental design.

How do I install Meta Forest Continuous Plot in Claude Code?

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

How do I install Meta Forest Continuous Plot in Codex?

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

Can I use Meta Forest Continuous 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-forest-continuous-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-forest-continuous-plot, .gemini/skills/meta-forest-continuous-plot, .github/skills/meta-forest-continuous-plot and .opencode/skills/meta-forest-continuous-plot in your project.

What does Meta Forest Continuous Plot need to run?

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

Does Meta Forest Continuous 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 Forest Continuous 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 Forest Continuous Plot use?

Meta Forest Continuous 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 Forest Continuous Plot use?

About 1.8k tokens (SKILL.md is roughly 7.2k 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 Forest Continuous Plot?

Skills that share tags, products or a category with Meta Forest Continuous Plot: Module Authoring (dna-seq/just-dna-lite, 141 stars), Vdjdb Extract (antigenomics/vdjdb-db, 157 stars), Nwb Conversion (K-Dense-AI/scientific-agent-skills, 48k stars) and Auditing Part11 Trails (maziyarpanahi/openmed, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Meta Forest Continuous Plot?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 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.