Agent skill

Probast Quality Assessment For Prediction Model Studies

by aipoch in aipoch/medical-research-skills

Assess bias in medical prediction model studies using PROBAST tool.

Install Probast Quality Assessment For Prediction Model Studies

skills CLI

$ npx skills add aipoch/medical-research-skills --skill probast-quality-assessment-for-prediction-model-studies -a claude-code

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

GitHub CLI

$ gh skill install aipoch/medical-research-skills probast-quality-assessment-for-prediction-model-studies --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/probast-quality-assessment-for-prediction-model-studies' .claude/skills/probast-quality-assessment-for-prediction-model-studies && 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/

Paste into Claude Code

Install the "probast-quality-assessment-for-prediction-model-studies" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Data%20Analysis/probast-quality-assessment-for-prediction-model-studies into .claude/skills/probast-quality-assessment-for-prediction-model-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "probast-quality-assessment-for-prediction-model-studies", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Data%20Analysis/probast-quality-assessment-for-prediction-model-studies

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill probast-quality-assessment-for-prediction-model-studies -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills probast-quality-assessment-for-prediction-model-studies --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/'scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies' .agents/skills/probast-quality-assessment-for-prediction-model-studies && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "probast-quality-assessment-for-prediction-model-studies" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Data%20Analysis/probast-quality-assessment-for-prediction-model-studies into .agents/skills/probast-quality-assessment-for-prediction-model-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "probast-quality-assessment-for-prediction-model-studies", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill probast-quality-assessment-for-prediction-model-studies -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills probast-quality-assessment-for-prediction-model-studies --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/'scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies' .cursor/skills/probast-quality-assessment-for-prediction-model-studies && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "probast-quality-assessment-for-prediction-model-studies" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Data%20Analysis/probast-quality-assessment-for-prediction-model-studies into .cursor/skills/probast-quality-assessment-for-prediction-model-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "probast-quality-assessment-for-prediction-model-studies", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/aipoch/medical-research-skills.git --path 'scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies'

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill probast-quality-assessment-for-prediction-model-studies -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills probast-quality-assessment-for-prediction-model-studies --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/'scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies' .gemini/skills/probast-quality-assessment-for-prediction-model-studies && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "probast-quality-assessment-for-prediction-model-studies" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Data%20Analysis/probast-quality-assessment-for-prediction-model-studies into .gemini/skills/probast-quality-assessment-for-prediction-model-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "probast-quality-assessment-for-prediction-model-studies", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install aipoch/medical-research-skills probast-quality-assessment-for-prediction-model-studies

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add aipoch/medical-research-skills --skill probast-quality-assessment-for-prediction-model-studies -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/'scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies' .github/skills/probast-quality-assessment-for-prediction-model-studies && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "probast-quality-assessment-for-prediction-model-studies" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Data%20Analysis/probast-quality-assessment-for-prediction-model-studies into .github/skills/probast-quality-assessment-for-prediction-model-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "probast-quality-assessment-for-prediction-model-studies", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aipoch/medical-research-skills --skill probast-quality-assessment-for-prediction-model-studies -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install aipoch/medical-research-skills probast-quality-assessment-for-prediction-model-studies --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/'scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies' .opencode/skills/probast-quality-assessment-for-prediction-model-studies && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "probast-quality-assessment-for-prediction-model-studies" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/scientific-skills/Data%20Analysis/probast-quality-assessment-for-prediction-model-studies into .opencode/skills/probast-quality-assessment-for-prediction-model-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "probast-quality-assessment-for-prediction-model-studies", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: probast-quality-assessment-for-prediction-model-studies
GitHub stars: 1.9k
Token cost: ~1.2k tokens
SKILL.md length: 579 words
Files: 4 (incl. scripts, references)
Skills in repo: 578
Repo updated: Sep 17, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Assess bias in medical prediction model studies using PROBAST tool.

Works in 4 steps: Extract Metadata → Assess Risk Domains (Parallel) → Determine Overall Risk → …
User wants to evaluate the quality
SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 3 more sections
Runs Python scripts from its folder; calls python

What it does

Probast Quality Assessment For Prediction Model Studies is an agent skill from aipoch/medical-research-skills. Assess bias in medical prediction model studies using PROBAST tool. Use when user wants to evaluate the quality or risk of bias of a medical paper (text or PDF).

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `probast-quality-assessment-for-prediction-model-studies_audit_result_v1.json`, `references/probast_prompts.md` and `scripts/extract_pdf.py`).

It sits in Documents & Office, covering PDF 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

User wants to evaluate the quality
Risk of bias of a medical paper (text

Example prompts

“/probast-quality-assessment-for-prediction-model-studies”

Requirements

Python 3

Workflow steps

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

Extract Metadata
Assess Risk Domains (Parallel)
Determine Overall Risk
Format 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 1 file in scripts/ (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

Probast Quality Assessment For Prediction Model Studies loads about 1.2k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 54 tokens; SKILL.md has 579 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~54
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~3.5k

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). 579 words, ~1,231 tokens.

Download SKILL.mdSave it as .claude/skills/probast-quality-assessment-for-prediction-model-studies/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: probast-quality-assessment-for-prediction-model-studies
description: Assess bias in medical prediction model studies using PROBAST tool. Use when user wants to evaluate the quality or risk of bias of a medical paper (text or PDF).
license: MIT
author: AIPOCH

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

PROBAST Quality Assessment

This skill evaluates the risk of bias in medical prediction model studies using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) framework. It analyzes the full text of a paper across four domains: Participants, Predictors, Outcome, and Analysis.

When to Use

Use this skill when you need assess bias in medical prediction model studies using probast tool. use when user wants to evaluate the quality or risk of bias of a medical paper (text or pdf) 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/extract_pdf.py is the most direct path to complete the request.
Use this skill when you need the probast-quality-assessment for prediction model studies package behavior rather than a generic answer.

Key Features

Scope-focused workflow aligned to: Assess bias in medical prediction model studies using PROBAST tool. Use when user wants to evaluate the quality or risk of bias of a medical paper (text or PDF).
Packaged executable path(s): scripts/extract_pdf.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

bash

cd "20260316/scientific-skills/Data Analytics/probast-quality-assessment-for-prediction-model-studies"
python -m py_compile scripts/extract_pdf.py
python scripts/extract_pdf.py --help

Example run plan:

Confirm the user input, output path, and any required config values.
Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
Run python scripts/extract_pdf.py with the validated inputs.
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/extract_pdf.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.

Show full SKILL.md (208 more words)Show less

Workflow

To perform the assessment, follow this sequence of operations using the prompts defined in references/probast_prompts.md.

Step 1: Extract Metadata

Extract the first author and year from the paper.

Prompt: Use "1. Metadata Extraction" from references/probast_prompts.md.

Step 2: Assess Risk Domains (Parallel)

Assess the risk of bias for each of the four domains. For each domain, use the corresponding prompt to generate a risk rating (Low/High/Unclear) and detailed reasoning.

Domain 1 (Participants): Use "2. Domain 1: Participants" from references/probast_prompts.md.
Domain 2 (Predictors): Use "3. Domain 2: Predictors" from references/probast_prompts.md.
Domain 3 (Outcome): Use "4. Domain 3: Outcome" from references/probast_prompts.md.
Domain 4 (Analysis): Use "5. Domain 4: Analysis" from references/probast_prompts.md.

Step 3: Determine Overall Risk

Combine the risk ratings from the four domains to determine the overall risk of bias.

Input: The risk ratings (Low/High/Unclear) from Domains 1-4.
Prompt: Use "6. Overall Risk Assessment" from references/probast_prompts.md.

Step 4: Format Output

Generate a final JSON report containing the risk ratings for all domains and the overall assessment.

Input: The results from Steps 1-3.
Prompt: Use "7. JSON Extraction" from references/probast_prompts.md.
Output Format: A JSON object matching the study_risk_of_bias_schema.

Helper Scripts

PDF Text Extraction

When the user provides a PDF file path, use extract_pdf.py to extract the text content before assessment:

Files

SKILL.md and 3 other files (scripts, references) in scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies of aipoch/medical-research-skills.

SKILL.md
probast-quality-assessment-for-prediction-model-studies_audit_result_v1.json
references/probast_prompts.md
scripts/extract_pdf.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

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Questions about Probast Quality Assessment For Prediction Model Studies

What does Probast Quality Assessment For Prediction Model Studies do?

Assess bias in medical prediction model studies using PROBAST tool. Probast Quality Assessment For Prediction Model Studies is an agent skill from aipoch/medical-research-skills. Assess bias in medical prediction model studies using PROBAST tool.

When should I use Probast Quality Assessment For Prediction Model Studies?

Probast Quality Assessment For Prediction Model Studies fits situations like: user wants to evaluate the quality; risk of bias of a medical paper (text.

How do I install Probast Quality Assessment For Prediction Model Studies in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill probast-quality-assessment-for-prediction-model-studies -a claude-code`. Or copy the skill folder (scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies in aipoch/medical-research-skills) into .claude/skills/probast-quality-assessment-for-prediction-model-studies in your project. Claude Code loads it when a task matches its description.

How do I install Probast Quality Assessment For Prediction Model Studies in Codex?

Run `npx skills add aipoch/medical-research-skills --skill probast-quality-assessment-for-prediction-model-studies -a codex`. Or copy the skill folder (scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies in aipoch/medical-research-skills) into .agents/skills/probast-quality-assessment-for-prediction-model-studies in your project. Codex loads it when a task matches its description.

Can I use Probast Quality Assessment For Prediction Model Studies 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 probast-quality-assessment-for-prediction-model-studies -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/probast-quality-assessment-for-prediction-model-studies, .gemini/skills/probast-quality-assessment-for-prediction-model-studies, .github/skills/probast-quality-assessment-for-prediction-model-studies and .opencode/skills/probast-quality-assessment-for-prediction-model-studies in your project.

What does Probast Quality Assessment For Prediction Model Studies need to run?

Going by SKILL.md and its folder, Probast Quality Assessment For Prediction Model Studies needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Probast Quality Assessment For Prediction Model Studies 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 Probast Quality Assessment For Prediction Model Studies 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 Probast Quality Assessment For Prediction Model Studies use?

Probast Quality Assessment For Prediction Model Studies 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 Probast Quality Assessment For Prediction Model Studies use?

About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.3k tokens, read only when the agent opens those files.

What are the alternatives to Probast Quality Assessment For Prediction Model Studies?

Skills that share tags, products or a category with Probast Quality Assessment For Prediction Model Studies: Mathmodel Skill (handsomeZR-netizen/mathmodel-skill, 292 stars), Find Skills (fastclaw-ai/fastclaw, 1.4k stars), Latchshot Page Capture (github/awesome-copilot, 40k stars) and Pie Chart Data Analysis (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 Probast Quality Assessment For Prediction Model Studies?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,937 GitHub stars. The repository holds 578 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.

Install Probast Quality Assessment For Prediction Model Studies

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

PROBAST Quality Assessment

When to Use

Key Features

Dependencies

Example Usage

Implementation Details

Workflow

Step 1: Extract Metadata

Step 2: Assess Risk Domains (Parallel)

Step 3: Determine Overall Risk

Step 4: Format Output

Helper Scripts

PDF Text Extraction

Files

Compare with similar skills

Similar skills

More from aipoch/medical-research-skills

Related

Questions about Probast Quality Assessment For Prediction Model Studies