Agent skill · Data & Analytics

ukb-skill

Use this skill whenever the user wants to analyze already available UK Biobank data for brain-related research, including neurological outcomes, cognitive phenotypes, brain MRI derived phenotypes, survival analysis, subgroup analysis, propensity score analysis, mediation analysis, sensitivity analysis, machine learning, visualization, or manuscript-ready summaries. This skill only covers post-extraction analysis and explicitly excludes RAP access and data download guidance.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill ukb-skill --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 15 KB
Bundled scripts: none
Requires: - nilearn-tool - fsl-tool - claw-shell
Path: skills/neuroclaw/ukb-skill/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# UKB Skill (Dataset Layer) ## Overview `ukb-skill` is the NeuroClaw **analysis-layer** skill for **brain-focused UK Biobank downstream research**. This skill is designed for users who already have local UK Biobank tables, derived phenotype tables, or neuroimaging-derived feature matrices and want to run downstream statistical or machine learning analyses. It follows the NeuroClaw hierarchical design principles: - This skill describes **WHAT needs to be analyzed** and **which existing skill should handle each part**. - It contains **no RAP download workflow** and **no cloud export instructions**. - It contains **no direct implementation code** and **no direct shell commands**. - All concrete execution should be delegated through `claw-shell` and related downstream skills. **Core workflow (never bypassed):** 1. Confirm that the user already has accessible UK Biobank-derived local data. 2. Identify the research target: neurological endpoint, cognitive phenotype, brain MRI phenotype, or predictive modeling task. 3. Identify the analysis type: regression, survival analysis, subgroup analysis, propensity score analysis, mediation analysis, sensitivity analysis, machine learning, visuali

What's inside
Steps it walks through
  1. Overview
  2. Scope Boundaries
  3. Included
  4. Excluded
  5. Benchmark-Facing Default Mainline
  6. Quick Reference (Common UKB Brain Tasks → Delegation Map)
  7. Recommended Strategy (Decision Logic)
  8. Minimal Input Requirements
  9. For Survival Analysis
  10. For Cross-Sectional Regression
  11. For Brain Feature Modeling
  12. Brain-Relevant UKB Targets
  13. When to Call This Skill
  14. Complementary / Related Skills
Commands it runs
python skills/ukb-skill/scripts/extract_ukb_phenotype.py \
python skills/ukb-skill/scripts/extract_ukb_phenotype.py --list-variables
python skills/ukb-skill/scripts/extract_ukb_cases.py \
python skills/ukb-skill/scripts/extract_ukb_cases.py --list-diseases
python skills/ukb-skill/scripts/build_ukb_survival.py \
python skills/ukb-skill/scripts/ukb_qc_summary.py \
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About this skill
What does the ukb-skill skill do?

Use this skill whenever the user wants to analyze already available UK Biobank data for brain-related research, including neurological outcomes, cognitive phenotypes, brain MRI derived phenotypes, survival analysis, subgroup analysis, propensity score analysis, mediation analysis, sensitivity analysis, machine learning, visualization, or manuscript-ready summaries. This skill only covers post-extraction analysis and explicitly excludes RAP access and data download guidance.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill ukb-skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going