Agent skill · Data & Analytics

abide-skill

Use this skill whenever the user wants an end-to-end workflow for the ABIDE (Autism Brain Imaging Data Exchange) dataset, including download, BIDS organization, and processing of sMRI and rs-fMRI data. Triggers include: 'ABIDE', 'ABIDE data', 'process ABIDE', 'ABIDE fMRI', 'ABIDE sMRI', 'autism imaging', or any request to run the ABIDE pipeline. This is the NeuroClaw dataset-orchestration layer for ABIDE.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill abide-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: 12 KB
Bundled scripts: none
Requires: - smri-skill - fmri-skill - bids-organizer - claw-shell
Path: skills/neuroclaw/abide-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

# ABIDE Skill (Dataset-Orchestration Layer) ## Overview `abide-skill` is the NeuroClaw orchestration skill for the **ABIDE (Autism Brain Imaging Data Exchange)** dataset. It coordinates a fixed three-phase workflow: 1. Download ABIDE data from the FCP/INDI repository or NITRC. 2. Prepare and validate BIDS-style data organization for downstream processing. 3. Delegate modality pipelines to `smri-skill` and `fmri-skill`. It also provides **phenotype extraction** and **QC integration** paths: - Extract and merge ABIDE phenotype tables (diagnosis, age, sex, site, FIQ, ADOS, etc.). - Generate per-subject QC summaries with exclusion lists. This skill follows NeuroClaw hierarchy: - Defines **WHAT to do**, not low-level implementation details. - Does **not** execute direct shell commands itself. - Delegates all execution via `claw-shell` to base/tool skills. **Research use only.** --- ## Download Stage (Mandatory First Step) ### Source ABIDE data is distributed through the **FCP/INDI** repository: - ABIDE I: https://fcon_1000.projects.nitrc.org/indi/abide/ - ABIDE II: https://fcon_1000.projects.nitrc.org/indi/abide_II.html - NITRC mirror: https://www.nitrc.org/projects/fcp_indi/ ### Suppor

What's inside
Steps it walks through
  1. Overview
  2. Download Stage (Mandatory First Step)
  3. Source
  4. Supported ABIDE Data Packages
  5. Delegation Rules for Download
  6. Download Inputs to Confirm in Plan
  7. Narrow Path: ABIDE Raw NIfTI -> BIDS Staging
  8. When this narrow path should dominate
  9. Narrow-path contract
  10. Expected narrow-path behavior
  11. Core Workflow (Never Bypassed)
  12. Input Layout (Example)
  13. BIDS Preparation
  14. Script: scripts/reorganizeabide.py
Commands it runs
python skills/abide-skill/scripts/reorganize_abide.py \
python skills/abide-skill/scripts/extract_abide_phenotype.py \
python skills/abide-skill/scripts/abide_qc_summary.py \
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About this skill
What does the abide-skill skill do?

Use this skill whenever the user wants an end-to-end workflow for the ABIDE (Autism Brain Imaging Data Exchange) dataset, including download, BIDS organization, and processing of sMRI and rs-fMRI data. Triggers include: 'ABIDE', 'ABIDE data', 'process ABIDE', 'ABIDE fMRI', 'ABIDE sMRI', 'autism imaging', or any request to run the ABIDE pipeline. This is the NeuroClaw dataset-orchestration layer for ABIDE.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill abide-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.

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