Agent skill · Data & Analytics

dmt-har-med-skill

Use this skill whenever the user wants an end-to-end workflow for the DMT-HAR-MED dataset (ds006644), including download, BIDS organization, and processing of rs-fMRI data from a psychedelic intervention study. Triggers include: 'DMT-HAR-MED', 'DMT HAR MED', 'ds006644', 'process DMT data', 'psychedelic fMRI', or any request to run the DMT-HAR-MED pipeline. This is the NeuroClaw dataset-orchestration layer for DMT-HAR-MED.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill dmt-har-med-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: 7 KB
Bundled scripts: none
Requires: - fmri-skill - bids-organizer - claw-shell
Path: skills/neuroclaw/dmt-har-med-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

# DMT-HAR-MED Skill (Dataset-Orchestration Layer) ## Overview `dmt-har-med-skill` is the NeuroClaw orchestration skill for the **DMT-HAR-MED** dataset (OpenNeuro ds006644). DMT-HAR-MED contains rs-fMRI data from 40 participants in a psychedelic intervention study investigating the effects of N,N-Dimethyltryptamine (DMT) on brain function. The dataset includes multiple experimental conditions (DMT, placebo) and behavioral/physiological measurements. It coordinates a fixed three-phase workflow: 1. Download DMT-HAR-MED data from OpenNeuro. 2. Prepare and validate BIDS-style data organization. 3. Delegate fMRI processing to `fmri-skill`. It also provides **phenotype extraction** and **QC integration** paths: - Extract DMT-HAR-MED phenotype data (intervention conditions, behavioral measures). - 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 DMT-HAR-MED data is available on OpenNeuro:

What's inside
Steps it walks through
  1. Overview
  2. Download Stage (Mandatory First Step)
  3. Source
  4. Supported DMT-HAR-MED Data Packages
  5. Delegation Rules for Download
  6. Download Inputs to Confirm in Plan
  7. Core Workflow (Never Bypassed)
  8. BIDS Preparation
  9. Script: scripts/reorganizedmtharmed.py
  10. Phenotype Extraction
  11. Script: scripts/extractdmtharmedphenotype.py
  12. QC Integration
  13. Script: scripts/dmtharmedqcsummary.py
  14. Recommended Output Layout
Commands it runs
python skills/dmt-har-med-skill/scripts/reorganize_dmt_har_med.py \
python skills/dmt-har-med-skill/scripts/extract_dmt_har_med_phenotype.py \
python skills/dmt-har-med-skill/scripts/dmt_har_med_qc_summary.py \
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About this skill
What does the dmt-har-med-skill skill do?

Use this skill whenever the user wants an end-to-end workflow for the DMT-HAR-MED dataset (ds006644), including download, BIDS organization, and processing of rs-fMRI data from a psychedelic intervention study. Triggers include: 'DMT-HAR-MED', 'DMT HAR MED', 'ds006644', 'process DMT data', 'psychedelic fMRI', or any request to run the DMT-HAR-MED pipeline. This is the NeuroClaw dataset-orchestration layer for DMT-HAR-MED.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill dmt-har-med-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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