**LLM 코딩 에이전트를위한 신경 과학 기술의 커뮤니티 컬렉션. **

skills-comm 에이전트 스킬 수집 LLM 에이전트 (Claude Code and other Agent that read SKILL.md/AGENTS.md) 실제 neuroimaging tools correctly 및 reproducibly를 구동하는 방법. 자주 묻는 질문 FSL, ANTs, FreeSurfer, DataLad, nipoppy 또는 SLURM에 대한 에이전트 improvise 명령 클러스터, 작업 지식 인코딩 – 오른쪽 플래그, 안전한 주문 작업, 품질 관리 검사 및 재현성 난간 - 그래서 일 사람들과 기계의 맞은편에 반복 가능합니다.

목표는 operationalization: "전문가가가 실제로이 도구를 실행하는 방법"을 포장 대리인은 수요에 적재할 수 있습니다.

이 누구를 위해

• **Method 개발자 및 도구 저자 ** 소프트웨어를 원하는 것은 의미가 없었습니다. LLM이 키보드에있을 때도 사용할 수 있습니다.
• **Neuroimaging 연구원 ** 커뮤니티를 따르는 코딩 에이전트 best-practice (BIDS, YODA/DataLad 검증, 명시된 도구 버전) 기본적으로.
• Contributors는 재사용 가능한 기술로 재현 가능한 레시피를 공유하고자 합니다.

여기에 있는 것

자신의 디렉토리로 plugins/]의 각 기여.

skills-comm

A community collection of neuroimaging skills for LLM coding agents.

skills-comm gathers Agent Skills that teach LLM agents (Claude Code and other agents that read SKILL.md/AGENTS.md) how to drive real neuroimaging tools correctly and reproducibly. Instead of letting an agent improvise commands for FSL, ANTs, FreeSurfer, DataLad, nipoppy, or a SLURM cluster, a skill encodes the operational knowledge — the right flags, the safe order of operations, the quality-control checks, and the reproducibility guardrails — so the work is repeatable across people and machines.

The goal is operationalization: turn “how an expert actually runs this tool” into a package an agent can load on demand.

Who this is for

• Method developers & tool authors who want their software used the way it was meant to be used, even when an LLM is at the keyboard.
• Neuroimaging researchers who want a coding agent that follows community best-practice (BIDS, YODA/DataLad provenance, explicit tool versioning) by default.
• Contributors who want to share a reproducible recipe as a reusable skill.

What’s in here

Each contribution lives under plugins/ as its own directory. A contribution is either a full Claude Code plugin (with a .claude-plugin/plugin.json manifest) or a bare skill collection (one or more SKILL.md directories). Current contributions:

skill-iterations/ is a teaching example: the same brain-extraction skill captured at six successive stages of refinement, showing how a raw draft is hardened into a reliable skill (fixing YAML, removing “Claude-isms”, generalizing across tools, improving QC). Read it to see what “good” looks like before you write your own.

Skill anatomy