skills/phase-preregistration/SKILL.md
Phase guidance for the neuroflow /preregistration command. Loaded automatically when /preregistration is invoked to orient agent behavior, relevant skills, and workflow hints for creating and managing pre-registration documents.
npx skillsauth add stanislavjiricek/neuroflow phase-preregistrationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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The preregistration phase produces and manages pre-registration documents that commit the research team to a specific hypothesis, design, and analysis plan before data collection begins.
.neuroflow/ideation/ if it exists; pull paradigm details from .neuroflow/experiment/ if it exists — do not ask the user to repeat information already in project memoryneuroflow:neuroflow-core — read first; defines the command lifecycle and .neuroflow/ write rulesprereg-[registry]-[date].md — the primary pre-registration document; version-control this fileprereg-review-[date].md — completeness and consistency review reportdeviations.md — running log of post-registration deviations; append only, never overwrite past entriesregistered-report.md — metadata for any submitted or accepted registered reports (DOI, stage, date)testing
This skill should be used whenever the user mentions BIDS, Brain Imaging Data Structure, BIDS conversion, BIDS validation, BIDS compliance, organizing neuroimaging data, dataset_description.json, participants.tsv, bids-validator, pybids, MNE-BIDS, or asks how to structure EEG/MEG/fMRI/iEEG/PET/DWI data for sharing or preprocessing. Also invoke when the user asks how to name scan files, what sidecar JSON fields are needed, how to set up derivatives/, or how to run fMRIPrep/MRIQC on their dataset. Invoke proactively during /data, /data-preprocess, and /data-analyze phases whenever the dataset structure is relevant to the task at hand.
tools
Phase guidance for the /meeting command. Covers meeting file structure, recurring templates, attendee resolution from profiles, Google Calendar MCP integration, agenda preparation with project context, and action-item-to-task conversion at all three levels (project, flowie, hive).
data-ai
Worker-critic agentic loop protocol — orchestrator coordinates a worker agent and a critic agent across up to 3 revision cycles to produce a vetted output for any phase.
development
Knowledge base skill — Karpathy-style LLM-maintained wiki at three levels (personal/flowie, project, team/hive). Handles ingest, query, lint, schema, and project-tagging workflows. Invoked by /flowie --wiki-* (personal), /wiki (project), /hive --wiki-* (team).