skills/phase-fails/SKILL.md
Phase guidance for the neuroflow /fails command. Orients agent approach for logging user dissatisfaction, categorising complaints accurately, and preparing GitHub issue reports.
npx skillsauth add stanislavjiricek/neuroflow phase-failsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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The /fails command is a dissatisfaction capture utility. Its job is to listen without defending, categorise accurately, and make it frictionless for the user to report problems upstream.
/fails — record the problem as stated; fixes belong in a follow-up conversationcore.md — Plugin behaviourProblems with how neuroflow itself behaves:
science.md — Scientific qualityProblems with the scientific work produced:
ux.md — Interaction qualityProblems with how the interaction felt:
After logging a fail, always ask whether the user wants to report it as a GitHub issue. Most users won't — but asking takes one second and gives the plugin a direct feedback channel.
When composing the issue:
[core], [science], or [ux]project_config.mdnode -e "process.stdout.write(encodeURIComponent('...'))") — probe once; if unavailable, encode manually (space→%20, newline→%0A, #→%23, &→%26, =→%3D, ?→%3F, +→%2B, /→%2F, :→%3A). Never use gh CLI — it requires authentication and is not needed here.open (macOS/Linux) or start "" "<url>" (Windows) rather than presenting it as text — the user should be one click from submittingneuroflow:neuroflow-core — read first; defines the command lifecycle and .neuroflow/ write rules/neuroflow:fails — runs this workflow as a slash command.
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).