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neuroskill-evidence

Track NeuroSkill interventions and life events with px labels for outcome metrics.

11|4|Updated Mar 3, 2026
Search Tags:#life-events#evidence-collection#neurofeedback#px-labels#intervention-tracking#protocol-ranking
One-click install
npx skills add https://github.com/NeuroSkill-com/skills --skill neuroskill-evidence
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neuroskill-evidence
Source: https://github.com/NeuroSkill-com/skills/tree/main/skills/neuroskill-evidence
Command: npx skills add https://github.com/NeuroSkill-com/skills --skill neuroskill-evidence

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides consistent, invisible evidence collection so every NeuroSkill protocol, hook, or life event yields structured px data that reveals what interventions actually shift the user’s EEG and wellbeing metrics.

Core Features & Use Cases

  • Automated px Labeling: Defines how to snapshot baselines, label px:start/px:end events with modality, trigger, and delta context, and infer outcomes for every intervention without bothering the user.
  • Life Event & Hook Awareness: Captures notes, skips, auto-triggers, and contextual life events so the evidence graph reflects coffee, walks, meetings, and hook responses alongside formal protocols.
  • Personal Ranking & Selection Rules: Aggregates outcomes to compute success rates, modality preferences, time-of-day trends, and retirement thresholds, guiding the LLM to offer interventions backed by individual history.

Quick Start

Ask the assistant to capture status, label px:start, run the protocol, and label px:end with the computed deltas so the evidence log knows how that intervention performed.

Frequently Asked Questions about neuroskill-evidence

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track intervention effectiveness using EEG and life event data?▼

Intervention effectiveness tracking works by capturing baseline and outcome metrics with px labels for every neurofeedback protocol and life event. The system computes deltas between px:start and px:end events to reveal what actually shifts EEG and wellbeing metrics.

What is evidence-driven personalization for neurofeedback protocols?▼

Evidence-driven personalization applies aggregated outcome data from px labels to rank intervention protocols. It calculates success rates, modality preferences, and time-of-day trends so recommendations are backed by individual history rather than generic rules.

How do I capture contextual life events like caffeine or walks alongside formal protocols?▼

Contextual life events are captured through life event and hook awareness features. The system logs notes, auto-triggers, and skips alongside formal protocols so the evidence graph reflects real-world factors like coffee, walks, and meetings automatically.

Can I automate outcome collection so users do not have to manually log every session?▼

Automated outcome collection is supported through invisible evidence tracking. The system snapshots baselines, labels px:start and px:end events with modality and trigger context, and infers outcomes without bothering the user during their protocol.

How do I rank and retire interventions based on personal history?▼

Intervention ranking aggregates px outcome data to compute success rates and retirement thresholds. Selection rules apply these heuristics to guide the LLM toward interventions backed by individual history and retire underperforming protocols.

Does intervention tracking work without requiring additional dependencies?▼

Intervention tracking operates independently with no external dependencies. It applies rules for required context fields, outcome heuristics, and aggregation strategies directly to EEG status queries and label searches within the NeuroSkill environment.

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