neuroskill-recipes

name	neuroskill-recipes
description	NeuroSkill use-case recipes and scripting patterns — shell snippets for focus and productivity monitoring, stress tracking, sleep quality analysis, cognitive load queries, meditation tracking, cross-modal graph search, A/B session comparison, time-range queries, and automation with cron/Python/Node.js/HTTP. Use when looking for practical examples or building automation pipelines.

NeuroSkill Use-Case Recipes

---

Focus & Productivity

# Current focus level:
npx neuroskill status --json | jq '.scores.focus'

# Is alpha suppressed? (good focus = low alpha)
npx neuroskill status --json | jq '.scores.bands.rel_alpha'

# Focus trend across today's session:
npx neuroskill session 0 --json | jq '{focus_avg: .metrics.focus, trend: .trends.focus, first_half: .first.focus, second_half: .second.focus}'

# Beta/alpha ratio — high = alert/focused, very high = stressed:
npx neuroskill status --json | jq '.scores.bar'

# Check spectral centroid — rises with cognitive load:
npx neuroskill status --json | jq '.scores.spectral_centroid'

# Compare a morning session vs an afternoon session:
npx neuroskill compare \
  --a-start 1740380100 --a-end 1740382665 \
  --b-start 1740412800 --b-end 1740415510 \
  --json | jq '.insights.deltas.focus'

# Find all moments in history that look like deep focus:
npx neuroskill search --start $(npx neuroskill sessions --json | jq '.sessions[0].start_utc') \
                   --end   $(npx neuroskill sessions --json | jq '.sessions[0].end_utc') \
                   --json | jq '.result.analysis.neighbor_metrics.focus'

# Label a focus block for later retrieval:
npx neuroskill label "deep focus block — no distractions"

# Search all prior labeled focus moments:
npx neuroskill search-labels "deep focus" --k 10

# Alert when focus drops — poll every 30 seconds:
while true; do
  F=$(npx neuroskill status --json | jq '.scores.focus')
  if (( $(echo "$F < 0.35" | bc -l) )); then
    npx neuroskill notify "Focus low" "Current: $F — take a break?"
  fi
  sleep 30
done

---

Stress

# LF/HF ratio — high = sympathetic dominance (stress):
npx neuroskill status --json | jq '.scores.lf_hf_ratio'

# Composite stress index from PPG:
npx neuroskill session 0 --json | jq '.metrics.stress_index'

# FAA — negative = frontal alpha withdrawal:
npx neuroskill status --json | jq '.scores.faa'

# Frontal beta elevation (arousal marker):
npx neuroskill status --json | jq '[.scores.bar, .scores.faa, .scores.lf_hf_ratio]'

# Compare stress markers across two sessions:
npx neuroskill compare --json | jq '.insights.deltas | {faa_delta: .faa, stress_hr: .hr, lf_hf: .lf_hf_ratio}'

# HRV breakdown (low rmssd = stress):
npx neuroskill session 0 --json | jq '{rmssd: .metrics.rmssd, sdnn: .metrics.sdnn, pnn50: .metrics.pnn50}'

# Label a stressful event:
npx neuroskill label "stressful presentation — racing thoughts"

# Find neurally similar stressful moments in history:
npx neuroskill search-labels "stress overwhelmed" --mode both --k 10

---

Sleep Quality

# Last night's sleep summary:
npx neuroskill sleep --json | jq '.summary'

# Deep sleep percentage (N3 — most restorative):
npx neuroskill sleep --json | jq '(.summary.n3_epochs / .summary.total_epochs * 100 | round | tostring) + "% N3"'

# REM percentage:
npx neuroskill sleep --json | jq '(.summary.rem_epochs / .summary.total_epochs * 100 | round | tostring) + "% REM"'

# Full analysis (efficiency, onset, transitions):
npx neuroskill sleep --json | jq '.analysis'

# Sleep for a specific session:
npx neuroskill sleep 0

# Wakefulness and drowsiness during the day:
npx neuroskill status --json | jq '{drowsiness: .scores.drowsiness, wakefulness: .consciousness.wakefulness}'

# 48h sleep summary from status:
npx neuroskill status --json | jq '.sleep'

---

Cognitive Load

# Raw TBR (theta/beta ratio) — healthy ~1.0; elevated = reduced cortical arousal:
npx neuroskill status --json | jq '.scores.tbr'

# Cognitive load score (0–1):
npx neuroskill status --json | jq '.scores.cognitive_load'

# PAC theta-gamma — working memory coupling:
npx neuroskill status --json | jq '.scores.pac_theta_gamma'

# Sample entropy — lower = more regular/predictable signal:
npx neuroskill session 0 --json | jq '.metrics.sample_entropy'

# Full session trend for TBR and cognitive load:
npx neuroskill session 0 --json | jq '{tbr: .metrics.tbr, cog_load: .metrics.cognitive_load, tbr_trend: .trends.tbr}'

# Watch TBR in real time (lower is better for focus):
while true; do
  npx neuroskill status --json | jq '{tbr: .scores.tbr, focus: .scores.focus}'
  sleep 10
done

---

Meditation & Relaxation

# Current meditation score:
npx neuroskill status --json | jq '.scores.meditation'

# Alpha peak frequency — rises during deep relaxation:
npx neuroskill status --json | jq '.scores.apf'

# Theta elevation (meditative absorption):
npx neuroskill status --json | jq '.scores.bands.rel_theta'

# Full session meditation trend:
npx neuroskill session 0 --json | jq '{meditation: .metrics.meditation, relaxation: .metrics.relaxation, trend: .trends.meditation}'

# Complexity during meditation (lower = more ordered):
npx neuroskill session 0 --json | jq '{perm_entropy: .metrics.permutation_entropy, sample_entropy: .metrics.sample_entropy}'

# Label meditation milestones:
npx neuroskill label "entered theta meditation state"
npx neuroskill label "meditation ended — felt deeply rested"

# Find all prior meditation sessions:
npx neuroskill search-labels "meditation" --mode both --k 20

# Compare a meditation session to a work session:
npx neuroskill compare \
  --a-start <meditation_start> --a-end <meditation_end> \
  --b-start <work_start> --b-end <work_end> \
  --json | jq '.insights.deltas | {relaxation, meditation: .meditation, alpha: .rel_alpha}'

---

Cross-Modal Graph Search

# Find concepts related to "deep focus" across all data layers:
npx neuroskill interactive "deep focus"

# Increase reach to capture labels up to 30 minutes from each EEG point:
npx neuroskill interactive "deep focus" --reach 30

# More neighbors at each layer for a richer graph:
npx neuroskill interactive "meditation" --k-text 8 --k-eeg 8 --k-labels 5 --reach 20

# What text labels are semantically closest to "low energy"?
npx neuroskill interactive "low energy" --json | jq '[.nodes[] | select(.kind == "text_label") | {text, sim: (1 - .distance | . * 100 | round)}]'

# What nearby labels cluster around EEG moments found via "stress"?
npx neuroskill interactive "stress" --json | jq '[.nodes[] | select(.kind == "found_label") | .text]'

# Count discovered nodes by layer:
npx neuroskill interactive "flow state" --json | jq '[.nodes | group_by(.kind)[] | {(.[0].kind): length}] | add'

# Visualize the graph (requires graphviz):
npx neuroskill interactive "deep focus" --dot | dot -Tsvg -o focus_graph.svg && open focus_graph.svg
npx neuroskill interactive "meditation" --dot | dot -Tpng -o meditation_graph.png

---

Comparing Two Sessions

# Auto: last 2 sessions:
npx neuroskill compare

# Get timestamps then compare explicitly:
npx neuroskill sessions --json | jq '.sessions[:2] | [.[].start_utc, .[].end_utc]'

npx neuroskill compare \
  --a-start 1740380100 --a-end 1740382665 \
  --b-start 1740412800 --b-end 1740415510

# Which metrics improved?
npx neuroskill compare --json | jq '.insights.improved'
npx neuroskill compare --json | jq '.insights.declined'

# Full delta table sorted by change:
npx neuroskill compare --json | jq '.insights.deltas | to_entries | sort_by(.value.pct) | reverse'

# 3D UMAP — how spatially separated are the two sessions?
npx neuroskill umap \
  --a-start 1740380100 --a-end 1740382665 \
  --b-start 1740412800 --b-end 1740415510 \
  --json | jq '.result.analysis.separation_score'

---

Time-Range Queries

All commands that accept --start and --end use Unix seconds (UTC).

# Get timestamps from the session list:
npx neuroskill sessions --json | jq '.sessions[0] | {start: .start_utc, end: .end_utc}'

# Convert a human date to Unix seconds:
date -j -f "%Y-%m-%d %H:%M" "2026-02-24 08:00" +%s      # macOS
date -d "2026-02-24 08:00" +%s                            # Linux

# Last 2 hours:
NOW=$(date +%s)
npx neuroskill sleep --start $((NOW - 7200)) --end $NOW

# Today midnight to now:
TODAY=$(date -j -v0H -v0M -v0S +%s 2>/dev/null || date -d "today 00:00" +%s)
npx neuroskill sleep --start $TODAY --end $(date +%s)

# The CLI always prints exact timestamps when auto-selecting — copy the rerun: line:
npx neuroskill sleep
# → rerun: npx neuroskill sleep --start 1740380100 --end 1740415510

---

Automation & Scripting

# ── Cron / scheduled polling ──────────────────────────────────────────────
# Every 5 minutes: log focus score to a CSV
*/5 * * * * node /path/to/npx neuroskill status --json \
  | jq -r '[now, .scores.focus, .scores.relaxation, .scores.hr] | @csv' \
  >> ~/eeg_log.csv

# ── Shell function wrappers ───────────────────────────────────────────────
neuroskill_focus()  { npx neuroskill status --json | jq '.scores.focus'; }
neuroskill_relax()  { npx neuroskill status --json | jq '.scores.relaxation'; }
neuroskill_tbr()    { npx neuroskill status --json | jq '.scores.tbr'; }
neuroskill_battery(){ npx neuroskill status --json | jq '.device.battery'; }

Python polling:

import subprocess, json, time

def neuroskill(cmd):
    r = subprocess.run(
        ["node", "neuroskill", *cmd.split(), "--json"],
        capture_output=True, text=True
    )
    return json.loads(r.stdout)

while True:
    data = neuroskill("status")
    focus = data["scores"]["focus"]
    print(f"Focus: {focus:.2f}")
    if focus < 0.35:
        neuroskill(f'notify "Focus dropped" "Current: {focus:.2f}"')
    time.sleep(30)

Python HTTP (no Node required):

import requests

PORT = 8375

def neuroskill(command, **kwargs):
    return requests.post(
        f"http://127.0.0.1:{PORT}/",
        json={"command": command, **kwargs}
    ).json()

status = neuroskill("status")
print("Focus:", status["scores"]["focus"])
print("Battery:", status["device"]["battery"], "%")

sessions = neuroskill("sessions")
sleep = neuroskill("sleep",
    start_utc=sessions["sessions"][0]["start_utc"],
    end_utc=sessions["sessions"][0]["end_utc"])
print("N3 sleep:", sleep["summary"]["n3_epochs"], "epochs")

Node.js HTTP polling:

const PORT = 8375;
const neuroskill = (cmd) =>
  fetch(`http://127.0.0.1:${PORT}/`, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify(cmd),
  }).then(r => r.json());

setInterval(async () => {
  const { scores } = await neuroskill({ command: "status" });
  console.log(`focus=${scores.focus.toFixed(2)} relax=${scores.relaxation.toFixed(2)} hr=${scores.hr.toFixed(1)}`);
}, 5000);

---

---

Cross-Modal Graph Search

# Basic: find concepts related to "deep focus" across all data layers:
npx neuroskill interactive "deep focus"

# Increase reach to capture labels up to 30 minutes from each EEG point:
npx neuroskill interactive "deep focus" --reach 30

# More neighbors at each layer for a richer graph:
npx neuroskill interactive "meditation" --k-text 8 --k-eeg 8 --k-labels 5 --reach 20

# What text labels are semantically closest to "anxiety"?
npx neuroskill interactive "anxiety" --json | jq '[.nodes[] | select(.kind == "text_label") | {text, sim: (1 - .distance | . * 100 | round)}]'

# What nearby labels cluster around EEG moments found via "stress"?
npx neuroskill interactive "stress" --json | jq '[.nodes[] | select(.kind == "found_label") | .text]'

# Count total discovered nodes by layer:
npx neuroskill interactive "flow state" --json | jq '[.nodes | group_by(.kind)[] | {(.[0].kind): length}] | add'

# Visualize the graph (requires graphviz):
npx neuroskill interactive "deep focus" --dot | dot -Tsvg -o focus_graph.svg
npx neuroskill interactive "meditation" --dot | dot -Tpng -o meditation_graph.png

# Chain with search-labels to verify what's in the text index first:
npx neuroskill search-labels "deep focus" --k 5 --json | jq '.results[].text'
npx neuroskill interactive "deep focus" --k-text 5 --k-eeg 5

---

Research use only. Sleep staging, consciousness metrics, neurological correlate indices, and all derived scores are research biomarkers and experimental indicators. They are not validated medical devices and must not be used for diagnosis or clinical decision-making.