neuroskill-recipes

# NeuroSkill Use-Case Recipes --- ## Focus & Productivity ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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)**. ```bash # 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 $(date +%s) # The CLI always prints exact timestamps when auto-selecting — copy the rerun: line: npx neuroskill sleep # → rerun: npx neuroskill sleep --start 1740380100 --end 1740415510 ``` --- ## Automation & Scripting ```bash # ── Cron / scheduled polling ────────────────────────────────────────────── # Every 5 minutes: log focus score to a CSV */5 * * * * node /path/to/npx neuroskill status --json \ | jq -r '[now, .scores.focus, .scores.relaxation, .scores.hr] | @csv' \ >> ~/eeg_log.csv # ── Shell function wrappers ─────────────────────────────────────────────── neuroskill_focus() { npx neuroskill status --json | jq '.scores.focus'; } neuroskill_relax() { npx neuroskill status --json | jq '.scores.relaxation'; } neuroskill_tbr() { npx neuroskill status --json | jq '.scores.tbr'; } neuroskill_battery(){ npx neuroskill status --json | jq '.device.battery'; } ``` **Python polling:** ```python import subprocess, json, time def neuroskill(cmd): r = subprocess.run( ["node", "neuroskill", *cmd.split(), "--json"], capture_output=True, text=True ) return json.loads(r.stdout) while True: data = neuroskill("status") focus = data["scores"]["focus"] print(f"Focus: {focus:.2f}") if focus < 0.35: neuroskill(f'notify "Focus dropped" "Current: {focus:.2f}"') time.sleep(30) ``` **Python HTTP (no Node required):** ```python import requests PORT = 8375 def neuroskill(command, **kwargs): return requests.post( f"http://127.0.0.1:{PORT}/", json={"command": command, **kwargs} ).json() status = neuroskill("status") print("Focus:", status["scores"]["focus"]) print("Battery:", status["device"]["battery"], "%") sessions = neuroskill("sessions") sleep = neuroskill("sleep", start_utc=sessions["sessions"][0]["start_utc"], end_utc=sessions["sessions"][0]["end_utc"]) print("N3 sleep:", sleep["summary"]["n3_epochs"], "epochs") ``` **Node.js HTTP polling:** ```javascript const PORT = 8375; const neuroskill = (cmd) => fetch(`http://127.0.0.1:${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 ```bash # 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"?