feat: design system overhaul — sidebar, AI chats, settings, brainstorm, color cleanup

- Sidebar: dynamic brand-accent colors, brainstorm section restyled
- AI chat general: popup panel with expand/collapse, hides when contextual AI open
- AI chat contextual: tabs reordered (Actions first), X close button, height fix
- Settings: all tabs restyled, 6 new color presets (sage, terracotta, iron, etc.)
- Global color cleanup: emerald/orange hardcoded → brand-accent dynamic
- Brainstorm page: orange → brand-accent throughout
- PageEntry animation component added to key pages
- Floating AI button: bg-brand-accent instead of hardcoded black
- i18n: all 15 locales updated with new AI/billing keys
- Billing: freemium quota tracking, BYOK, stripe subscription scaffolding
- Admin: integrated into new design
- AGENTS.md + CLAUDE.md project rules added

.agents/skills/suno-feedback-elicitor/scripts/batch-full-analysis.py

@@ -0,0 +1,380 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# dependencies = ["librosa>=0.10", "numpy>=1.24"]
+# ///
+"""
+Batch full analysis -- tempo stability, energy arc, section boundaries,
+and spectral balance for every track in a catalog directory.
+
+Outputs a summary report in JSON or Markdown text format.
+
+Exit codes:
+  0 = analysis completed successfully
+  1 = invalid arguments or no audio files found
+  2 = missing dependencies (librosa/numpy)
+"""
+
+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent / "_shared"))
+from audio_deps import require_audio_deps
+from companion_writer import update_companion, resolve_companion_path
+from json_archiver import resolve_archive_arg, write_archive
+
+SCRIPT_NAME = "batch-full-analysis"
+
+
+def format_time(seconds):
+    m = int(seconds // 60)
+    s = int(seconds % 60)
+    return f"{m}:{s:02d}"
+
+
+def analyze_track(filepath):
+    """Full analysis of a single track. Returns a dict of results."""
+    import librosa
+    import numpy as np
+
+    filename = os.path.basename(filepath)
+    results = {'file': filename}
+
+    try:
+        y, sr = librosa.load(filepath, sr=22050)
+        duration = librosa.get_duration(y=y, sr=sr)
+        results['duration'] = duration
+
+        # === BPM & TEMPO STABILITY ===
+        tempo_overall, beats = librosa.beat.beat_track(y=y, sr=sr)
+        bpm = float(tempo_overall[0]) if hasattr(tempo_overall, '__len__') else float(tempo_overall)
+        results['bpm'] = round(bpm, 1)
+
+        beat_times = librosa.frames_to_time(beats, sr=sr)
+        if len(beat_times) > 3:
+            ibis = np.diff(beat_times)
+            local_bpms = 60.0 / ibis
+            bpm_std = np.std(local_bpms)
+            results['bpm_stability'] = "steady" if bpm_std < 5 else "slight variation" if bpm_std < 15 else "TEMPO CHANGES"
+            results['bpm_range'] = (round(np.percentile(local_bpms, 10), 0), round(np.percentile(local_bpms, 90), 0))
+        else:
+            results['bpm_stability'] = "too few beats"
+            results['bpm_range'] = (0, 0)
+
+        # === KEY ===
+        pitch_classes = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
+        major_profile = np.array([6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88])
+        minor_profile = np.array([6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17])
+        chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
+        chroma_avg = np.mean(chroma, axis=1)
+        best_corr = -1
+        best_key = "Unknown"
+        for i in range(12):
+            rolled = np.roll(chroma_avg, -i)
+            for profile, mode in [(major_profile, "major"), (minor_profile, "minor")]:
+                corr = np.corrcoef(rolled, profile)[0, 1]
+                if corr > best_corr:
+                    best_corr = corr
+                    best_key = f"{pitch_classes[i]} {mode}"
+        results['key'] = best_key
+        results['key_conf'] = round(best_corr, 3)
+
+        # === ENERGY ARC ===
+        rms = librosa.feature.rms(y=y)[0]
+        hop_length = 512
+        max_rms = np.max(rms) if np.max(rms) > 0 else 1
+
+        # 5-second windows for energy
+        window_frames = int(5 * sr / hop_length)
+        num_windows = len(rms) // window_frames
+        energies = []
+        for i in range(num_windows):
+            avg = np.mean(rms[i*window_frames:(i+1)*window_frames])
+            pct = int((avg / max_rms) * 100)
+            energies.append(pct)
+
+        results['energy_min'] = min(energies) if energies else 0
+        results['energy_max'] = max(energies) if energies else 0
+        results['energy_range'] = results['energy_max'] - results['energy_min']
+
+        # Detect significant energy shifts
+        shifts = []
+        for i in range(1, len(energies)):
+            diff = energies[i] - energies[i-1]
+            if abs(diff) > 20:
+                t = i * 5
+                direction = "UP" if diff > 0 else "DOWN"
+                shifts.append(f"{format_time(t)} {direction} {abs(diff)}%")
+        results['energy_shifts'] = shifts
+        results['energy_profile'] = energies
+
+        # Classify dynamic character
+        if results['energy_range'] < 20:
+            results['dynamic_character'] = "FLAT — minimal dynamics"
+        elif results['energy_range'] < 40:
+            results['dynamic_character'] = "MODERATE — some dynamic range"
+        elif len(shifts) >= 3:
+            results['dynamic_character'] = "HIGHLY DYNAMIC — big swings"
+        else:
+            results['dynamic_character'] = "DYNAMIC — wide range"
+
+        # === SPECTRAL BALANCE ===
+        S = np.abs(librosa.stft(y))
+        freqs = librosa.fft_frequencies(sr=sr)
+        low_mask = freqs < 250
+        mid_mask = (freqs >= 250) & (freqs < 2000)
+        high_mask = freqs >= 2000
+
+        low = np.mean(S[low_mask, :])
+        mid = np.mean(S[mid_mask, :])
+        high = np.mean(S[high_mask, :])
+        total = low + mid + high
+        if total == 0:
+            total = 1
+        results['spectral_low'] = int(low / total * 100)
+        results['spectral_mid'] = int(mid / total * 100)
+        results['spectral_high'] = int(high / total * 100)
+
+        # === SECTION BOUNDARIES ===
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+        n_sections = min(8, max(3, int(duration / 30)))  # Scale sections by duration
+        bounds = librosa.segment.agglomerative(mfcc, k=n_sections)
+        bound_times = librosa.frames_to_time(bounds, sr=sr)
+        results['sections'] = [format_time(t) for t in bound_times if t > 0.5]
+
+    except Exception as e:
+        results['error'] = str(e)
+
+    return results
+
+
+def format_json(all_results):
+    """Format results as standard module JSON."""
+    tracks = []
+    for r in all_results:
+        if 'error' in r:
+            tracks.append({
+                'file': r['file'],
+                'status': 'error',
+                'error': r['error'],
+            })
+            continue
+        tracks.append({
+            'file': r['file'],
+            'duration': round(r['duration'], 1),
+            'duration_display': format_time(r['duration']),
+            'bpm': r['bpm'],
+            'bpm_stability': r['bpm_stability'],
+            'bpm_range': list(r['bpm_range']),
+            'key': r['key'],
+            'key_confidence': r['key_conf'],
+            'dynamic_character': r['dynamic_character'],
+            'energy': {
+                'min': r['energy_min'],
+                'max': r['energy_max'],
+                'range': r['energy_range'],
+                'shifts': r['energy_shifts'],
+                'profile': r['energy_profile'],
+            },
+            'spectral_balance': {
+                'low_pct': r['spectral_low'],
+                'mid_pct': r['spectral_mid'],
+                'high_pct': r['spectral_high'],
+            },
+            'sections': r['sections'],
+        })
+
+    return json.dumps({
+        'script': 'batch-full-analysis',
+        'status': 'ok',
+        'track_count': len(all_results),
+        'tracks': tracks,
+    }, indent=2)
+
+
+def format_text(all_results):
+    """Format results as a Markdown report."""
+    lines = []
+    lines.append("# Catalog Audio Analysis\n")
+    lines.append("## Summary Table\n")
+    lines.append("| Track | Duration | BPM | Stability | Key | Dyn Range | Character |")
+    lines.append("|-------|----------|-----|-----------|-----|-----------|----------|")
+    for r in all_results:
+        if 'error' in r:
+            continue
+        dur = format_time(r['duration'])
+        lines.append(
+            f"| {r['file'].replace('.mp3','')} | {dur} | {r['bpm']} "
+            f"| {r['bpm_stability']} | {r['key']} | {r['energy_range']}% "
+            f"| {r['dynamic_character']} |"
+        )
+
+    lines.append("\n## Energy Shifts (>20% jumps)\n")
+    for r in all_results:
+        if 'error' in r or not r.get('energy_shifts'):
+            continue
+        lines.append(f"### {r['file'].replace('.mp3','')}")
+        for shift in r['energy_shifts']:
+            lines.append(f"- {shift}")
+        lines.append("")
+
+    lines.append("\n## Section Boundaries\n")
+    lines.append("| Track | Sections |")
+    lines.append("|-------|----------|")
+    for r in all_results:
+        if 'error' in r:
+            continue
+        sections = r.get('sections', [])
+        lines.append(f"| {r['file'].replace('.mp3','')} | {' / '.join(sections)} |")
+
+    lines.append("\n## Spectral Balance\n")
+    lines.append("| Track | Low (<250Hz) | Mid (250-2kHz) | High (>2kHz) |")
+    lines.append("|-------|-------------|----------------|-------------|")
+    for r in all_results:
+        if 'error' in r:
+            continue
+        lines.append(
+            f"| {r['file'].replace('.mp3','')} | {r['spectral_low']}% "
+            f"| {r['spectral_mid']}% | {r['spectral_high']}% |"
+        )
+
+    return "\n".join(lines) + "\n"
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Batch audio analysis: tempo, energy, sections, spectral balance."
+    )
+    parser.add_argument(
+        "--audio-dir", default="docs/audio",
+        help="Directory containing .mp3 files (default: docs/audio)",
+    )
+    parser.add_argument(
+        "--format", choices=["json", "text"], default="json",
+        help="Output format (default: json)",
+    )
+    parser.add_argument(
+        "-o", "--output",
+        help="Output file path (default: stdout)",
+    )
+    parser.add_argument(
+        "--archive", nargs="?", const="", default="",
+        help=(
+            "Persist full JSON output to a dated catalog archive. "
+            "With no path: writes to docs/audio-analysis/catalog/<YYYY-MM-DD>-deep.json. "
+            "Pass an explicit path to override. Default: ON."
+        ),
+    )
+    parser.add_argument(
+        "--no-archive", dest="archive", action="store_const", const=None,
+        help="Skip writing the JSON archive.",
+    )
+    parser.add_argument(
+        "--companion", nargs="?", const="", default="",
+        help=(
+            "Refresh the canonical Markdown companion file. "
+            "With no path: writes to docs/catalog-analysis-report.md. "
+            "Pass an explicit path to override. Default: ON."
+        ),
+    )
+    parser.add_argument(
+        "--no-companion", dest="companion", action="store_const", const=None,
+        help="Skip refreshing the Markdown companion file.",
+    )
+    args = parser.parse_args()
+
+    require_audio_deps()
+    import librosa  # noqa: F401
+    import numpy as np  # noqa: F401
+
+    audio_dir = args.audio_dir
+    if not os.path.isdir(audio_dir):
+        print(json.dumps({
+            "script": "batch-full-analysis",
+            "status": "fail",
+            "error": f"Audio directory not found: {audio_dir}",
+        }), file=sys.stderr)
+        sys.exit(1)
+
+    mp3s = sorted([
+        os.path.join(audio_dir, f)
+        for f in os.listdir(audio_dir)
+        if f.endswith('.mp3')
+    ])
+
+    if not mp3s:
+        print(json.dumps({
+            "script": "batch-full-analysis",
+            "status": "fail",
+            "error": f"No .mp3 files found in: {audio_dir}",
+        }), file=sys.stderr)
+        sys.exit(1)
+
+    print(f"Analyzing {len(mp3s)} tracks...\n", file=sys.stderr)
+
+    all_results = []
+    for filepath in mp3s:
+        print(f"  Processing: {os.path.basename(filepath)}...", end="", flush=True, file=sys.stderr)
+        result = analyze_track(filepath)
+        all_results.append(result)
+        if 'error' in result:
+            print(f" ERROR: {result['error']}", file=sys.stderr)
+        else:
+            print(f" done ({result['bpm']} BPM, {result['key']}, {result['dynamic_character']})", file=sys.stderr)
+
+    # Format output
+    if args.format == "json":
+        output = format_json(all_results)
+    else:
+        output = format_text(all_results)
+
+    # Write output
+    if args.output:
+        with open(args.output, 'w') as f:
+            f.write(output)
+        print(f"\nReport saved to: {args.output}", file=sys.stderr)
+    else:
+        print(output)
+
+    # JSON archive (default ON unless --no-archive). Identifier suffix "-deep"
+    # to distinguish from analyze-audio.py's lighter summary archive.
+    from datetime import datetime, timezone
+    today = datetime.now(timezone.utc).strftime("%Y-%m-%d") + "-deep"
+    archive_target = resolve_archive_arg("catalog", today, args.archive)
+    if archive_target is not None:
+        try:
+            json_data = json.loads(format_json(all_results))
+        except Exception as exc:
+            print(f"  WARN: archive skipped — JSON build failed: {exc}", file=sys.stderr)
+        else:
+            res = write_archive(archive_target, json_data)
+            print(f"  ARCHIVED: {res['path']} ({res['bytes_written']} bytes)", file=sys.stderr)
+
+    # Companion .md refresh (default ON unless --no-companion).
+    # Title + timestamp live INSIDE the AUTOGEN markers so each refresh
+    # updates them. Hand-curated sections in the companion file live
+    # outside the markers and are preserved.
+    companion_target = resolve_companion_path(SCRIPT_NAME, args.companion)
+    if companion_target is not None:
+        timestamp = datetime.now(timezone.utc).isoformat()
+        title_block = (
+            "# Catalog Audio Analysis — Full\n"
+            f"_Generated by `{SCRIPT_NAME}` on {timestamp}_\n\n"
+        )
+        body_lines = format_text(all_results).split("\n")
+        cut = 0
+        while cut < len(body_lines):
+            line = body_lines[cut]
+            if line.startswith("##") or (line.strip() and not line.startswith("#")):
+                break
+            cut += 1
+        md_body = title_block + "\n".join(body_lines[cut:])
+        res = update_companion(companion_target, SCRIPT_NAME, md_body)
+        print(f"  COMPANION: {res['status']} {res['path']} ({res['bytes_written']} bytes)", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()