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Genre Audit

Audit existing genre tags across a Rekordbox collection using cached enrichment and audio evidence.

Agent prompt

Section titled “Agent prompt”

Paste into your agent to start:

Audit my existing genre tags.

Constraints

  • Taxonomy is compiled. The alias map in genre.rs cannot be changed at runtime. If the user disagrees with a mapping, the agent works around it by using the user’s preferred genre directly.
  • No auto-tagging. Every genre change requires explicit human approval.
  • Cache-first. Audit runs from cached data. If cache is empty for a track, it is flagged as insufficient evidence — do not trigger enrichment mid-audit.
  • XML export only. No direct DB writes. All changes flow through update_tracks → preview_changes → write_xml.

Prerequisites

cache_coverage(has_genre=true)

All providers must be at 100% before proceeding. If not, hydrate first:

analyze_audio_batch(has_genre=true, skip_cached=true, max_tracks=200)
enrich_tracks(has_genre=true, skip_cached=true, providers=["discogs"], max_tracks=50)
enrich_tracks(has_genre=true, skip_cached=true, providers=["beatport"], max_tracks=50)

Repeat until all providers reach 100%. Report progress between batches.

Recommended: run suggest_normalizations() and stage alias fixes before auditing. This ensures the audit compares against canonical genre names, not aliases that inflate conflict counts.

Steps

1. Audit batch

audit_genres(max_tracks=50)

The server applies the genre decision tree to tracks that already have genres, comparing the evidence-based result against the current tag. Only conflicts and manual-review tracks appear in results.

Report the confidence distribution before review: “This batch: N high, N medium, N low, N insufficient out of N total.”

2. Review by confidence tier

Process each tier separately, highest confidence first.

High confidence

Present as a numbered list, grouped by suggested genre for scannability:

High confidence (12 conflicts):


→ Deep Techno (5):
 #1  Artist A — Track X | Techno → Deep Techno (Beatport: Raw/Deep, Discogs: Deep Techno x3)
 #2  Artist B — Track Y | Techno → Deep Techno (label: Ilian Tape, audio: atmospheric)
 ...


→ Tech House (3):
 #6  Artist C — Track Z | House → Tech House (Beatport: Tech House, Discogs: Tech House x2)
 ...

Ask user: “Approve all, or reject/change specific numbers.”

Stage approved changes.

Medium confidence

Same numbered-list format. Highlight evidence tensions where sources disagree.

Ask user: “Approve all, or reject/change specific numbers. Say ‘investigate #N’ for any you want more context on.”

For investigated tracks: use artist discography, label roster, and related tracks in the library to form a recommendation.

Stage approved changes.

Low confidence and manual review

These need per-track judgment. For each:

  1. Read the evidence and flags from the audit result
  2. Use artist reputation, label context, and track title to form a recommendation
  3. Present reasoning: Artist on Label — known for [context]. Evidence: [summary]. Recommend: GENRE because [reason].

Present in small groups (3–5) and ask user to approve/reject/change each.

Stage approved changes.

3. Paginate

Repeat Steps 1–2 with offset until all tracks are processed.

Report cumulative progress between batches: "Batch 3/N: M audited, X changes staged, Y confirmed."

4. Export

preview_changes()

Ask user: “Export these changes to XML?”

write_xml()

Report output path, then walk the user through the Rekordbox import:

  1. Add XML to Rekordbox — Open Preferences → Advanced → rekordbox xml → Imported Library → Browse → select the exported XML file.
  2. Open the XML view — In the sidebar, click the “Display rekordbox xml” icon. The imported tracks appear under “All Tracks”.
  3. Import into collection — Select all tracks (Cmd+A), right-click → Import To Collection. When prompted “Do you want to load information in the tag of the library being imported?”, click Yes (tick “Don’t ask me again” for bulk imports).