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Genre Audit

Checks your existing genre tags against Discogs, Beatport, and audio evidence. Only conflicts and questionable tags are surfaced — tracks where the evidence confirms the genre are skipped. Every change needs your approval.

Paste into your agent:

Audit my existing genre tags.

When to audit

Section titled “When to audit”
  • After genre classification — catch any misclassifications
  • When you suspect a genre was applied too broadly (e.g. everything tagged “Techno”)
  • After importing tracks from another collection with different conventions
  • Periodically, as enrichment data improves

How it works

Section titled “How it works”

The audit uses the same decision tree as classification, but compares results against existing tags instead of filling blanks. Only conflicts and tracks needing manual review show up.

Audit vs Classification: Classification assigns genres to ungenred tracks. Audit verifies genres that are already there. Classification shows everything; audit only shows problems.

Prerequisites

Section titled “Prerequisites”

All enrichment providers should be at 100% cache coverage. If you’re following Library Cleanup, Session 2 (enrichment) handles this.

Step-by-step

Section titled “Step-by-step”

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).

Next steps

Section titled “Next steps”