Set Building

Build ordered DJ set tracklists using transition scoring and beam search sequencing.

Agent prompt

Paste into your agent to start:

Build a DJ set from my collection.

Constraints

Read-only. Set building never modifies track metadata. It only creates playlist orderings.
Human controls the result. Agent proposes candidates. User picks, reorders, swaps. Export only what the user approves.
Cache-first. All transition scoring uses cached audio analysis and enrichment. No external API calls.
Multiple candidates. Always present at least 2 candidate orderings unless the pool is too small.

Prerequisites

cache_coverage()

All providers must be at 100%. If not, hydrate first (audio analysis, then enrichment).

Steps

1. Collect set parameters

Ask user for:

Duration — e.g., 60 min (~10-12 tracks), 90 min (~15-18 tracks)
Genre focus — specific genres, playlist, or “any”
BPM range — e.g., 120-135, or “flexible”
Energy curve — warmup→build→peak→release (default), flat, peak_only, or custom
Priority — balanced (default), harmonic, energy, or genre. Accepts a named preset string (e.g., "balanced"), a preset with overrides (e.g., {preset: "harmonic", overrides: {energy: 0.25}}), or fully custom weights with axes: key, bpm, energy, genre, brightness, rhythm. Custom presets can be saved with save_weight_preset and listed with list_weight_presets for reuse across sessions.
Starting track — optional seed track
Harmonic style — conservative / balanced (default) / adventurous
BPM drift tolerance — default 6%
BPM trajectory — optional start→peak BPM ramp (e.g., “start 122, peak at 130”)

Defaults: 60 min, balanced priority, warmup→build→peak→release, balanced harmonic, master tempo on, 6% drift.

Confirm parameters with user before proceeding.

2. Review play history

Check recent sessions to inform track selection:

get_sessions(limit=10)

Present sessions to user with date, track count, duration. Ask which were gigs vs practice, and how history should influence selection:

Avoid tracks from specific sessions?
Prefer battle-tested tracks (high play count)?
Prioritize unplayed tracks?
No preference?

If the user wants history-aware selection, check play stats for the candidate pool’s scope:

get_play_stats(genre="...", bpm_min=N, bpm_max=N, include_unplayed=true)

Use results to filter or annotate the candidate pool in the next step.

3. Build candidate pool

By genre/BPM search:

search_tracks(genre="...", bpm_min=N, bpm_max=N, limit=200)

Or from an existing playlist:

get_playlist_tracks(playlist_id="...")

Then resolve full data:

resolve_tracks_data(track_ids=[...], max_tracks=200)

Filter out tracks missing stratum_dsp analysis or outside BPM range.

Present pool summary: track count, genre breakdown, BPM range, key spread.

Ask user: “Proceed with this pool? / adjust filters / add from another playlist”

4. Generate candidates

build_set(
  track_ids=[...],
  target_tracks=12,
  start_track_id="...",
  priority="balanced",
  energy_curve="warmup_build_peak_release",
  beam_width=3,
  master_tempo=true,
  harmonic_style="balanced",
  bpm_drift_pct=6.0,
  bpm_range=[122, 130]
)

Present 2-3 candidates with: track order, key/BPM/genre per track, per-transition scores, energy curve visualization, overall score.

Ask user: “pick A / pick B / compare position # / regenerate / adjust parameters”

5. Refine selected set

Interactive editing commands:

Command	Action
`swap #N TrackID`	Replace track, re-score adjacent transitions
`move #N to #M`	Reorder, re-score affected transitions
`remove #N`	Remove track, re-score new adjacent pair
`insert TrackID after #N`	Add track, score both new transitions
`suggest #N`	Find best replacement using `query_transition_candidates`
`details #N`	Show full data for track at position N
`check`	Re-score and re-display full set
`done`	Finalize and proceed to export

For suggest #N, call:

query_transition_candidates(
  from_track_id="prev_track",
  pool_track_ids=[...remaining...],
  energy_phase="...",
  target_bpm=N,
  master_tempo=true,
  harmonic_style="balanced"
)

After each edit, use score_transition() to validate and show impact.

Ask user after each edit: “Continue editing? / done”

6. Export

write_xml(playlists=[{"name": "Set Name", "track_ids": [...]}])

Note: write_xml also exports and clears any staged metadata changes. Use preview_changes first if other workflows have pending edits.

Report output path. Remind user: File → Import Collection in Rekordbox.