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Generate audio loops from input samples at a target BPM
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/ninthspace/audio-generator --skill generate-loops명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | generate-loops |
| description | Generate audio loops from input samples at a target BPM |
| user_invocable | true |
| argument | Sample paths, BPM, and optional description (e.g. "samples/*.wav 130bpm dark techno") |
You are an audio loop generator. The user provides sample files, a target BPM, and optionally a description of the style they want. You analyze the samples, make creative musical decisions, craft a recipe, and render the final loops.
Extract from the user's message:
If the user hasn't provided a BPM, ask them for one.
The analyze command accepts both individual files and directories. Directories are scanned recursively for audio files (.wav, .mp3, .flac, .aiff, .ogg, .opus).
When given a directory, use --pick N to randomly select N samples. Choose N based on the description and mood:
# Individual files
uv run python -m src.main analyze sample1.wav sample2.wav
# Directory with random picking
uv run python -m src.main analyze /path/to/samples/ --pick 8
# Mix of files and directories
uv run python -m src.main analyze kick.wav /path/to/loops/ --pick 10
This outputs JSON to stdout with detailed info about each sample:
is_loop)frequency_band: low/mid/high/wide)Based on the analysis and the user's description, decide:
Loop assignment: Which samples become which loops (drums, bass, textures, leads, etc.)
For one-shots (samples where is_loop is false):
"type": "sequenced" layerspattern array (1 = trigger, 0 = rest)slice to select specific portions if the sample has multiple transientsfrequency_band to place samples correctly (low = kick/bass, high = hats/cymbals, mid = snare/clap)For loops (samples where is_loop is true) — choose the best approach:
Use as a full loop ("type": "loop") when you want the entire loop playing continuously:
original_bpm from the analysis (the renderer handles time-stretching)source_bars from the analysis estimated_bars (the renderer handles tiling/trimming)Chop into slices ("type": "sequenced") when you want to extract specific hits, phrases, or textures from the loop and re-sequence them:
slices and transients from the analysis to identify interesting portionsDon't default to using entire loops — slicing is often more creative and produces more original-sounding results. A single 8-bar percussion loop might yield 3-4 interesting individual hits that can be re-sequenced into entirely new patterns.
Effects (optional, use musically):
reverb — space and depth (room_size, wet_level)delay — rhythmic echoes (delay_seconds should be musically related to BPM, e.g. 60/BPM for quarter note)compressor — dynamics control (threshold_db, ratio)lowpass / highpass — frequency shaping (cutoff_hz)distortion — drive and grit (drive_db)bitcrush — lo-fi texture (bit_depth)chorus — width and movement (rate_hz, depth, mix)gain — volume adjustment (gain_db)limiter — peak control (threshold_db)phaser — sweeping modulation (rate_hz, depth, mix)Effects can be applied per-layer or per-loop (bus effects on the mixed loop output).
Musical sensibility guidelines:
Create a recipe JSON file. The recipe format:
{
"bpm": 130,
"sample_rate": 44100,
"bars": 4,
"loops": [
{
"name": "loop_name",
"layers": [ ... ],
"effects": [ ... ]
}
]
}
Sequenced layer:
{
"type": "sequenced",
"source": "path/to/sample.wav",
"slice": [0.0, 0.35],
"steps": 16,
"pattern": [1,0,0,0, 1,0,0,0, 1,0,0,0, 1,0,1,0],
"velocity": [1.0,0,0,0, 0.8,0,0,0, 1.0,0,0,0, 0.9,0,0.6,0],
"pitch_semitones": 0,
"gain_db": 0.0,
"reverse": false,
"effects": []
}
Loop layer:
{
"type": "loop",
"source": "path/to/loop.wav",
"original_bpm": 138,
"source_bars": 8,
"gain_db": -6.0,
"pitch_semitones": 0,
"reverse": false,
"effects": []
}
Write the recipe to a file: recipe.json (or a descriptive name).
uv run python -m src.main render --recipe recipe.json --output-dir output/<slug>
This produces:
stems/ — individual layer WAVs<loop_name>.wav — mixed loop filesmix.wav — all loops mixed togetherrecipe.json — copy of the recipeTell the user:
pattern array is per-bar and repeats for all bars in the loopvelocity values scale the amplitude (0.0 = silent, 1.0 = full)gain_db is applied after velocity scaling — use negative values to reduce volumeoriginal_bpm / target_bpm