Agent skill

Audio Track Production Workflow

by HKUDS in HKUDS/OpenSpace

Walks through producing a master audio track plus stems in Python, from checking a reference file and timing sections by BPM to effects, a zip archive and final verification.

MITAuto-check passedMedia & Creative

Install Audio Track Production Workflow

skills CLI
$ npx skills add HKUDS/OpenSpace --skill audio-track-production -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HKUDS/OpenSpace audio-track-production --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HKUDS/OpenSpace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/benchmarks/gdpval/skills/audio-track-production .claude/skills/audio-track-production && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
audio-track-production
GitHub stars
7.8k
Token cost
~2.9k tokens
SKILL.md length
257 words
Files
2
Skills in repo
199
Repo updated
First seen
Licence
MIT

At a glance

Walks through producing a master audio track plus stems in Python, from checking a reference file and timing sections by BPM to effects, a zip archive and final verification.

  • Works in 7 steps: Verify Reference File → Calculate Timing Parameters → Generate Stems with Explicit Sample Type → …
  • Producing a master track plus separate stems from a reference file
  • SKILL.md covers Overview, Step 1: Verify Reference File, Step 2: Calculate Timing… and Step 3: Generate Stems with…, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

This skill sets out a seven-step pattern for tasks that ask for a master track and several stems delivered as a verified archive. The agent first checks that the reference audio file exists and is readable, then derives beat-aligned section transition times from the tempo (BPM) and the total duration. Stems are generated with the sample type stated explicitly so bit depth stays consistent, and effects are applied with scipy.signal.

The remaining steps export the master and every stem with matching specifications using soundfile, package the deliverables into a zip file, and run a verification pass that compares each output with the expected specs and records passed and failed counts with details. A complete example script ties the steps together. The code in the skill is Python and relies on soundfile, NumPy and SciPy.

When your agent uses it

  • Producing a master track plus separate stems from a reference file
  • Aligning song section changes to a given BPM and duration
  • Packaging audio deliverables in a zip and verifying their specs

Example prompts

  • “Build a master track and drum, bass and synth stems from reference.wav, then zip and verify them.”
  • “Work out beat-aligned section transitions for a song with a known BPM and length.”
  • “Check that every exported stem has the same sample rate and bit depth as the master.”

Requirements

  • Python with soundfile, NumPy and SciPy

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Verify Reference File
  2. Calculate Timing Parameters
  3. Generate Stems with Explicit Sample Type
  4. Apply Effects via scipy.signal
  5. Export Master and Stems
  6. Archive Deliverables
  7. Verify All Outputs

What it can do on your machine

Read from SKILL.md and the folder at commit 3827781. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Audio Track Production Workflow loads about 2.9k tokens when it runs. Until then it costs about 27 tokens; SKILL.md has 257 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~27
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HKUDS/OpenSpace at commit 3827781, republished under its MIT licence (© HKUDS). 257 words, ~2,850 tokens.

Download SKILL.mdSave it as .claude/skills/audio-track-production/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
audio-track-production
description
End-to-end audio production workflow with stems, effects, archiving, and verification

Audio Track Production Workflow

This skill provides a reusable pattern for executing audio production tasks that require generating a master track and multiple stems, applying effects, and delivering verified outputs in an archive.

Overview

Follow these steps in order to ensure consistent, verifiable audio production outputs:

  1. Verify reference audio file
  2. Calculate timing parameters from BPM and duration
  3. Generate stems with explicit sample type specifications
  4. Apply audio effects via signal processing
  5. Export master track and all stems
  6. Archive deliverables in zip format
  7. Verify all outputs match specifications

Step 1: Verify Reference File

Before processing, verify the reference audio file is valid and readable:

python
import soundfile as sf

# Verify reference file exists and is readable
info = sf.info('reference_track.wav')
print(f"Sample rate: {info.samplerate} Hz")
print(f"Duration: {info.frames / info.samplerate:.2f} seconds")
print(f"Channels: {info.channels}")
print(f"Subtype: {info.subtype}")

Step 2: Calculate Timing Parameters

Derive timing for key section transitions from BPM and total duration:

python
def calculate_section_transitions(bpm, total_duration_sec, sections):
    """Calculate beat-aligned transition points for song sections."""
    beats_per_second = bpm / 60.0
    total_beats = total_duration_sec * beats_per_second
    
    # Distribute sections proportionally or by specified ratios
    section_durations = {}
    cumulative_time = 0
    
    for section_name, beat_count in sections.items():
        duration = beat_count / beats_per_second
        section_durations[section_name] = {
            'start': cumulative_time,
            'end': cumulative_time + duration,
            'beats': beat_count
        }
        cumulative_time += duration
    
    return section_durations

# Example usage
sections = calculate_section_transitions(
    bpm=120,
    total_duration_sec=137,
    sections={'intro': 16, 'verse': 32, 'chorus': 32, 'bridge': 16, 'outro': 16}
)

Step 3: Generate Stems with Explicit Sample Type

Always specify sample type explicitly when generating stems to ensure bit-depth consistency:

python
import numpy as np
import soundfile as sf

def generate_stem(name, duration_sec, sample_rate, subtype='FLOAT'):
    """Generate a stem with explicit sample type specification."""
    frames = int(duration_sec * sample_rate)
    
    # Generate audio content (replace with actual synthesis/processing)
    t = np.linspace(0, duration_sec, frames)
    audio_data = np.sin(2 * np.pi * 440 * t)  # Example: 440Hz tone
    
    # Ensure proper data type for specified subtype
    if subtype == 'FLOAT':
        audio_data = audio_data.astype(np.float32)
    elif subtype == 'PCM_24':
        audio_data = np.clip(audio_data, -1, 1) * (2**23 - 1)
        audio_data = audio_data.astype(np.int32)
    
    sf.write(
        f'{name}_stem.wav',
        audio_data,
        sample_rate,
        subtype=subtype,  # Explicit subtype for 24-bit float or other
        format='WAV'
    )
    return audio_data

# Example: Generate 4 stems at 48kHz, 137s, 24-bit float
sample_rate = 48000
duration = 137
stems = ['guitars', 'synths', 'bridge', 'bass']

for stem_name in stems:
    generate_stem(stem_name, duration, sample_rate, subtype='FLOAT')

Step 4: Apply Effects via scipy.signal

Use scipy.signal for applying audio effects and processing:

python
from scipy import signal
import numpy as np

def apply_lowpass_filter(audio_data, sample_rate, cutoff_freq=5000):
    """Apply a lowpass filter using scipy.signal."""
    nyquist = sample_rate / 2
    normalized_cutoff = cutoff_freq / nyquist
    
    # Design Butterworth filter
    b, a = signal.butter(4, normalized_cutoff, btype='low')
    
    # Apply filter
    filtered_data = signal.filtfilt(b, a, audio_data)
    return filtered_data

def apply_reverb_simple(audio_data, sample_rate, decay=0.5, delay_samples=1000):
    """Apply simple reverb effect."""
    reverbed = np.copy(audio_data)
    decay_factor = decay
    
    for i in range(1, 6):
        delayed = np.zeros_like(audio_data)
        if len(audio_data) > delay_samples * i:
            delayed[delay_samples * i:] = audio_data[:-delay_samples * i]
        reverbed += delayed * (decay_factor ** i)
    
    return np.clip(reverbed, -1, 1)

# Apply effects to stems
for stem_name in stems:
    data, sr = sf.read(f'{stem_name}_stem.wav')
    processed = apply_lowpass_filter(data, sr, cutoff_freq=8000)
    processed = apply_reverb_simple(processed, sr, decay=0.3)
    sf.write(f'{stem_name}_stem_processed.wav', processed, sr, subtype='FLOAT')

Step 5: Export Master and Stems

Export all final deliverables with consistent specifications:

python
def export_audio(filepath, audio_data, sample_rate, subtype='FLOAT'):
    """Export audio file with verified specifications."""
    sf.write(
        filepath,
        audio_data,
        sample_rate,
        subtype=subtype,
        format='WAV'
    )
    # Verify export
    info = sf.info(filepath)
    assert info.samplerate == sample_rate, f"Sample rate mismatch: {info.samplerate}"
    assert info.subtype == subtype, f"Subtype mismatch: {info.subtype}"
    print(f"Exported: {filepath} ({info.duration:.2f}s, {info.samplerate}Hz)")

# Export master (mix of all stems)
master_audio = np.zeros_like(stem_audio)  # Replace with actual mix
for stem_name in stems:
    stem_data, _ = sf.read(f'{stem_name}_stem_processed.wav')
    master_audio += stem_data * 0.5  # Simple mix with gain staging

master_audio = np.clip(master_audio, -1, 1)
export_audio('master_track.wav', master_audio, sample_rate=48000, subtype='FLOAT')

# Export final stems
for stem_name in stems:
    stem_data, sr = sf.read(f'{stem_name}_stem_processed.wav')
    export_audio(f'{stem_name}.wav', stem_data, sample_rate=48000, subtype='FLOAT')

Step 6: Archive Deliverables

Package all outputs in a zip archive:

python
import zipfile
import os

def create_archive(archive_name, file_list):
    """Create zip archive of deliverables."""
    with zipfile.ZipFile(archive_name, 'w', zipfile.ZIP_DEFLATED) as zipf:
        for filepath in file_list:
            if os.path.exists(filepath):
                zipf.write(filepath, os.path.basename(filepath))
                print(f"Added to archive: {filepath}")
            else:
                print(f"WARNING: File not found: {filepath}")
    
    # Verify archive
    with zipfile.ZipFile(archive_name, 'r') as zipf:
        contents = zipf.namelist()
        print(f"Archive contains {len(contents)} files: {contents}")
    
    return archive_name

# Archive master and stems
deliverables = ['master_track.wav'] + [f'{stem}.wav' for stem in stems]
create_archive('audio_deliverables.zip', deliverables)

Step 7: Verify All Outputs

Final verification that all outputs match specifications:

python
def verify_outputs(expected_specs):
    """Verify all output files match expected specifications."""
    results = {'passed': 0, 'failed': 0, 'details': []}
    
    for filepath, specs in expected_specs.items():
        if not os.path.exists(filepath):
            results['failed'] += 1
            results['details'].append(f"MISSING: {filepath}")
            continue
        
        info = sf.info(filepath)
        errors = []
        
        if specs.get('sample_rate') and info.samplerate != specs['sample_rate']:
            errors.append(f"sample_rate: expected {specs['sample_rate']}, got {info.samplerate}")
        
        if specs.get('subtype') and info.subtype != specs['subtype']:
            errors.append(f"subtype: expected {specs['subtype']}, got {info.subtype}")
        
        if specs.get('min_duration') and info.duration < specs['min_duration']:
            errors.append(f"duration: expected >= {specs['min_duration']}s, got {info.duration}s")
        
        if errors:
            results['failed'] += 1
            results['details'].append(f"FAILED: {filepath} - {'; '.join(errors)}")
        else:
            results['passed'] += 1
            results['details'].append(f"PASSED: {filepath} ({info.duration:.2f}s, {info.samplerate}Hz, {info.subtype})")
    
    return results

# Verification specifications
expected_specs = {
    'master_track.wav': {'sample_rate': 48000, 'subtype': 'FLOAT', 'min_duration': 137},
    'guitars.wav': {'sample_rate': 48000, 'subtype': 'FLOAT', 'min_duration': 137},
    'synths.wav': {'sample_rate': 48000, 'subtype': 'FLOAT', 'min_duration': 137},
    'bridge.wav': {'sample_rate': 48000, 'subtype': 'FLOAT', 'min_duration': 137},
    'bass.wav': {'sample_rate': 48000, 'subtype': 'FLOAT', 'min_duration': 137},
}

verification = verify_outputs(expected_specs)
print(f"\nVerification: {verification['passed']} passed, {verification['failed']} failed")
for detail in verification['details']:
    print(detail)

assert verification['failed'] == 0, "Output verification failed!"

Complete Workflow Example

python
#!/usr/bin/env python3
"""Complete audio production workflow execution."""

import soundfile as sf
import numpy as np
from scipy import signal
import zipfile
import os

# Configuration
SAMPLE_RATE = 48000
DURATION = 137
BPM = 120
STEM_NAMES = ['guitars', 'synths', 'bridge', 'bass']
SUBTYPE = 'FLOAT'

def run_workflow():
    # Step 1: Verify reference
    ref_info = sf.info('reference.wav')
    print(f"Reference: {ref_info.duration}s @ {ref_info.samplerate}Hz")
    
    # Step 2: Calculate timing
    bpm = BPM
    beats_per_sec = bpm / 60
    
    # Step 3-4: Generate and process stems
    for stem in STEM_NAMES:
        frames = int(DURATION * SAMPLE_RATE)
        t = np.linspace(0, DURATION, frames)
        audio = np.sin(2 * np.pi * 220 * t)  # Example content
        
        # Apply effects
        audio = apply_lowpass_filter(audio, SAMPLE_RATE, 8000)
        
        # Export with explicit subtype
        sf.write(f'{stem}.wav', audio, SAMPLE_RATE, subtype=SUBTYPE)
    
    # Step 5: Export master
    master = np.zeros(int(DURATION * SAMPLE_RATE))
    for stem in STEM_NAMES:
        data, _ = sf.read(f'{stem}.wav')
        master += data * 0.5
    master = np.clip(master, -1, 1)
    sf.write('master_track.wav', master, SAMPLE_RATE, subtype=SUBTYPE)
    
    # Step 6: Archive
    files = ['master_track.wav'] + [f'{s}.wav' for s in STEM_NAMES]
    with zipfile.ZipFile('deliverables.zip', 'w') as zf:
        for f in files:
            zf.write(f)
    
    # Step 7: Verify
    specs = {f: {'sample_rate': SAMPLE_RATE, 'subtype': SUBTYPE} for f in files}
    results = verify_outputs(specs)
    assert results['failed'] == 0
    print("Workflow complete!")

if __name__ == '__main__':
    run_workflow()

Key Principles

  • Explicit sample types: Always specify subtype parameter (e.g., subtype='FLOAT' for 24-bit float WAV)
  • Verify at each step: Check file properties after each major operation
  • Consistent specifications: Maintain same sample rate, bit depth, and duration across all outputs
  • Archive for delivery: Package all deliverables together for easy distribution
  • Final verification: Assert all outputs meet specifications before declaring success

© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in benchmarks/gdpval/skills/audio-track-production of HKUDS/OpenSpace.

  • SKILL.md
  • .skill_id

Open the folder on GitHubat commit 3827781

Compare with similar skills

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UAV Trajectory Overlay from VideoXXLiu-HNU/visualize_uav_trajectory242—~535Automated safety check: PassGPL-3.0
Procedural LofiIvanWng97/pixtuoid491—~3.5kAutomated safety check: PassMIT
Suede Release LinterJasonColapietro/suede-creator-skills127—~2.3kAutomated safety check: NotesMIT
Video Creation Capability MatrixHKUDS/CLI-Anything52k—~12kAutomated safety check: PassApache-2.0

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Works with

Questions about Audio Track Production Workflow

What does Audio Track Production Workflow do?

Walks through producing a master audio track plus stems in Python, from checking a reference file and timing sections by BPM to effects, a zip archive and final verification. This skill sets out a seven-step pattern for tasks that ask for a master track and several stems delivered as a verified archive. The agent first checks that the reference audio file exists and is readable, then derives beat-aligned section transition times from the tempo (BPM) and the total duration.

When should I use Audio Track Production Workflow?

Audio Track Production Workflow fits situations like: producing a master track plus separate stems from a reference file; aligning song section changes to a given BPM and duration; packaging audio deliverables in a zip and verifying their specs.

How do I install Audio Track Production Workflow in Claude Code?

Run `npx skills add HKUDS/OpenSpace --skill audio-track-production -a claude-code`. Or copy the skill folder (benchmarks/gdpval/skills/audio-track-production in HKUDS/OpenSpace) into .claude/skills/audio-track-production in your project. Claude Code loads it when a task matches its description.

How do I install Audio Track Production Workflow in Codex?

Run `npx skills add HKUDS/OpenSpace --skill audio-track-production -a codex`. Or copy the skill folder (benchmarks/gdpval/skills/audio-track-production in HKUDS/OpenSpace) into .agents/skills/audio-track-production in your project. Codex loads it when a task matches its description.

Can I use Audio Track Production Workflow in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/OpenSpace --skill audio-track-production -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/audio-track-production, .gemini/skills/audio-track-production, .github/skills/audio-track-production and .opencode/skills/audio-track-production in your project.

What does Audio Track Production Workflow need to run?

SKILL.md names no scripts, command-line tools or credentials: Audio Track Production Workflow is instructions for the agent only. Our summary lists: Python with soundfile, NumPy and SciPy.

Does Audio Track Production Workflow access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Audio Track Production Workflow safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Audio Track Production Workflow use?

Audio Track Production Workflow is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Audio Track Production Workflow use?

About 2.9k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Audio Track Production Workflow?

Skills that share tags, products or a category with Audio Track Production Workflow: VRGDG H3 Short Film Pipeline (vrgamegirl19/comfyui-vrgamedevgirl, 765 stars), UAV Trajectory Overlay from Video (XXLiu-HNU/visualize_uav_trajectory, 242 stars), Procedural Lofi (IvanWng97/pixtuoid, 491 stars) and Suede Release Linter (JasonColapietro/suede-creator-skills, 127 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Audio Track Production Workflow?

HKUDS (a GitHub organization) maintains it in HKUDS/OpenSpace, which has 7,754 GitHub stars. The repository holds 199 skills in this directory. The repository was last updated on August 12, 2026.

Source: HKUDS/OpenSpace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.