audio-track-production

audio-track-production is a skill for Claude Code, Codex from HKUDS/OpenSpace. It costs 20 tokens per session (2,805 once invoked), scanned A, original, MIT.

A step-by-step workflow for producing a finished audio track, separate instrument parts, and a verified archive. Stems are individual audio parts, such as drums or vocals, that can be mixed separately.

In plain words
What is it for?
Use it to inspect reference audio, calculate section timings, create and process stems, export a master track, and package the results.
Why use it?
It reduces errors by checking the input, timing, effects, exported files, and archive contents throughout production.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to inspect reference audio, calculate section timings, create and process stems, export a master track, and package the results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hkuds/openspace/audio-track-production
About the project

OpenSpace is a skill-management layer for AI agents that stores, retrieves, evaluates, shares, and improves reusable workflows. It is intended for people using multiple coding agents who want skills to be reused and refined based on task outcomes. The catalogue provides 200 skills for use with OpenSpace and the agents it supports.

HKUDS/OpenSpace · 7,544 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add HKUDS/OpenSpace --skill audio-track-production
Clone the repo
git clone --depth 1 https://github.com/HKUDS/OpenSpace

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for audio-track-production

README.md
[![agentmods](https://agentmods.dev/badge/skills/hkuds/openspace/audio-track-production.svg)](https://agentmods.dev/skills/hkuds/openspace/audio-track-production)
Your own site
<a href="https://agentmods.dev/skills/hkuds/openspace/audio-track-production"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/audio-track-production.svg" alt="Measured on agentmods" height="20"></a>
Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,805 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00020 $0.02805
Opus 5 $0.00010 $0.01403
Sonnet 5 $0.00004 $0.00561
Haiku 4.5 $0.00002 $0.00281

Measured 8d ago against content hash c6cabbb86b71, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

audio-track-production scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

benchmarks/gdpval/skills/audio-track-production/SKILL.md · 340 lines

How it starts

The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

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:

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}
)

Read the full file on GitHub · 340 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 340 lines · 20 tokens per session scan A c6cabbb86b71

Subscribe to this mod's changes

audio-track-production is a skill published in the GitHub repository HKUDS/OpenSpace (7,544 stars, last pushed 26d ago), licensed MIT. It adds 20 tokens to every session and 2,805 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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