incremental-audio-workflow

incremental-audio-workflow is a skill for Claude Code, Codex from HKUDS/OpenSpace. It costs 25 tokens per session (6,158 once invoked), scanned A, original, MIT.

A step-by-step process for making audio tracks from separate parts, called stems, such as drums or other instrument layers. It checks each part before moving to the next step.

In plain words
What is it for?
For calculating section timing, checking reference audio, creating and checking stems, applying effects, exporting a master track, and verifying the final files.
Why use it?
It finds bad files, timing problems, and processing errors early instead of discovering them after the whole track is finished.

Skill for Claude CodeCodex

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

Good fit For calculating section timing, checking reference audio, creating and checking stems, applying effects, exporting a master track, and verifying the final files.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hkuds/openspace/audio-track-production-enhanced
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 · 7,552 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-enhanced
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 incremental-audio-workflow

README.md
[![agentmods](https://agentmods.dev/badge/skills/hkuds/openspace/audio-track-production-enhanced/github.svg)](https://agentmods.dev/skills/hkuds/openspace/audio-track-production-enhanced)
Your own site
<a href="https://agentmods.dev/skills/hkuds/openspace/audio-track-production-enhanced"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/audio-track-production-enhanced/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for incremental-audio-workflow

Your own site · 80×15
<a href="https://agentmods.dev/skills/hkuds/openspace/audio-track-production-enhanced"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/audio-track-production-enhanced.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,158 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 $0.00025 $0.06158
Opus 5 $0.00013 $0.03079
Sonnet 5 $0.00005 $0.01232
Haiku 4.5 $0.00003 $0.00616

Measured 10d ago against content hash 4b251564430f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

incremental-audio-workflow 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 10d 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-enhanced/SKILL.md · 648 lines

How it starts

The opening of the file, as written. The whole thing — 648 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Incremental Audio Production Workflow

This skill provides a resilient pattern for audio production that emphasizes incremental verification and fail-fast principles. Each major step produces verified outputs before proceeding, reducing iteration count and catching errors early.

Overview

Follow these steps in strict order. Each step must complete successfully and pass verification before proceeding to the next:

  1. Early timing calculation - Derive section transitions from BPM and duration first
  2. Verify reference audio - Validate input file properties
  3. Generate and verify each stem individually - One stem at a time with immediate verification
  4. Generate drum stem separately - Dedicated drum extension with rhythm patterns
  5. Apply effects with verification - Process each stem and verify output
  6. Export master track - Mix all verified stems
  7. Archive and final verification - Package deliverables with comprehensive checks

Key Differences from Standard Workflow

  • Incremental verification: Verify each stem immediately after generation, not just at the end
  • Fail-fast approach: Stop and report errors at each step rather than accumulating failures
  • Early timing: Calculate section transitions before any audio generation
  • Separated drums: Drum stem generation is a distinct step with rhythm-specific processing
  • Memory-efficient: Process stems individually to avoid large array operations that cause sandbox failures

Step 1: Calculate Timing Parameters (Early)

Calculate all timing parameters before generating any audio. This ensures consistent timing across all stems:

def calculate_section_transitions(bpm, total_duration_sec, sections):
    """Calculate beat-aligned transition points for song sections."""
    beats_per_second = bpm / 60.0
    
    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,
            'start_beat': cumulative_time * beats_per_second
        }
        cumulative_time += duration
    
    return section_durations

# Configuration
BPM = 120
DURATION = 137
SECTIONS = {'intro': 16, 'verse': 32, 'chorus': 32, 'bridge': 16, 'outro': 16}

timing = calculate_section_transitions(BPM, DURATION, SECTIONS)
print("Timing calculated:")
for section, data in timing.items():
    print(f"  {section}: {data['start']:.2f}s - {data['end']:.2f}s ({data['beats']} beats)")

Read the full file on GitHub · 648 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. 10d ago First seen · 648 lines · 25 tokens per session scan A 4b251564430f

Subscribe to this mod's changes

incremental-audio-workflow is a skill published in the GitHub repository HKUDS/OpenSpace (7,552 stars, last pushed 28d ago), licensed MIT. It adds 25 tokens to every session and 6,158 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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