# Diagnostic Stem Delivery

Audio production with diagnostic analysis, timecode parsing from documents, and...

- Repository: https://github.com/HKUDS/AgentPilot (7784 stars)
- Category: Media
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add HKUDS/AgentPilot --skill diagnostic-stem-delivery -g -a claude-code -y`
Codex: `npx skills add HKUDS/AgentPilot --skill diagnostic-stem-delivery -g -a codex -y`
Cursor: `npx skills add HKUDS/AgentPilot --skill diagnostic-stem-delivery -g -a cursor -y`
Gemini CLI: `npx skills add HKUDS/AgentPilot --skill diagnostic-stem-delivery -g -a gemini-cli -y`

## Description

Audio production with diagnostic analysis, timecode parsing from documents, and verified export workflow

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Diagnostic Stem Audio Production Workflow

This skill provides a resilient pattern for audio production that emphasizes **diagnostic analysis before editing**, **explicit timecode extraction from documents**, **incremental verification**, **fail-fast principles**, and **mandatory deliverable verification**. Each major step produces verified outputs before proceeding, with comprehensive audio diagnostics at specified timecodes.

## Overview

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

1. **Parse timecodes from source documents** - Extract edit spots/timecodes from DOCX/text sources
2. **Perform diagnostic audio analysis** - Analyze reference audio at each timecode (pitch, clicks, frequency)
3. **Calculate timing parameters** - Derive section transitions from BPM and duration
4. **Verify reference audio** - Validate input file properties and extract target duration
5. **Generate and verify each stem individually** - One stem at a time with immediate verification
6. **Detect and resolve duration mismatches** - Apply appropriate extension strategy
7. **Apply edits based on diagnostics** - Make informed edits using analysis results
8. **Mix with verification** - Combine stems and verify mix integrity
9. **Export and verify deliverable** - Generate final output with comprehensive checks

## Key Principles

- **Diagnostics first**: Analyze audio at edit points BEFORE making any changes
- **Document-driven**: Parse timecodes directly from source documents (DOCX, TXT)
- **Incremental verification**: Verify each stem immediately after generation
- **Fail-fast approach**: Stop and report errors at each step
- **Mandatory export**: Final step MUST produce verified deliverable file
- **Tool reliability**: Use run_shell with inline Python for audio processing (avoid execute_code_sandbox for audio)

## Step 0: Parse Timecodes from Source Documents

The rest: https://github.com/HKUDS/AgentPilot/blob/HEAD/openspace/benchmarks/gdpval/skills/audio-track-production-enhanced-enhanced-enhanced/SKILL.md