remix mashup 2x2 youtube

I want to make remixes/mashups of songs on YouTube. The way this will work is I enter the name of 4 songs ("artist name - song name"), 2 of which I want to use as instrumentals and 2 of which I want to use as acapellas, along with the rough start time for the mashup to begin (eg 12.5 seconds). The tool will visit YouTube and download the URL of each song. It will then get the bpm tempo of each. Next it will extract the acapellas of the first 3 and instrumentals of the next 3. Next, it will extract the downbeats and bpm of each song. Next, it will look for the nearest "downbeat" to the start timer that user specified, and cut the instrumental/acapella from that point, beginning there and finishing 64 beats later (approx 16 bars, ie a full verse). Next, it will then calculate the amount that the acapella needs to be sped up or slowed down by, and execute the pitch shift/time stretch. Finally, it will combine both of these audio files in one file, and return the 64 beat "remix/mashup" MP3. It will of course repeat this for all 4 remixes/mashups (so we have 2 instrumentals x 2 acappelas = 4 total mashup mp3s returned).

Sub Agent #1 | Sub Agent #2 | Sub Agent #3

final-output-comps..1

Thank you for the detailed explanation of your automated workflow project. Let's address your questions one by one:

a) Final Output:
The final output of this automated workflow would be a set of 4 MP3 audio files. Each MP3 file would contain a 64-beat (approximately 16 bars) remix/mashup combining the instrumental of one song with the acapella of another song. The acapella would be time-stretched and pitch-shifted to match the tempo and key of the instrumental.

b) Core Components/Outputs:
To generate the final output, the following core components and outputs would be required:

1. User Input:
   - Names of 4 songs ("artist name - song name"), with 2 designated as instrumentals and 2 as acapellas
   - Rough start time for the mashup to begin (in seconds)

2. Song Downloads:
   - YouTube URLs of each song
   - Downloaded audio files of each song

3. Tempo and Key Analysis:
   - BPM (beats per minute) tempo of each song
   - Key information of each song

4. Stem Extraction:
   - Acapella stems extracted from the first 2 songs
   - Instrumental stems extracted from the next 2 songs

5. Downbeat Detection:
   - Downbeats identified for each song

6. Audio Slicing:
   - Instrumental and acapella slices created, starting from the nearest downbeat to the user-specified start time and lasting for 64 beats

7. Tempo and Pitch Adjustment:
   - Acapella slices time-stretched and pitch-shifted to match the tempo and key of the corresponding instrumental slices

8. Audio Mixing:
   - Instrumental and adjusted acapella slices combined into a single audio file for each remix/mashup

9. Final Output:
   - 4 MP3 files, each containing a 64-beat remix/mashup

These core components represent the essential building blocks required to generate the final output of 4 remix/mashup MP3 files. The automated workflow would need to handle each of these steps, passing the necessary data and audio files between them to ultimately produce the desired mashups.

final-output-comps..2

## Understanding the Final Output and Components

### a) Final Output

The final output of this workflow would be **four MP3 files**. Each MP3 file would be a 16-bar (approximately 64 beats) mashup combining one instrumental track with one acapella track, resulting in four unique combinations.

### b) Components and Breakdown

To achieve this final output, several key components and processing steps are necessary:

**Inputs:**

*   **Song information:**  4 strings containing "artist name - song name" (2 for instrumentals, 2 for acapellas)
*   **Start time:** A float representing the desired starting point in seconds (e.g., 12.5)

**Processing Steps and Outputs:**

1.  **Download Audio:**
    *   **Output:** Download the audio for each of the 4 songs from YouTube as temporary audio files.

2.  **Tempo Analysis:**
    *   **Output:** Analyze the tempo of each audio file and extract the Beats Per Minute (BPM) value.

3.  **Audio Extraction:**
    *   **Output:** 
        *   For the 2 designated "acapella" songs, extract and isolate the vocal tracks (acappella) using audio source separation techniques.
        *   For the 2 designated "instrumental" songs, extract and isolate the instrumental tracks (everything except vocals).

4.  **Downbeat Detection:** 
    *   **Output:** Analyze each extracted audio file (2 acapellas & 2 instrumentals) to identify the precise timing of the downbeats or the start of each musical bar.

5.  **Audio Slicing based on Start Time:**
    *   **Output:** 
        *   For each audio file, locate the nearest downbeat to the user-specified start time.
        *   Cut a 64-beat segment from each audio file, starting from the identified downbeat.

6.  **Tempo/Pitch Adjustment:** 
    *   **Output:** 
        *   For each acapella segment, calculate the required adjustment factor to match the tempo (BPM) of the corresponding instrumental segment.
        *   Apply time-stretching and pitch-shifting algorithms to each acapella segment to align its tempo with the instrumental.

7.  **Mixing/Combining:**
    *   **Output:** Combine each adjusted acapella segment with its corresponding instrumental segment, creating 4 separate MP3 files, each representing a unique mashup.

**Additional Considerations:**

*   **Error Handling:** Implement mechanisms to handle potential errors during download, analysis, or processing of audio files.
*   **Temporary Files:**  Develop a strategy for managing temporary audio files efficiently, potentially deleting them after the final mashups have been created.

By breaking down the task into these specific components and outputs, you can build a clear roadmap for your AI agent to accomplish the desired outcome of creating the four mashup MP3 files.

final-output-comps GPT..3

a) The final output of this process would be four mashup .mp3 files. Each of these files contains a 64-beat-long remix of one acapella and one instrumental song, shifted/stretched to perfectly match each other in terms of tempo.

b) To generate this final output, following core components/outputs would be needed:

1. Song Information: For each of the 4 songs, the AI agent will need to fetch information such as the song name, artist name, BPM (beats per minute), and the YouTube URL from which the song is to be downloaded.

2. Song Files: The AI will need to download each song from YouTube, possibly in .mp3 format, to be able to manipulate the music.

3. Acapella and Instrumental Versions: Acapella and instrumental versions of each song would be necessary. These can ideally be extracted from the downloaded song files.

4. Beat Information: For each song, the AI will need to extract the downbeats and the precise start point of the mashup based on the user-defined start timer.

5. Cut and Stretched Audio: The AI needs to cut each instrumental/acapella from the specific beat, and complete approximately 64 beats later. It would also calculate the amount that the acapella needs to be sped up or slowed down and apply the pitch shift/time stretch to match the BPMs.

6. Combined Audio: The AI will finally mesh together the instrumental and acapella parts, creating the final .mp3 file for each mashup.

Thus, the output for each component of the process primarily consists of audio files in different states of processing, before finally giving out four mashup .mp3 files.

CLAUDE:- final-output-and-component-outputs

b) Core Components and Outputs:
To generate the final output, the following core components and outputs would be required:

1. User Input:
   - A structured input format (e.g., JSON file) containing:
     - Names of 4 songs ("artist name - song name"), with 2 designated as instrumentals and 2 as acapellas.
     - Rough start time for the mashup to begin (in seconds).

2. YouTube Interaction:
   - A module to search YouTube for the specified songs and retrieve their URLs.
   - A function to download the audio from the YouTube videos as temporary audio files.

3. Audio Analysis:
   - A library or API (such as librosa or Essentia) to analyze the downloaded audio files and extract:
     - BPM (beats per minute) tempo of each song.
     - Key information of each song.
     - Acapella stems from the first 2 songs.
     - Instrumental stems from the last 2 songs.
     - Downbeats for each song.

4. Audio Processing:
   - A function to identify the nearest downbeat to the user-specified start time for each song.
   - A module to cut a 64-beat segment from each audio file, starting from the identified downbeat.
   - An algorithm to calculate the pitch shift and time stretch required to match the acapella's tempo and key to the instrumental.
   - An audio processing library (such as pydub or librosa) to apply the pitch shift and time stretch to the acapella segments.

5. Audio Mixing:
   - A function to combine each adjusted acapella segment with its corresponding instrumental segment, creating 4 separate MP3 files, each representing a unique mashup.

6. Output Generation:
   - A module to generate and save the resulting 4 MP3 files, each containing a 64-beat remix/mashup.
   - A mechanism to return or make the MP3 files accessible to the user.

Additional Considerations:
- Error handling mechanisms to handle potential issues during download, analysis, or processing of audio files.
- Efficient management of temporary audio files, potentially deleting them after the final mashups have been created.

By breaking down the task into these specific components and developing the necessary scripts and modules, your AI agent can automate the process of creating remixes/mashups based on user input. The output for each component primarily consists of audio files in different states of processing, ultimately resulting in four mashup MP3 files that seamlessly combine the instrumental and acapella tracks.

claude 3 - extract final output

extract component outputs

b) Core Components and Outputs:
To generate the final output, the following core components and outputs would be required:

2. YouTube Interaction:
   - A module to search YouTube for the specified songs and retrieve their URLs.
   - A function to download the audio from the YouTube videos as temporary audio files.

5. Audio Mixing:
   - A function to combine each adjusted acapella segment with its corresponding instrumental segment, creating 4 separate MP3 files, each representing a unique mashup.

6. Output Generation:
   - A module to generate and save the resulting 4 MP3 files, each containing a 64-beat remix/mashup.
   - A mechanism to return or make the MP3 files accessible to the user.

Claude - research-ideas-and-input-variables

Thank you for the detailed description of your project. Let me address your questions one by one:

a) Clarifying questions for the final output:
- Should the output be a single MP3 file containing all 4 mashups, or 4 separate MP3 files?
- Is there a specific naming convention you'd like for the output file(s)?
- Do you have a preferred bitrate or audio quality for the output MP3(s)?
- Should the output include any metadata (e.g., artist, title, album) for each mashup?

b) Required inputs before starting the task:
- The names of the 4 songs ("artist name - song name") to be used in the mashups.
- Specification of which 2 songs will be used for instrumentals and which 2 for acapellas.
- The desired start time for each mashup (in seconds).

c) Potential task segmentation:
I believe the task can benefit from segmentation in the following ways:

1. Downloading and preprocessing:
   - Downloading the YouTube videos for each song.
   - Extracting the audio from the downloaded videos.
   - Determining the BPM and downbeats for each song.

2. Acapella and instrumental extraction:
   - Extracting the acapellas from the specified songs.
   - Extracting the instrumentals from the specified songs.

3. Mashup creation (repeated for each combination):
   - Identifying the nearest downbeat to the specified start time.
   - Cutting the instrumental and acapella starting from the identified downbeat.
   - Pitch-shifting and time-stretching the acapella to match the instrumental's BPM.
   - Combining the instrumental and acapella into a single 64-beat mashup.

4. Output generation:
   - Exporting the mashups as MP3 file(s) with the specified audio quality and metadata.

Segmenting the tasks in this manner allows for better organization, parallel processing (where possible), and easier debugging if issues arise during the workflow.

GPT - research-ideas-and-input-variables

a) To clarify the final output of the task, I might ask the following questions:
1. What audio file format should the final mashups be saved in? Although MP3 was mentioned, it's worth confirming that this is the desired format for all users.
2. Are there any specific qualities that the final mashup/remix should have regarding acoustic properties, balance, harmonics etc?
3. Do you have any preferences as to the relative volume levels of the instrumental and acapella tracks in the final mashup?
4. Do you want the AI to select the most 'interesting' part of each track for the mashup, based on musical analysis, or should it always be a 64-beat section starting near a specific time provided by the user?
5. Is there a set order to combining the 2 instrumentals with the 2 acapellas? Would there be preference for certain mixes taking precedence?

b) As mentioned in the original task prompt, the following inputs should be accepted before initiating the task completion process:
1. The names of four songs (specified as "Artist name - Song name.") which will be used for the mashup process. These should be ordered in two pairs of instrumental and acapella respectively.
2. The approximate start time for each song where the mashup should begin.
3. Any user preferences for how the final mashup should sound, if the user wishes to provide them. These could include things like genre, mood, bpm range, and so on, which might assist the AI in successfully executing the task.
4. A location on the local machine/network/cloud where the final audio files should be saved.
5. Any other custom parameters, such as bit rate for the final output or suggested song partitions, that the user might want to input.

template-ideas

To refine our definition of a successful output for your automated remix/mashup workflow, we can collect and analyze the following templates and resources:

1. Existing remix/mashup tracks:
   - Visit websites like SoundCloud, Mixcloud, or YouTube and search for popular remix/mashup tracks in various genres.
   - Analyze the structure, length, and composition of these tracks to understand what makes them successful.
   - Pay attention to how the instrumentals and acapellas are combined, the transitions between sections, and the overall coherence of the mashup.

2. Remix/mashup tutorials and guides:
   - Search for written tutorials, blog posts, or forum discussions that provide step-by-step instructions on creating remixes/mashups.
   - Look for screenshots or video demonstrations that showcase the workflow and techniques used by experienced producers.
   - Extract key insights on audio processing, tempo matching, pitch-shifting, and other technical aspects of creating seamless mashups.

3. Music production software templates:
   - Explore popular music production software like Ableton Live, FL Studio, or Logic Pro and look for built-in or user-created templates designed for remixing or mashup creation.
   - Analyze the structure and layout of these templates to understand how they facilitate the remix/mashup process.
   - Pay attention to the arrangement of tracks, the use of audio effects, and the overall organization of the project.

4. Remix/mashup contests and challenges:
   - Look for online remix/mashup contests or challenges hosted by music platforms, labels, or communities.
   - Review the winning entries and analyze their characteristics, such as creativity, technical skill, and adherence to the contest rules.
   - Extract insights on what makes a remix/mashup stand out and resonate with listeners.

5. Feedback and reviews from music communities:
   - Visit music forums, subreddits, or online communities dedicated to remixing, mashups, or music production.
   - Read feedback, critiques, and reviews of remix/mashup tracks shared by community members.
   - Identify common praise points and criticisms to understand what qualities are valued in successful remixes/mashups.

By collecting and analyzing these templates and resources, we can gain a clearer understanding of what constitutes a successful remix/mashup output. We can identify patterns, techniques, and best practices that can guide the development of our automated workflow.

Some key characteristics of a successful remix/mashup output may include:
- Seamless integration of instrumentals and acapellas
- Proper tempo and pitch matching
- Cohesive structure and arrangement
- Creativity and originality in combining different elements
- High audio quality and production value
- Adherence to copyright and licensing requirements (if applicable)

By incorporating these insights into our automated workflow, we can ensure that the generated MP3 files align with the standards and expectations of successful remixes/mashups in the music community.

claude 3 - searches

Here are 5 specific Google search keyphrases to help refine our definition of a successful output for the automated remix/mashup workflow:

1. "best mashup tracks of all time soundcloud"
   - This search will return popular and well-received mashup tracks on SoundCloud, allowing us to analyze their structure, composition, and techniques used.

2. "how to make a mashup in ableton live tutorial"
   - This search will provide step-by-step tutorials and guides on creating mashups using Ableton Live, a popular music production software. We can extract insights on workflow, audio processing, and best practices.

3. "remix contest winning entries youtube"
   - This search will showcase winning entries from remix contests hosted on YouTube. By reviewing these top-performing remixes, we can identify common characteristics and qualities that make them stand out.

4. "mashup templates for fl studio"
   - This search will return user-created or built-in templates for creating mashups in FL Studio, another widely used music production software. Analyzing these templates will give us an understanding of how to structure and organize our automated workflow.

5. "reddit feedback on mashup tracks"
   - This search will lead us to discussions and feedback on mashup tracks shared within the Reddit community. Reading through the comments and critiques will provide valuable insights into what listeners appreciate or dislike in mashups.

By conducting these specific searches and reviewing the top results, we can gather relevant examples, templates, and insights to refine our understanding of what constitutes a successful remix/mashup output. The information gained will help us fine-tune our automated workflow to generate high-quality and well-received mashups that align with the standards and expectations of the music community.

Best Mashup Tracks of All Time on SoundCloud
How to Make a Mashup in Ableton Live Tutorial
Remix Contest Winning Entries on YouTube
Mashup Templates for FL Studio
Reddit Feedback on Mashup Tracks

subagent-summary

A) FULL TASK SUMMARY: Automatically generate 4 mashup MP3 files by combining instrumentals and acapellas from user-specified YouTube songs.

B) FINAL TASK OUTPUT: 4 MP3 audio files, each containing a 64-beat (approximately 16 bars) remix/mashup combining the instrumental of one song with the acapella of another song. The acapella is time-stretched and pitch-shifted to match the tempo and key of the instrumental. Each MP3 file has a bitrate of 192 kbps and a sample rate of 44.1 kHz.

C) COMPONENTS OVERVIEW:
1. User Input Processor
2. YouTube Downloader
3. Audio Analyzer
4. Audio Processor
5. Audio Mixer
6. Output Generator

D) OVERVIEW OF SUBAGENTS:
1. User Input Processor: Handles user input and validates the provided data.
2. YouTube Downloader: Searches YouTube for the specified songs and downloads the audio.
3. Audio Analyzer: Analyzes the downloaded audio files to extract tempo, key, acapella, instrumental, and downbeat information.
4. Audio Processor: Identifies the nearest downbeat, cuts the 64-beat segments, and applies pitch shift and time stretch to the acapella.
5. Audio Mixer: Combines each adjusted acapella segment with its corresponding instrumental segment.
6. Output Generator: Generates and saves the resulting 4 MP3 files and makes them accessible to the user.

E) SUBAGENT 1: User Input Processor
- Name: UserInputProcessor
- Final Output: Validated and structured user input data (JSON format)
- Tasks:
  1. Prompt the user to enter the names of 4 songs ("artist name - song name"), with 2 designated as instrumentals and 2 as acapellas.
  2. Prompt the user to enter the rough start time for the mashup to begin (in seconds).
  3. Validate the user input, ensuring that song names are provided and the start time is a valid number.
  4. Structure the validated input data into a JSON format.
- Sequence: Prompt for song names > Prompt for start time > Validate input > Structure data > Output JSON

F) SUBAGENT 2: YouTube Downloader
- Name: YouTubeDownloader
- Final Output: 4 temporary audio files downloaded from YouTube
- Tasks:
  1. Receive the structured user input data from the UserInputProcessor.
  2. Use the YouTube Data API to search for the specified songs on YouTube.
  3. Extract the video URLs from the search results.
  4. Use a library like youtube-dl or pytube to download the audio from the YouTube videos.
  5. Save the downloaded audio as temporary files.
- Sequence: Receive user input > Search YouTube > Extract video URLs > Download audio > Save temporary files

G) SUBAGENT 3: Audio Analyzer
- Name: AudioAnalyzer
- Final Output: Analyzed audio data (tempo, key, acapella, instrumental, downbeats)
- Tasks:
  1. Load the temporary audio files downloaded by the YouTubeDownloader.
  2. Use a library like librosa or Essentia to analyze each audio file.
  3. Extract the BPM (beats per minute) tempo and key information for each song.
  4. Isolate the acapella stems from the first 2 songs and instrumental stems from the last 2 songs.
  5. Detect the downbeats for each song.
- Sequence: Load audio files > Analyze tempo and key > Extract acapella and instrumental stems > Detect downbeats > Output analyzed data

H) SUBAGENT 4: Audio Processor
- Name: AudioProcessor
- Final Output: Processed audio segments (64-beat acapella and instrumental segments)
- Tasks:
  1. Receive the analyzed audio data from the AudioAnalyzer.
  2. Identify the nearest downbeat to the user-specified start time for each song.
  3. Cut a 64-beat segment from each audio file, starting from the identified downbeat.
  4. Calculate the pitch shift and time stretch required to match the acapella's tempo and key to the instrumental.
  5. Apply the pitch shift and time stretch to the acapella segments using a library like pydub or librosa.
- Sequence: Receive analyzed data > Identify nearest downbeat > Cut 64-beat segments > Calculate pitch shift and time stretch > Apply adjustments > Output processed segments

I) SUBAGENT 5: Audio Mixer
- Name: AudioMixer
- Final Output: 4 mixed audio files (mashups)
- Tasks:
  1. Receive the processed acapella and instrumental segments from the AudioProcessor.
  2. Combine each adjusted acapella segment with its corresponding instrumental segment.
  3. Ensure proper synchronization and alignment of the segments.
  4. Apply any necessary volume adjustments or cross-fading.
  5. Export the mixed audio as 4 separate files.
- Sequence: Receive processed segments > Combine acapella and instrumental > Synchronize and align > Apply volume adjustments > Export mixed audio files

J) SUBAGENT 6: Output Generator
- Name: OutputGenerator
- Final Output: 4 MP3 files accessible to the user
- Tasks:
  1. Receive the mixed audio files from the AudioMixer.
  2. Convert the mixed audio files to MP3 format with a bitrate of 192 kbps and a sample rate of 44.1 kHz.
  3. Generate appropriate file names for each mashup based on the original song names.
  4. Save the MP3 files to a designated output directory.
  5. Provide the user with access to the generated MP3 files (e.g., through a download link or a local file path).
- Sequence: Receive mixed audio files > Convert to MP3 format > Generate file names > Save MP3 files > Provide user access

subagent-summary-amended-for-actual-tasks..1

A) FULL TASK SUMMARY: Automatically generate 4 mashup MP3 files by combining instrumentals and acapellas from user-specified YouTube songs.

C) COMPONENTS OVERVIEW:
1. YouTube Downloader
2. Audio Analyzer
3. Audio Processor
4. Audio Mixer
5. Output Generator

D) OVERVIEW OF SUBAGENTS:
1. YouTube Downloader: Searches YouTube for the specified songs and downloads the audio.
2. Audio Analyzer: Analyzes the downloaded audio files to extract tempo, key, acapella, instrumental, and downbeat information.
3. Audio Processor: Identifies the nearest downbeat, cuts the 64-beat segments, and applies pitch shift and time stretch to the acapella.
4. Audio Mixer: Combines each adjusted acapella segment with its corresponding instrumental segment.
5. Output Generator: Generates and saves the resulting 4 MP3 files and makes them accessible to the user.

E) SUBAGENT 1: YouTube Downloader
- Name: YouTubeDownloader
- Final Output: 4 temporary audio files downloaded from YouTube
- Tasks:
  1. Receive the structured user input data from the user interface.
  2. Use task 15 (Search Google) to search for the specified songs on YouTube.
  3. Extract the video URLs from the search results.
  4. Use task 43 (Download youtube video to mp4) to download the audio from the YouTube videos.
  5. Use task 44 (Convert mp4 to mp3) to convert the downloaded videos to MP3 format.
  6. Use task 9 (Save content to server) to save the MP3 files as temporary files on the server.
- Sequence: Receive user input > Search YouTube > Extract video URLs > Download videos > Convert to MP3 > Save temporary files

F) SUBAGENT 2: Audio Analyzer
- Name: AudioAnalyzer
- Final Output: Analyzed audio data (tempo, key, acapella, instrumental, downbeats)
- Tasks:
  1. Load the temporary MP3 files downloaded by the YouTubeDownloader.
  2. Use task 33 (Get tempo (BPM) of mp3) to analyze each audio file and extract the BPM (beats per minute) tempo and beatpoint information for each song.
  3. Use task 37 (Extract all audio stems) to isolate the acapella and instrumental stems from each song.
  4. REQUIRED: A task or API call to detect the key of each song.
  5. REQUIRED: A task or API call to detect the downbeats for each song.
- Sequence: Load MP3 files > Analyze tempo and beatpoints > Extract acapella and instrumental stems > Detect key > Detect downbeats > Output analyzed data

G) SUBAGENT 3: Audio Processor
- Name: AudioProcessor
- Final Output: Processed audio segments (64-beat acapella and instrumental segments)
- Tasks:
  1. Receive the analyzed audio data from the AudioAnalyzer.
  2. REQUIRED: A task or API call to identify the nearest downbeat to the user-specified start time for each song.
  3. REQUIRED: A task or API call to cut a 64-beat segment from each audio file, starting from the identified downbeat.
  4. REQUIRED: A task or API call to calculate the pitch shift and time stretch required to match the acapella's tempo and key to the instrumental.
  5. REQUIRED: A task or API call to apply the pitch shift and time stretch to the acapella segments.
- Sequence: Receive analyzed data > Identify nearest downbeat > Cut 64-beat segments > Calculate pitch shift and time stretch > Apply adjustments > Output processed segments

H) SUBAGENT 4: Audio Mixer
- Name: AudioMixer
- Final Output: 4 mixed audio files (mashups)
- Tasks:
  1. Receive the processed acapella and instrumental segments from the AudioProcessor.
  2. REQUIRED: A task or API call to combine each adjusted acapella segment with its corresponding instrumental segment.
  3. REQUIRED: A task or API call to ensure proper synchronization and alignment of the segments.
  4. REQUIRED: A task or API call to apply any necessary volume adjustments or cross-fading.
  5. Use task 9 (Save content to server) to save the mixed audio files on the server.
- Sequence: Receive processed segments > Combine acapella and instrumental > Synchronize and align > Apply volume adjustments > Save mixed audio files

I) SUBAGENT 5: Output Generator
- Name: OutputGenerator
- Final Output: 4 MP3 files accessible to the user
- Tasks:
  1. Receive the mixed audio files from the AudioMixer.
  2. REQUIRED: A task or API call to convert the mixed audio files to MP3 format with a bitrate of 192 kbps and a sample rate of 44.1 kHz.
  3. Use task 8 (Move files around server) to move the MP3 files to a designated output directory accessible to the user.
  4. Provide the user with access to the generated MP3 files (e.g., through a download link or a local file path).
- Sequence: Receive mixed audio files > Convert to MP3 format > Move MP3 files to output directory > Provide user access

subagent-summary-amended-for-actual-tasks..2

Thank you for providing the updated list of available tasks. Based on this information, I have revised the subagents and their tasks to align with the objective of creating an automated workflow for generating mashup remixes using YouTube songs. Here's the updated list:

SUBAGENT 1: YouTube Downloader
- Final Output: 4 temporary MP3 files downloaded from YouTube
- Tasks:
  1. Receive the structured user input data (song names and start time).
  2. Search YouTube for the specified songs using Task 15 (Search Google).
  3. Extract the video URLs from the search results.
  4. Download the audio from the YouTube videos using Task 43 (Download YouTube video to MP4).
  5. Convert the downloaded MP4 files to MP3 using Task 44 (Convert MP4 to MP3).
  6. Save the MP3 files to the server using Task 9 (Save content to server).

SUBAGENT 2: Audio Analyzer
- Final Output: Analyzed audio data (tempo, key, stems, downbeats)
- Tasks:
  1. Load the temporary MP3 files downloaded by the YouTube Downloader using Task 10 (Access text files already saved).
  2. Extract the tempo (BPM) and beatpoint timings of each song using Task 33 (Get tempo (BPM) of MP3).
  3. Extract the instrumental and acapella stems from each song using Task 36 (Extract instrumental/vocal stems).
  4. REQUIRED: A task or library to detect the downbeats of each song.

SUBAGENT 3: Audio Processor
- Final Output: Processed audio segments (64-beat acapella and instrumental segments)
- Tasks:
  1. Receive the analyzed audio data from the Audio Analyzer.
  2. Identify the nearest downbeat to the user-specified start time for each song.
  3. Split/extract the 64-beat segment from each stem using Task 45 (Split/extract video by interval), adjusting the task to work with MP3 files.
  4. REQUIRED: A task or library to calculate the pitch shift and time stretch required to match the acapella's tempo and key to the instrumental.
  5. REQUIRED: A task or library to apply the pitch shift and time stretch to the acapella segments.

SUBAGENT 4: Audio Mixer
- Final Output: 4 mixed MP3 files (mashups)
- Tasks:
  1. Receive the processed acapella and instrumental segments from the Audio Processor.
  2. REQUIRED: A task or library to combine each adjusted acapella segment with its corresponding instrumental segment, ensuring proper synchronization and alignment.
  3. REQUIRED: A task or library to apply volume adjustments or cross-fading to the mixed audio.
  4. Save the mixed audio files to the server using Task 9 (Save content to server).

SUBAGENT 5: Output Generator
- Final Output: 4 MP3 files accessible to the user
- Tasks:
  1. Receive the mixed audio files from the Audio Mixer.
  2. REQUIRED: A task or library to ensure the mixed audio files are in MP3 format with a bitrate of 192 kbps and a sample rate of 44.1 kHz.
  3. Generate appropriate file names for each mashup based on the original song names.
  4. Save the MP3 files to a designated output directory on the server using Task 9 (Save content to server).
  5. Provide the user with access to the generated MP3 files (e.g., through a download link or a local file path).

Please note that some required functionalities, such as detecting downbeats, calculating and applying pitch shift and time stretch, combining audio segments, and ensuring specific MP3 format settings, do not have direct task equivalents in the provided list. These are marked as "REQUIRED" and would need to be added as new tasks or libraries to enable the agent to complete the mashup remix generation process effectively.

subagent-summary-amended-for-actual-tasks GPT..3

Based on the tasks available to the agent, I've revised the subagents needed to fulfill the task. The original subagent tasks will now be associated with the nearest relevant available task from your list, and I'll designate REQUIREMENTS where the current suite of tasks is insufficient to perform the necessary work. Let's call this sophisticated AI Agent we're constructing "Mashup Composer."

#### REVISED SUBAGENTS AND THEIR ASSOCIATED TASKS FOR MASHUP COMPOSER:

**1. YouTube Downloader & Converter**:
- Receive structured user input data.
- Use task 43 (Download youtube video to mp4) to download the specified YouTube videos.
- Follow with task 44 (Convert mp4 to mp3) to extract the audio tracks from the downloaded videos.

**2. Audio Analyzer & Processor**:
- Take the mp3 files from the previous stage and use task 33 (Get tempo (BPM) of mp3) to determine the BPM and beatpoints.
- REQUIRED: A new task to determine the key of each song (None of the available tasks provide this).
- Use task 36 (Extract instrumental/vocal stems) to separate the instrumentals and acapellas.

**3. Segment Cutter**:
- Based on user-specific start time and downbeat data, identify the section of the audio to use.
- REQUIRED: A task that can locate the nearest downbeat relative to the user input start time and cut a 64-beat segment from the downbeat.

**4. Audio Time-Stretching**:
- REQUIRED: A task to pitch-shift the acapella to match the instrumental BPM and key, ensuring proper synchronization (Current tasks do not cover complex audio processing such as BPM matching and key transposition).

**5. Audio Mixing & Mastering**:
- Combine the time-stretched acapella with the corresponding instrumental.
- REQUIRED: A task to mix two audio segments into one file, applying any necessary volume adjustments or effects for mastering, and ensuring proper synchronization and alignment.

**6. Output Generator**:
- Compile the final mix into an MP3 file with appropriate quality settings.
- Utilize task 31 (Create voice-over from text) to create an MP3; even though it's for voice-overs, this suggests an MP3 generation capability.
- REQUIRED: We need a specific task that saves the generated MP3 with a bitrate of 192 kbps and a sample rate of 44.1 kHz, tags it based on the original songs' names, and retains the audio quality post-processing.

#### SUMMARY OF REQUIREMENTS FOR NEW TASKS:

- **Audio Key Analysis Task**: Determine the key of each song to facilitate accurate pitch shifting.
- **Downbeats & Segment Cutter Task**: Locate the nearest downbeat to the user-specified time and cut a precise 64-beat segment from the downbeat.
- **Audio Time-Stretching and Pitch-Shifting Task**: Adjust the BPM and key of the acapella to match the instrumental track.
- **Audio Mixing and Mastering Task**: Mix the adjusted acapella with the instrumental track, apply final mastering (volume balancing, EQ, etc.), and generate a high-quality MP3 with specified bitrate and sample rate.

Once these new tasks are created and integrated into the Agent's suite of capabilities, "Mashup Composer" would be able to automate the process of creating song mashups/remixes based on user specifications with minimal manual intervention.

subagent-summary-amended-for-actual-tasks

A) FULL TASK SUMMARY: Automatically generate 4 mashup MP3 files by combining instrumentals and acapellas from user-specified YouTube songs.

C) COMPONENTS OVERVIEW:
1. YouTube Downloader & Converter
2. Audio Analyzer & Processor
3. Segment Cutter
4. Audio Time-Stretching & Pitch-Shifting
5. Audio Mixing & Mastering
6. Output Generator

D) OVERVIEW OF SUBAGENTS:
1. YouTube Downloader & Converter: Searches YouTube for the specified songs, downloads the videos, and converts them to MP3 format.
2. Audio Analyzer & Processor: Analyzes the downloaded MP3 files to extract tempo, key, instrumental, and acapella stems.
3. Segment Cutter: Identifies the nearest downbeat to the user-specified start time and cuts a 64-beat segment from each stem.
4. Audio Time-Stretching & Pitch-Shifting: Adjusts the tempo and key of the acapella segments to match the corresponding instrumentals.
5. Audio Mixing & Mastering: Combines each adjusted acapella segment with its corresponding instrumental segment, applies volume adjustments and effects, and ensures proper synchronization and alignment.
6. Output Generator: Generates and saves the resulting 4 MP3 files with appropriate file names, bitrate, and sample rate, making them accessible to the user.

E) SUBAGENT 1: YouTube Downloader & Converter
- Final Output: 4 temporary MP3 files downloaded from YouTube
- Tasks:
  1. Receive the structured user input data (song names and start time).
  2. Search YouTube for the specified songs using Task 15 (Search Google).
  3. Extract the video URLs from the search results.
  4. Download the videos using Task 43 (Download YouTube video to MP4).
  5. Convert the downloaded videos to MP3 format using Task 44 (Convert MP4 to MP3).
  6. Save the MP3 files to the server using Task 9 (Save content to server).

F) SUBAGENT 2: Audio Analyzer & Processor
- Final Output: Analyzed audio data (tempo, key, stems)
- Tasks:
  1. Load the temporary MP3 files downloaded by the YouTube Downloader using Task 10 (Access text files already saved).
  2. Extract the tempo (BPM) of each song using Task 33 (Get tempo (BPM) of MP3).
  3. REQUIRED: A task or library to detect the key of each song.
  4. Extract the instrumental and acapella stems from each song using Task 36 (Extract instrumental/vocal stems).

G) SUBAGENT 3: Segment Cutter
- Final Output: Processed audio segments (64-beat instrumental and acapella segments)
- Tasks:
  1. Receive the analyzed audio data from the Audio Analyzer & Processor.
  2. REQUIRED: A task or library to identify the nearest downbeat to the user-specified start time for each song.
  3. REQUIRED: A task or library to cut a 64-beat segment from each stem, starting from the identified downbeat.

H) SUBAGENT 4: Audio Time-Stretching & Pitch-Shifting
- Final Output: Adjusted acapella segments matching the tempo and key of the instrumentals
- Tasks:
  1. Receive the 64-beat instrumental and acapella segments from the Segment Cutter.
  2. REQUIRED: A task or library to calculate the pitch shift and time stretch required to match the acapella's tempo and key to the instrumental.
  3. REQUIRED: A task or library to apply the pitch shift and time stretch to the acapella segments.

I) SUBAGENT 5: Audio Mixing & Mastering
- Final Output: 4 mixed MP3 files (mashups)
- Tasks:
  1. RIRED: A task or library to combine each adjusted acapella segment with its correspo nding instrumental segment, ensuring proper synchronization and alignment.
  2. REQUIRED: A task or library to apply volume adjustments, cross-fading, and final mastering effects to the mixed audio.
  3. Save the mixed audio files to the server using Task 9 (Save content to server).

J) SUBAGENT 6: Output Generator
- Final Output: 4 MP3 files accessible to the user
- Tasks:
  1. Receive the mixed audio files from the Audio Mixing & Mastering subagent.
  2. REQUIRED: A task or library to ensure the mixed audio files are in MP3 format with a bitrate of 192 kbps and a sample rate of 44.1 kHz.
  3. Generate appropriate file names for each mashup based on the original song names.
  4. Save the MP3 files to a designated output directory on the server using Task 9 (Save content to server).
  5. Provide the user with access to the generated MP3 files (e.g., through a download link or a local file path).

K) SUMMARY OF REQUIREMENTS FOR NEW TASKS:
- Audio Key Analysis Task: Determine the key of each song to facilitate accurate pitch shifting.
- Downbeats & Segment Cutter Task: Locate the nearest downbeat to the user-specified time and cut a precise 64-beat segment from the downbeat.
- Audio Time-Stretching and Pitch-Shifting Task: Adjust the BPM and key of the acapella to match the instrumental track.  
- Audio Mixing and Mastering Task: Mix the adjusted acapella with the instrumental track, apply final mastering (volume balancing, EQ, etc.), and generate a high-quality MP3 with specified bitrate and sample rate.

Once these new tasks are created and integrated into the agent's capabilities, it will be able to automate the process of creating song mashups/remixes based on user specifications with minimal manual intervention.

random-autocomplete

To achieve the end goal of creating 4 MP3 audio files containing 64-beat remixes/mashups combining instrumentals and acapellas from different songs, the most relevant tasks might be:

1. Accepting user input for the names of 4 songs and the desired start time for the mashup
2. Searching for and downloading the YouTube URLs for each song
3. Analyzing the tempo (BPM) of each song
4. Extracting the instrumentals from the first 2 songs and acapellas from the last 2 songs
5. Detecting the downbeats in each instrumental and acapella
6. Cutting the instrumentals and acapellas starting from the nearest downbeat to the user-specified start time, and creating a 64-beat segment
7. Calculating the required pitch shift and time stretch for each acapella to match the corresponding instrumental
8. Applying the pitch shift and time stretch to each acapella
9. Combining each instrumental with its corresponding pitch-shifted/time-stretched acapella to create the final mashup
10. Exporting the resulting mashups as MP3 files

The overall sequence would be:

User Input > YouTube Search & Download > Tempo Analysis > Instrumental/Acapella Extraction > Downbeat Detection > Audio Segmentation > Pitch Shift/Time Stretch Calculation > Pitch Shift/Time Stretch Application > Audio Combination > MP3 Export

To accomplish these tasks, the Agent could utilize various APIs and Python libraries, such as:

- YouTube Data API for searching and downloading YouTube URLs
- librosa or pydub for audio analysis, manipulation, and processing
- soundfile for reading and writing audio files
- numpy for numerical computations and array manipulation
- ffmpeg (via a Python wrapper) for audio format conversion and manipulation

The Agent would orchestrate the entire process by calling the appropriate APIs and libraries in the correct sequence, passing data between them as needed, and handling any necessary error checking and data validation. The Virtual Machine running Windows 11 would provide a stable environment for the Agent to execute these tasks and store the intermediate and final audio files.

claude 3 - mermaid-for-all

SubAgent #1

claude 3 - subagent1

subagent1-refined

A) SUBAGENT SUMMARY: YouTube Downloader & Converter subagent searches for specified songs on YouTube, downloads the videos, and converts them to MP3 format.

B) FINAL TASK OUTPUT: 4 temporary MP3 files downloaded from YouTube, each with a bitrate of 192 kbps and a sample rate of 44.1 kHz, saved to the server at a specified internal URL location.

C) SUBAGENT INPUT:
- Structured user input data containing:
  - Names of 4 songs ("artist name - song name"), with 2 designated as instrumentals and 2 as acapellas.
  - Rough start time for the mashup to begin (in seconds).

E) SUBAGENT TASK SUMMARY:
1. Receive structured user input data > Task 1 (GPT4) to parse input data and extract song names and start time.
2. For each song:
   a. Search YouTube using Task 15 (Search Google) with the song name as the keyword.
   b. Task 1 (GPT4) to extract the top video URL from the search results.
   c. Download the YouTube video using Task 43 (Download YouTube video to MP4), saving the MP4 file to the server.
   d. Convert the downloaded MP4 to MP3 format using Task 44 (Convert MP4 to MP3).
   e. Save the MP3 file to the server using Task 9 (Save content to server) at a specified internal URL location.
3. Output the internal URL locations of the 4 downloaded and converted MP3 files.

F) SILOS:
1. Song 1 (Instrumental):
   - YouTube search > Video URL extraction > MP4 download > MP3 conversion > Server storage
2. Song 2 (Instrumental):
   - YouTube search > Video URL extraction > MP4 download > MP3 conversion > Server storage
3. Song 3 (Acapella):
   - YouTube search > Video URL extraction > MP4 download > MP3 conversion > Server storage
4. Song 4 (Acapella):
   - YouTube search > Video URL extraction > MP4 download > MP3 conversion > Server storage

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive structured user input data.
2. Task 1 (GPT4): Parse input data and extract song names and start time.
3. Song 1 (Instrumental):
   a. Task 15 (Search Google): Search YouTube with the song name as the keyword.
   b. Task 1 (GPT4): Extract the top video URL from the search results.
   c. Task 43 (Download YouTube video to MP4): Download the YouTube video, saving the MP4 file to the server.
   d. Task 44 (Convert MP4 to MP3): Convert the downloaded MP4 to MP3 format.
   e. Task 9 (Save content to server): Save the MP3 file to the server at a specified internal URL location.
4. Song 2 (Instrumental):
   a. Task 15 (Search Google): Search YouTube with the song name as the keyword.
   b. Task 1 (GPT4): Extract the top video URL from the search results.
   c. Task 43 (Download YouTube video to MP4): Download the YouTube video, saving the MP4 file to the server.
   d. Task 44 (Convert MP4 to MP3): Convert the downloaded MP4 to MP3 format.
   e. Task 9 (Save content to server): Save the MP3 file to the server at a specified internal URL location.
5. Song 3 (Acapella):
   a. Task 15 (Search Google): Search YouTube with the song name as the keyword.
   b. Task 1 (GPT4): Extract the top video URL from the search results.
   c. Task 43 (Download YouTube video to MP4): Download the YouTube video, saving the MP4 file to the server.
   d. Task 44 (Convert MP4 to MP3): Convert the downloaded MP4 to MP3 format.
   e. Task 9 (Save content to server): Save the MP3 file to the server at a specified internal URL location.
6. Song 4 (Acapella):
   a. Task 15 (Search Google): Search YouTube with the song name as the keyword.
   b. Task 1 (GPT4): Extract the top video URL from the search results.
   c. Task 43 (Download YouTube video to MP4): Download the YouTube video, saving the MP4 file to the server.
   d. Task 44 (Convert MP4 to MP3): Convert the downloaded MP4 to MP3 format.
   e. Task 9 (Save content to server): Save the MP3 file to the server at a specified internal URL location.
7. Output the internal URL locations of the 4 downloaded and converted MP3 files.

Subagent1 task-ID-numbers

subagent1-output-variables

Here's the updated task list with output tokens added:

--------------
G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive structured user input data.
   - Output: [user-input]
2. Task 1 (GPT4): Parse input data and extract song names and start time.
   - Input: [user-input]
   - Output: [song1-name], [song2-name], [song3-name], [song4-name], [start-time]
3. Song 1 (Instrumental):
   a. Task 15 (Search Google): Search YouTube with the song name as the keyword.
      - Input: [song1-name]
      - Output: [song1-search-results]
   b. Task 1 (GPT4): Extract the top video URL from the search results.
      - Input: [song1-search-results]
      - Output: [song1-url]
   c. Task 43 (Download YouTube video to MP4): Download the YouTube video, saving the MP4 file to the server.
      - Input: [song1-url]
      - Output: [song1-mp4]
   d. Task 44 (Convert MP4 to MP3): Convert the downloaded MP4 to MP3 format.
      - Input: [song1-mp4]
      - Output: [song1-mp3]
   e. Task 9 (Save content to server): Save the MP3 file to the server at a specified internal URL location.
      - Input: [song1-mp3]
      - Output: [song1-internal-url]
4. Song 2 (Instrumental):
   a. Task 15 (Search Google): Search YouTube with the song name as the keyword.
      - Input: [song2-name]
      - Output: [song2-search-results]
   b. Task 1 (GPT4): Extract the top video URL from the search results.
      - Input: [song2-search-results]
      - Output: [song2-url]
   c. Task 43 (Download YouTube video to MP4): Download the YouTube video, saving the MP4 file to the server.
      - Input: [song2-url]
      - Output: [song2-mp4]
   d. Task 44 (Convert MP4 to MP3): Convert the downloaded MP4 to MP3 format.
      - Input: [song2-mp4]
      - Output: [song2-mp3]
   e. Task 9 (Save content to server): Save the MP3 file to the server at a specified internal URL location.
      - Input: [song2-mp3]
      - Output: [song2-internal-url]
5. Song 3 (Acapella):
   a. Task 15 (Search Google): Search YouTube with the song name as the keyword.
      - Input: [song3-name]
      - Output: [song3-search-results]
   b. Task 1 (GPT4): Extract the top video URL from the search results.
      - Input: [song3-search-results]
      - Output: [song3-url]
   c. Task 43 (Download YouTube video to MP4): Download the YouTube video, saving the MP4 file to the server.
      - Input: [song3-url]
      - Output: [song3-mp4]
   d. Task 44 (Convert MP4 to MP3): Convert the downloaded MP4 to MP3 format.
      - Input: [song3-mp4]
      - Output: [song3-mp3]
   e. Task 9 (Save content to server): Save the MP3 file to the server at a specified internal URL location.
      - Input: [song3-mp3]
      - Output: [song3-internal-url]
6. Song 4 (Acapella):
   a. Task 15 (Search Google): Search YouTube with the song name as the keyword.
      - Input: [song4-name]
      - Output: [song4-search-results]
   b. Task 1 (GPT4): Extract the top video URL from the search results.
      - Input: [song4-search-results]
      - Output: [song4-url]
   c. Task 43 (Download YouTube video to MP4): Download the YouTube video, saving the MP4 file to the server.
      - Input: [song4-url]
      - Output: [song4-mp4]
   d. Task 44 (Convert MP4 to MP3): Convert the downloaded MP4 to MP3 format.
      - Input: [song4-mp4]
      - Output: [song4-mp3]
   e. Task 9 (Save content to server): Save the MP3 file to the server at a specified internal URL location.
      - Input: [song4-mp3]
      - Output: [song4-internal-url]
7. Output the internal URL locations of the 4 downloaded and converted MP3 files.
   - Input: [song1-internal-url], [song2-internal-url], [song3-internal-url], [song4-internal-url]
   - Output: [mp3-urls]
--------------

H) OUTPUT TOKENS

[user-input], [song1-name], [song2-name], [song3-name], [song4-name], [start-time], [song1-search-results], [song1-url], [song1-mp4], [song1-mp3], [song1-internal-url], [song2-search-results], [song2-url], [song2-mp4], [song2-mp3], [song2-internal-url], [song3-search-results], [song3-url], [song3-mp4], [song3-mp3], [song3-internal-url], [song4-search-results], [song4-url], [song4-mp4], [song4-mp3], [song4-internal-url], [mp3-urls]

subagent1refined-repeating-json-core

G) TASKFLOW SUMMARY

Title task: Parse user input and extract song names and start time
Description of the task: The first task is to parse the structured user input data and extract the song names (artist and title) for the 4 songs, as well as the specified start time for the mashup.
Input(s) involved: [user_input_data]
Output token name: [extracted_song_names], [start_time]

Title task: Search YouTube for Song 1 (Instrumental)
Description of the task: Use the extracted song name for Song 1 (Instrumental) to perform a YouTube search and retrieve the top video URL from the search results.
Input(s) involved: [extracted_song_names]
Output token name: [song1_video_url]

Title task: Download YouTube video for Song 1 (Instrumental)
Description of the task: Download the YouTube video for Song 1 (Instrumental) using the retrieved video URL, saving the video as an MP4 file on the server.
Input(s) involved: [song1_video_url]
Output token name: [song1_mp4_file]

Title task: Convert MP4 to MP3 for Song 1 (Instrumental)
Description of the task: Convert the downloaded MP4 file for Song 1 (Instrumental) to MP3 format for further processing.
Input(s) involved: [song1_mp4_file]
Output token name: [song1_mp3_file]

Title task: Save MP3 file for Song 1 (Instrumental) to server
Description of the task: Save the converted MP3 file for Song 1 (Instrumental) to the server at a specified internal URL location for later access.
Input(s) involved: [song1_mp3_file]
Output token name: [song1_mp3_url]

Title task: Search YouTube for Song 2 (Instrumental)
Description of the task: Use the extracted song name for Song 2 (Instrumental) to perform a YouTube search and retrieve the top video URL from the search results.
Input(s) involved: [extracted_song_names]
Output token name: [song2_video_url]

Title task: Download YouTube video for Song 2 (Instrumental)
Description of the task: Download the YouTube video for Song 2 (Instrumental) using the retrieved video URL, saving the video as an MP4 file on the server.
Input(s) involved: [song2_video_url]
Output token name: [song2_mp4_file]

Title task: Convert MP4 to MP3 for Song 2 (Instrumental)
Description of the task: Convert the downloaded MP4 file for Song 2 (Instrumental) to MP3 format for further processing.
Input(s) involved: [song2_mp4_file]
Output token name: [song2_mp3_file]

Title task: Save MP3 file for Song 2 (Instrumental) to server
Description of the task: Save the converted MP3 file for Song 2 (Instrumental) to the server at a specified internal URL location for later access.
Input(s) involved: [song2_mp3_file]
Output token name: [song2_mp3_url]

Title task: Search YouTube for Song 3 (Acapella)
Description of the task: Use the extracted song name for Song 3 (Acapella) to perform a YouTube search and retrieve the top video URL from the search results.
Input(s) involved: [extracted_song_names]
Output token name: [song3_video_url]

Title task: Download YouTube video for Song 3 (Acapella)
Description of the task: Download the YouTube video for Song 3 (Acapella) using the retrieved video URL, saving the video as an MP4 file on the server.
Input(s) involved: [song3_video_url]
Output token name: [song3_mp4_file]

Title task: Convert MP4 to MP3 for Song 3 (Acapella)
Description of the task: Convert the downloaded MP4 file for Song 3 (Acapella) to MP3 format for further processing.
Input(s) involved: [song3_mp4_file]
Output token name: [song3_mp3_file]

Title task: Save MP3 file for Song 3 (Acapella) to server
Description of the task: Save the converted MP3 file for Song 3 (Acapella) to the server at a specified internal URL location for later access.
Input(s) involved: [song3_mp3_file]
Output token name: [song3_mp3_url]

Title task: Search YouTube for Song 4 (Acapella)
Description of the task: Use the extracted song name for Song 4 (Acapella) to perform a YouTube search and retrieve the top video URL from the search results.
Input(s) involved: [extracted_song_names]
Output token name: [song4_video_url]

Title task: Download YouTube video for Song 4 (Acapella)
Description of the task: Download the YouTube video for Song 4 (Acapella) using the retrieved video URL, saving the video as an MP4 file on the server.
Input(s) involved: [song4_video_url]
Output token name: [song4_mp4_file]

Title task: Convert MP4 to MP3 for Song 4 (Acapella)
Description of the task: Convert the downloaded MP4 file for Song 4 (Acapella) to MP3 format for further processing.
Input(s) involved: [song4_mp4_file]
Output token name: [song4_mp3_file]

Title task: Save MP3 file for Song 4 (Acapella) to server
Description of the task: Save the converted MP3 file for Song 4 (Acapella) to the server at a specified internal URL location for later access.
Input(s) involved: [song4_mp3_file]
Output token name: [song4_mp3_url]

Title task: Output internal URL locations of downloaded MP3 files
Description of the task: Output the internal URL locations of the 4 downloaded and converted MP3 files for the instrumental and acapella tracks.
Input(s) involved: [song1_mp3_url], [song2_mp3_url], [song3_mp3_url], [song4_mp3_url]
Output token name: [mp3_file_urls]

subagent1mermaid

<!DOCTYPE html>
<html>
<head>
  <title>Mermaid Diagram Example</title>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script>
</head>
<body>
  <div class="mermaid">
graph TD
    A[Parse user input and extract song names and start time] --> B{Extracted song names}
    A --> C{Start time}
    B --> D[Search YouTube for Song 1 Instrumental]
    D --> E[Download YouTube video for Song 1 Instrumental]
    E --> F[Convert MP4 to MP3 for Song 1 Instrumental]
    F --> G[Save MP3 file for Song 1 Instrumental to server]
    G --> N{song1_mp3_url}
    B --> H[Search YouTube for Song 2 Instrumental]
    H --> I[Download YouTube video for Song 2 Instrumental]
    I --> J[Convert MP4 to MP3 for Song 2 Instrumental]
    J --> K[Save MP3 file for Song 2 Instrumental to server]
    K --> O{song2_mp3_url}
    B --> L[Search YouTube for Song 3 Acapella]
    L --> M[Download YouTube video for Song 3 Acapella]
    M --> P[Convert MP4 to MP3 for Song 3 Acapella]
    P --> Q[Save MP3 file for Song 3 Acapella to server]
    Q --> R{song3_mp3_url}
    B --> S[Search YouTube for Song 4 Acapella]
    S --> T[Download YouTube video for Song 4 Acapella]
    T --> U[Convert MP4 to MP3 for Song 4 Acapella]
    U --> V[Save MP3 file for Song 4 Acapella to server]
    V --> W{song4_mp3_url}
    N --> X[Output internal URL locations of downloaded MP3 files]
    O --> X
    R --> X
    W --> X
  </div>

SubAgent #2

claude 3 - subagent2

subagent2-refined

A) SUBAGENT SUMMARY: Audio Analyzer & Processor subagent analyzes the downloaded MP3 files to extract tempo, key, and instrumental/acapella stems.

B) FINAL TASK OUTPUT: JSON file containing the analyzed audio data for each of the 4 songs, including:
- Tempo (BPM) as a float value
- Key as a string (e.g., "C Major", "A Minor")
- Instrumental stem MP3 URL
- Acapella stem MP3 URL

C) SUBAGENT INPUT:
- 4 MP3 file URLs of the songs downloaded by the YouTube Downloader subagent

D) SUBAGENT TASK SUMMARY:
1. Load the 4 MP3 files using Task 10 (Access text files already saved).
2. For each MP3 file:
   a. Extract the tempo using Task 33 (Get tempo (BPM) of MP3).
   b. Detect the key using a new task or library (to be developed).
   c. Extract the instrumental and acapella stems using Task 36 (Extract instrumental/vocal stems).
   d. Save the instrumental and acapella stem MP3s to the server using Task 9 (Save content to server).
3. Create a JSON object containing the analyzed data for each song:
   ```json
   {
     "song1": {
       "tempo": 120.5,
       "key": "C Major",
       "instrumental_url": "server/_public/timestamp/song1_instrumental.mp3",
       "acapella_url": "server/_public/timestamp/song1_acapella.mp3"
     },
     "song2": {
       "tempo": 98.0,
       "key": "A Minor",
       "instrumental_url": "server/_public/timestamp/song2_instrumental.mp3",
       "acapella_url": "server/_public/timestamp/song2_acapella.mp3"
     },
     ...
   }
   ```
4. Save the JSON file to the server using Task 9 (Save content to server).

E) SILOS:
1. Tempo Extraction Silo:
   - Load MP3 file
   - Extract tempo using Task 33
   - Return tempo value
2. Key Detection Silo (new task/library required):
   - Load MP3 file
   - Detect key using new task/library
   - Return key value
3. Stem Extraction Silo:
   - Load MP3 file
   - Extract instrumental and acapella stems using Task 36
   - Save stem MP3s to server using Task 9
   - Return URLs of saved stem MP3s
4. JSON Creation Silo:
   - Receive tempo, key, and stem URLs for each song
   - Create JSON object with analyzed data
   - Save JSON file to server using Task 9
   - Return URL of saved JSON file

subagent2refined-repeating

A) SUBAGENT SUMMARY: Audio Analyzer & Processor subagent analyzes the downloaded MP3 files to extract tempo, key, and instrumental/acapella stems.

C) SUBAGENT INPUT:
- 4 MP3 file URLs of the songs downloaded by the YouTube Downloader subagent

E) SILOS:
1. Tempo Extraction Silo
2. Key Detection Silo (new task/library required)
3. Stem Extraction Silo
4. JSON Creation Silo

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Load MP3 file for Song 1 using Task 10
2. Extract tempo for Song 1 using Task 33
3. Detect key for Song 1 using new task/library
4. Extract instrumental stem for Song 1 using Task 36
5. Save instrumental stem MP3 for Song 1 to server using Task 9
6. Extract acapella stem for Song 1 using Task 36
7. Save acapella stem MP3 for Song 1 to server using Task 9
8. Load MP3 file for Song 2 using Task 10
9. Extract tempo for Song 2 using Task 33
10. Detect key for Song 2 using new task/library
11. Extract instrumental stem for Song 2 using Task 36
12. Save instrumental stem MP3 for Song 2 to server using Task 9
13. Extract acapella stem for Song 2 using Task 36
14. Save acapella stem MP3 for Song 2 to server using Task 9
15. Load MP3 file for Song 3 using Task 10
16. Extract tempo for Song 3 using Task 33
17. Detect key for Song 3 using new task/library
18. Extract instrumental stem for Song 3 using Task 36
19. Save instrumental stem MP3 for Song 3 to server using Task 9
20. Extract acapella stem for Song 3 using Task 36
21. Save acapella stem MP3 for Song 3 to server using Task 9
22. Load MP3 file for Song 4 using Task 10
23. Extract tempo for Song 4 using Task 33
24. Detect key for Song 4 using new task/library
25. Extract instrumental stem for Song 4 using Task 36
26. Save instrumental stem MP3 for Song 4 to server using Task 9
27. Extract acapella stem for Song 4 using Task 36
28. Save acapella stem MP3 for Song 4 to server using Task 9
29. Create JSON object with analyzed data for all 4 songs
30. Save JSON file to server using Task 9

Subagent2 task-ID-numbers

subagent2-output-variables

Here is the updated task list with output tokens added:

--------------
A) SUBAGENT SUMMARY: Audio Analyzer & Processor subagent analyzes the downloaded MP3 files to extract tempo, key, and instrumental/acapella stems.

C) SUBAGENT INPUT:
- 4 MP3 file URLs of the songs downloaded by the YouTube Downloader subagent [song1-mp3], [song2-mp3], [song3-mp3], [song4-mp3]

E) SILOS:
1. Tempo Extraction Silo
2. Key Detection Silo (new task/library required)
3. Stem Extraction Silo
4. JSON Creation Silo

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Load MP3 file for Song 1 using Task 10 [song1-mp3]
2. Extract tempo for Song 1 using Task 33 [song1-tempo]
3. Detect key for Song 1 using new task/library [song1-key]
4. Extract instrumental stem for Song 1 using Task 36 [song1-instrumental]
5. Save instrumental stem MP3 for Song 1 to server using Task 9 [song1-instrumental-url]
6. Extract acapella stem for Song 1 using Task 36 [song1-acapella]
7. Save acapella stem MP3 for Song 1 to server using Task 9 [song1-acapella-url]
8. Load MP3 file for Song 2 using Task 10 [song2-mp3]
9. Extract tempo for Song 2 using Task 33 [song2-tempo]
10. Detect key for Song 2 using new task/library [song2-key]
11. Extract instrumental stem for Song 2 using Task 36 [song2-instrumental]
12. Save instrumental stem MP3 for Song 2 to server using Task 9 [song2-instrumental-url]
13. Extract acapella stem for Song 2 using Task 36 [song2-acapella]
14. Save acapella stem MP3 for Song 2 to server using Task 9 [song2-acapella-url]
15. Load MP3 file for Song 3 using Task 10 [song3-mp3]
16. Extract tempo for Song 3 using Task 33 [song3-tempo]
17. Detect key for Song 3 using new task/library [song3-key]
18. Extract instrumental stem for Song 3 using Task 36 [song3-instrumental]
19. Save instrumental stem MP3 for Song 3 to server using Task 9 [song3-instrumental-url]
20. Extract acapella stem for Song 3 using Task 36 [song3-acapella]
21. Save acapella stem MP3 for Song 3 to server using Task 9 [song3-acapella-url]
22. Load MP3 file for Song 4 using Task 10 [song4-mp3]
23. Extract tempo for Song 4 using Task 33 [song4-tempo]
24. Detect key for Song 4 using new task/library [song4-key]
25. Extract instrumental stem for Song 4 using Task 36 [song4-instrumental]
26. Save instrumental stem MP3 for Song 4 to server using Task 9 [song4-instrumental-url]
27. Extract acapella stem for Song 4 using Task 36 [song4-acapella]
28. Save acapella stem MP3 for Song 4 to server using Task 9 [song4-acapella-url]
29. Create JSON object with analyzed data for all 4 songs [audio-analysis-json]
30. Save JSON file to server using Task 9 [audio-analysis-json-url]

H) OUTPUT TOKENS:
- [song1-mp3], [song2-mp3], [song3-mp3], [song4-mp3]
- [song1-tempo], [song2-tempo], [song3-tempo], [song4-tempo]
- [song1-key], [song2-key], [song3-key], [song4-key]
- [song1-instrumental], [song2-instrumental], [song3-instrumental], [song4-instrumental]
- [song1-instrumental-url], [song2-instrumental-url], [song3-instrumental-url], [song4-instrumental-url]
- [song1-acapella], [song2-acapella], [song3-acapella], [song4-acapella]
- [song1-acapella-url], [song2-acapella-url], [song3-acapella-url], [song4-acapella-url]
- [audio-analysis-json], [audio-analysis-json-url]
--------------

subagent2refined-repeating-json-core

Here is the taskflow summary for the Audio Analyzer & Processor subagent:

G) TASKFLOW SUMMARY

1. Title task: Load MP3 file
   Description of the task: Load an MP3 file from the provided URL using a file loading library or API.
   Input(s) involved: [MP3_URL]
   Output token name: [LOADED_MP3]

2. Title task: Extract tempo
   Description of the task: Analyze the loaded MP3 file to determine its tempo in beats per minute (BPM) using a tempo detection library or algorithm.
   Input(s) involved: [LOADED_MP3]
   Output token name: [TEMPO_BPM]

3. Title task: Detect key
   Description of the task: Analyze the loaded MP3 file to determine its musical key using a key detection library or algorithm.
   Input(s) involved: [LOADED_MP3]
   Output token name: [DETECTED_KEY]

4. Title task: Extract instrumental stem
   Description of the task: Separate the instrumental components from the loaded MP3 file using a source separation library or algorithm.
   Input(s) involved: [LOADED_MP3]
   Output token name: [INSTRUMENTAL_STEM]

5. Title task: Save instrumental stem MP3
   Description of the task: Save the extracted instrumental stem as a new MP3 file on the server using a file saving library or API.
   Input(s) involved: [INSTRUMENTAL_STEM]
   Output token name: [SAVED_INSTRUMENTAL_MP3_URL]

6. Title task: Extract acapella stem
   Description of the task: Separate the vocal components from the loaded MP3 file using a source separation library or algorithm.
   Input(s) involved: [LOADED_MP3]
   Output token name: [ACAPELLA_STEM]

7. Title task: Save acapella stem MP3
   Description of the task: Save the extracted acapella stem as a new MP3 file on the server using a file saving library or API.
   Input(s) involved: [ACAPELLA_STEM]
   Output token name: [SAVED_ACAPELLA_MP3_URL]

8. Title task: Create JSON object
   Description of the task: Generate a JSON object containing the analyzed data for all 4 songs, including tempo, key, and the URLs of the instrumental and acapella stem MP3s.
   Input(s) involved: [TEMPO_BPM_1], [DETECTED_KEY_1], [SAVED_INSTRUMENTAL_MP3_URL_1], [SAVED_ACAPELLA_MP3_URL_1], [TEMPO_BPM_2], [DETECTED_KEY_2], [SAVED_INSTRUMENTAL_MP3_URL_2], [SAVED_ACAPELLA_MP3_URL_2], [TEMPO_BPM_3], [DETECTED_KEY_3], [SAVED_INSTRUMENTAL_MP3_URL_3], [SAVED_ACAPELLA_MP3_URL_3], [TEMPO_BPM_4], [DETECTED_KEY_4], [SAVED_INSTRUMENTAL_MP3_URL_4], [SAVED_ACAPELLA_MP3_URL_4]
   Output token name: [ANALYZED_DATA_JSON]

9. Title task: Save JSON file
   Description of the task: Save the generated JSON object containing the analyzed data for all 4 songs as a JSON file on the server using a file saving library or API.
   Input(s) involved: [ANALYZED_DATA_JSON]
   Output token name: [SAVED_JSON_URL]

subagent2mermaid

<!DOCTYPE html>
<html>
<head>
  <title>Mermaid Diagram Example</title>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script>
</head>
<body>
  <div class="mermaid">
graph TD
    A[Load MP3 file for Song 1] --> B(Extract tempo for Song 1)
    A --> C(Detect key for Song 1)
    A --> D(Extract instrumental stem for Song 1)
    A --> F(Extract acapella stem for Song 1)
    D --> E(Save instrumental stem MP3 for Song 1 to server)
    F --> G(Save acapella stem MP3 for Song 1 to server)
    H[Load MP3 file for Song 2] --> I(Extract tempo for Song 2)
    H --> J(Detect key for Song 2)
    H --> K(Extract instrumental stem for Song 2)
    H --> M(Extract acapella stem for Song 2)
    K --> L(Save instrumental stem MP3 for Song 2 to server)
    M --> N(Save acapella stem MP3 for Song 2 to server)
    O[Load MP3 file for Song 3] --> P(Extract tempo for Song 3)
    O --> Q(Detect key for Song 3)
    O --> R(Extract instrumental stem for Song 3)
    O --> T(Extract acapella stem for Song 3)
    R --> S(Save instrumental stem MP3 for Song 3 to server)
    T --> U(Save acapella stem MP3 for Song 3 to server)
    V[Load MP3 file for Song 4] --> W(Extract tempo for Song 4)
    V --> X(Detect key for Song 4)
    V --> Y(Extract instrumental stem for Song 4)
    V --> AA(Extract acapella stem for Song 4)
    Y --> Z(Save instrumental stem MP3 for Song 4 to server)
    AA --> AB(Save acapella stem MP3 for Song 4 to server)
    B --> AC{Create JSON object with analyzed data for all 4 songs}
    C --> AC
    E --> AC
    G --> AC
    I --> AC
    J --> AC
    L --> AC
    N --> AC
    P --> AC
    Q --> AC
    S --> AC
    U --> AC
    W --> AC
    X --> AC
    Z --> AC
    AB --> AC
    AC --> AD(Save JSON file to server)
  </div>

SubAgent #3

claude 3 - subagent3

subagent1-refined

A) SUBAGENT SUMMARY: Identify the nearest downbeat to a user-specified start time in an audio file and cut a 64-beat segment from the instrumental and acapella stems.

B) FINAL TASK OUTPUT: Two MP3 audio files (one instrumental and one acapella), each containing a 64-beat segment starting from the nearest downbeat to the user-specified start time. The MP3 files should have a bitrate of 192 kbps and a sample rate of 44.1 kHz.

C) SUBAGENT INPUT:
- Analyzed audio data from the Audio Analyzer & Processor subagent, including:
  - MP3 URLs of the instrumental and acapella stems
  - Tempo (BPM) of each stem
  - User-specified start time (in seconds) for each stem

E) SUBAGENT TASK SUMMARY:
1. Receive the analyzed audio data (MP3 URLs, tempo, and user-specified start times) from the Audio Analyzer & Processor subagent.
2. For each stem (instrumental and acapella):
   a. Use Task 33 (Get tempo (BPM) of MP3) to extract the beatpoint timings of the stem.
   b. Process the beatpoint timings with an LLM to identify the nearest downbeat to the user-specified start time.
   c. Calculate the start time (in seconds) of the nearest downbeat.
   d. Use the calculated start time and the tempo to determine the end time of the 64-beat segment (end_time = start_time + (64 / (tempo / 60))).
   e. Use Task 45 (Split/extract video by interval) to extract the 64-beat segment from the stem MP3, specifying the calculated start and end times.
   f. Save the extracted segment as a new MP3 file using Task 9 (Save content to server).
3. Output the URLs of the two new MP3 files (64-beat instrumental and acapella segments) to the next subagent.

F) SILOS:
This subagent does not require silos as it processes the instrumental and acapella stems sequentially using the same set of tasks.

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive the analyzed audio data (MP3 URLs, tempo, and user-specified start times) from the Audio Analyzer & Processor subagent.

2. For the instrumental stem:
   a. Use Task 33 (Get tempo (BPM) of MP3) to extract the beatpoint timings of the instrumental stem.
   b. Process the beatpoint timings with an LLM to identify the nearest downbeat to the user-specified start time for the instrumental stem.
   c. Calculate the start time (in seconds) of the nearest downbeat for the instrumental stem.
   d. Use the calculated start time and the tempo to determine the end time of the 64-beat segment for the instrumental stem (end_time = start_time + (64 / (tempo / 60))).
   e. Use Task 45 (Split/extract video by interval) to extract the 64-beat segment from the instrumental stem MP3, specifying the calculated start and end times.
   f. Use Task 9 (Save content to server) to save the extracted instrumental segment as a new MP3 file.

3. For the acapella stem:
   a. Use Task 33 (Get tempo (BPM) of MP3) to extract the beatpoint timings of the acapella stem.
   b. Process the beatpoint timings with an LLM to identify the nearest downbeat to the user-specified start time for the acapella stem.
   c. Calculate the start time (in seconds) of the nearest downbeat for the acapella stem.
   d. Use the calculated start time and the tempo to determine the end time of the 64-beat segment for the acapella stem (end_time = start_time + (64 / (tempo / 60))).
   e. Use Task 45 (Split/extract video by interval) to extract the 64-beat segment from the acapella stem MP3, specifying the calculated start and end times.
   f. Use Task 9 (Save content to server) to save the extracted acapella segment as a new MP3 file.

4. Output the URLs of the two new MP3 files (64-beat instrumental and acapella segments) to the next subagent.

Subagent1 task-ID-numbers3

subagent3-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS):

1. Receive the analyzed audio data (MP3 URLs, tempo, and user-specified start times) from the Audio Analyzer & Processor subagent. 
   - Input tokens: [instrumental1-url], [instrumental2-url], [acapella1-url], [acapella2-url], [instrumental1-tempo], [instrumental2-tempo], [acapella1-tempo], [acapella2-tempo], [instrumental1-start-time], [instrumental2-start-time], [acapella1-start-time], [acapella2-start-time]

2. For each instrumental stem (repeat for instrumental1 and instrumental2):
   a. Use Task 33 (Get tempo (BPM) of MP3) to extract the beatpoint timings of the instrumental stem.
      - Input token: [instrumental1-url] or [instrumental2-url]
      - Output token: [instrumental1-beatpoints] or [instrumental2-beatpoints]
   b. Process the beatpoint timings with an LLM to identify the nearest downbeat to the user-specified start time for the instrumental stem.
      - Input tokens: [instrumental1-beatpoints] or [instrumental2-beatpoints], [instrumental1-start-time] or [instrumental2-start-time]
      - Output token: [instrumental1-nearest-downbeat] or [instrumental2-nearest-downbeat]
   c. Calculate the start time (in seconds) of the nearest downbeat for the instrumental stem.
      - Input token: [instrumental1-nearest-downbeat] or [instrumental2-nearest-downbeat]
      - Output token: [instrumental1-segment-start] or [instrumental2-segment-start]
   d. Use the calculated start time and the tempo to determine the end time of the 64-beat segment for the instrumental stem (end_time = start_time + (64 / (tempo / 60))).
      - Input tokens: [instrumental1-segment-start] or [instrumental2-segment-start], [instrumental1-tempo] or [instrumental2-tempo]
      - Output token: [instrumental1-segment-end] or [instrumental2-segment-end]
   e. Use Task 45 (Split/extract video by interval) to extract the 64-beat segment from the instrumental stem MP3, specifying the calculated start and end times.
      - Input tokens: [instrumental1-url] or [instrumental2-url], [instrumental1-segment-start] or [instrumental2-segment-start], [instrumental1-segment-end] or [instrumental2-segment-end]
      - Output token: [instrumental1-segment-mp3] or [instrumental2-segment-mp3]
   f. Use Task 9 (Save content to server) to save the extracted instrumental segment as a new MP3 file.
      - Input token: [instrumental1-segment-mp3] or [instrumental2-segment-mp3]
      - Output token: [instrumental1-segment-url] or [instrumental2-segment-url]

3. For each acapella stem (repeat for acapella1 and acapella2):
   a. Use Task 33 (Get tempo (BPM) of MP3) to extract the beatpoint timings of the acapella stem.
      - Input token: [acapella1-url] or [acapella2-url]
      - Output token: [acapella1-beatpoints] or [acapella2-beatpoints]
   b. Process the beatpoint timings with an LLM to identify the nearest downbeat to the user-specified start time for the acapella stem.
      - Input tokens: [acapella1-beatpoints] or [acapella2-beatpoints], [acapella1-start-time] or [acapella2-start-time]
      - Output token: [acapella1-nearest-downbeat] or [acapella2-nearest-downbeat]
   c. Calculate the start time (in seconds) of the nearest downbeat for the acapella stem.
      - Input token: [acapella1-nearest-downbeat] or [acapella2-nearest-downbeat]
      - Output token: [acapella1-segment-start] or [acapella2-segment-start]
   d. Use the calculated start time and the tempo to determine the end time of the 64-beat segment for the acapella stem (end_time = start_time + (64 / (tempo / 60))).
      - Input tokens: [acapella1-segment-start] or [acapella2-segment-start], [acapella1-tempo] or [acapella2-tempo]
      - Output token: [acapella1-segment-end] or [acapella2-segment-end]
   e. Use Task 45 (Split/extract video by interval) to extract the 64-beat segment from the acapella stem MP3, specifying the calculated start and end times.
      - Input tokens: [acapella1-url] or [acapella2-url], [acapella1-segment-start] or [acapella2-segment-start], [acapella1-segment-end] or [acapella2-segment-end]
      - Output token: [acapella1-segment-mp3] or [acapella2-segment-mp3]
   f. Use Task 9 (Save content to server) to save the extracted acapella segment as a new MP3 file.
      - Input token: [acapella1-segment-mp3] or [acapella2-segment-mp3]
      - Output token: [acapella1-segment-url] or [acapella2-segment-url]

4. Output the URLs of the eight new MP3 files (64-beat instrumental and acapella segments) to the next subagent.
   - Output tokens: [instrumental1-segment-url], [instrumental2-segment-url], [acapella1-segment-url], [acapella2-segment-url]

H) OUTPUT TOKENS:
- [instrumental1-url]
- [instrumental2-url]
- [acapella1-url]
- [acapella2-url]
- [instrumental1-tempo]
- [instrumental2-tempo]
- [acapella1-tempo]
- [acapella2-tempo]
- [instrumental1-start-time]
- [instrumental2-start-time]
- [acapella1-start-time]
- [acapella2-start-time]
- [instrumental1-beatpoints]
- [instrumental2-beatpoints]
- [instrumental1-nearest-downbeat]
- [instrumental2-nearest-downbeat]
- [instrumental1-segment-start]
- [instrumental2-segment-start]
- [instrumental1-segment-end]
- [instrumental2-segment-end]
- [instrumental1-segment-mp3]
- [instrumental2-segment-mp3]
- [instrumental1-segment-url]
- [instrumental2-segment-url]
- [acapella1-beatpoints]
- [acapella2-beatpoints]
- [acapella1-nearest-downbeat]
- [acapella2-nearest-downbeat]
- [acapella1-segment-start]
- [acapella2-segment-start]
- [acapella1-segment-end]
- [acapella2-segment-end]
- [acapella1-segment-mp3]
- [acapella2-segment-mp3]
- [acapella1-segment-url]
- [acapella2-segment-url]

subagent3refined-repeating-json-core

G) TASKFLOW SUMMARY

Title task: Extract beatpoint timings of instrumental stem
Description of the task: Use a tempo extraction tool to identify the beatpoint timings of the instrumental stem MP3 file.
Input(s) involved: [instrumental_stem_mp3]
Output token name: [instrumental_beatpoint_timings]

Title task: Identify nearest downbeat to start time for instrumental
Description of the task: Process the beatpoint timings with an LLM to identify the nearest downbeat to the user-specified start time for the instrumental stem.
Input(s) involved: [instrumental_beatpoint_timings], [instrumental_start_time]
Output token name: [instrumental_nearest_downbeat]

Title task: Calculate start time of nearest downbeat for instrumental
Description of the task: Convert the nearest downbeat information into a specific start time (in seconds) for the instrumental stem.
Input(s) involved: [instrumental_nearest_downbeat]
Output token name: [instrumental_segment_start_time]

Title task: Determine end time of 64-beat instrumental segment
Description of the task: Use the calculated start time and the tempo to determine the end time of the 64-beat segment for the instrumental stem.
Input(s) involved: [instrumental_segment_start_time], [instrumental_tempo]
Output token name: [instrumental_segment_end_time]

Title task: Extract 64-beat segment from instrumental stem
Description of the task: Use a video/audio extraction tool to extract the 64-beat segment from the instrumental stem MP3, specifying the calculated start and end times.
Input(s) involved: [instrumental_stem_mp3], [instrumental_segment_start_time], [instrumental_segment_end_time]
Output token name: [instrumental_64beat_segment]

Title task: Save instrumental segment as new MP3 file
Description of the task: Use a file saving tool to save the extracted instrumental segment as a new MP3 file on the server.
Input(s) involved: [instrumental_64beat_segment]
Output token name: [instrumental_segment_mp3_url]

Title task: Extract beatpoint timings of acapella stem
Description of the task: Use a tempo extraction tool to identify the beatpoint timings of the acapella stem MP3 file.
Input(s) involved: [acapella_stem_mp3]
Output token name: [acapella_beatpoint_timings]

Title task: Identify nearest downbeat to start time for acapella
Description of the task: Process the beatpoint timings with an LLM to identify the nearest downbeat to the user-specified start time for the acapella stem.
Input(s) involved: [acapella_beatpoint_timings], [acapella_start_time]
Output token name: [acapella_nearest_downbeat]

Title task: Calculate start time of nearest downbeat for acapella
Description of the task: Convert the nearest downbeat information into a specific start time (in seconds) for the acapella stem.
Input(s) involved: [acapella_nearest_downbeat]
Output token name: [acapella_segment_start_time]

Title task: Determine end time of 64-beat acapella segment
Description of the task: Use the calculated start time and the tempo to determine the end time of the 64-beat segment for the acapella stem.
Input(s) involved: [acapella_segment_start_time], [acapella_tempo]
Output token name: [acapella_segment_end_time]

Title task: Extract 64-beat segment from acapella stem
Description of the task: Use a video/audio extraction tool to extract the 64-beat segment from the acapella stem MP3, specifying the calculated start and end times.
Input(s) involved: [acapella_stem_mp3], [acapella_segment_start_time], [acapella_segment_end_time]
Output token name: [acapella_64beat_segment]

Title task: Save acapella segment as new MP3 file
Description of the task: Use a file saving tool to save the extracted acapella segment as a new MP3 file on the server.
Input(s) involved: [acapella_64beat_segment]
Output token name: [acapella_segment_mp3_url]

Title task: Output segment MP3 URLs to next subagent
Description of the task: Pass the URLs of the two new MP3 files (64-beat instrumental and acapella segments) to the next subagent for further processing.
Input(s) involved: [instrumental_segment_mp3_url], [acapella_segment_mp3_url]
Output token name: [segment_mp3_urls]

subagent3mermaid

prompts-required

Based on the components and outputs described above, here are the unique prompts that would need to be written for this agent workflow:

Task: Generate YouTube Search Queries
Output of the task: A list of search queries for each song provided by the user.
Description: A prompt that takes the user-provided song information ("artist name - song name") and generates suitable YouTube search queries to find the corresponding audio files. The prompt should handle potential variations in the song information and create queries that are likely to yield the desired audio files.

Task: Extract Audio from YouTube Videos
Output of the task: Temporary audio files downloaded from the YouTube videos.
Description: A prompt that guides the AI agent on how to use a YouTube downloading library or API to extract the audio from the YouTube videos obtained through the search queries. The prompt should provide instructions on handling potential errors, managing temporary files, and ensuring the audio quality is suitable for further processing.

Task: Analyze Audio Files
Output of the task: Extracted audio features for each song (BPM, key, acapella/instrumental stems, downbeats).
Description: A prompt that instructs the AI agent on using audio analysis libraries or APIs (such as librosa or Essentia) to analyze the downloaded audio files. The prompt should guide the agent on extracting the necessary audio features, including BPM, key information, acapella/instrumental stems, and downbeats. It should also handle potential errors and edge cases that may arise during the analysis process.

Task: Identify Nearest Downbeat and Cut Audio Segments
Output of the task: 64-beat audio segments cut from each audio file, starting from the nearest downbeat to the user-specified start time.
Description: A prompt that guides the AI agent on identifying the nearest downbeat to the user-specified start time in each audio file and cutting a 64-beat segment starting from that point. The prompt should provide instructions on handling cases where the user-specified start time is close to the end of the audio file or if there are any issues with the audio file itself.

Task: Calculate Pitch Shift and Time Stretch
Output of the task: The amount of pitch shift and time stretch required to match the acapella's tempo and key to the instrumental.
Description: A prompt that instructs the AI agent on calculating the necessary pitch shift and time stretch to match the acapella's tempo and key to the instrumental. The prompt should guide the agent on using the extracted audio features (BPM and key information) to determine the required adjustments. It should also handle potential edge cases and provide guidance on ensuring the pitch shift and time stretch values are within reasonable ranges.

Task: Apply Pitch Shift and Time Stretch
Output of the task: Adjusted acapella segments that match the tempo and key of their corresponding instrumental segments.
Description: A prompt that guides the AI agent on using an audio processing library (such as pydub or librosa) to apply the calculated pitch shift and time stretch to the acapella segments. The prompt should provide instructions on handling potential errors and ensuring the quality of the adjusted audio segments.

Task: Mix Acapella and Instrumental Segments
Output of the task: Four separate MP3 files, each containing a unique 64-beat remix/mashup.
Description: A prompt that instructs the AI agent on combining each adjusted acapella segment with its corresponding instrumental segment to create the final mashup MP3 files. The prompt should guide the agent on using an audio mixing library or API to perform the mixing process, handling potential audio alignment issues, and ensuring the resulting mashups sound seamless and harmonious.

These prompts cover the core tasks required for the AI agent to generate the desired remixes/mashups based on user input. Additional prompts may be necessary for error handling, file management, and user interaction, depending on the specific implementation of the agent workflow.

subagent1-json-array

[
  {
    "taskID": 1,
    "semanticTitleOfTask": "Parse user input",
    "taskDescription": "Parse the structured user input data and extract the song names (artist and title) for the 4 songs, as well as the specified start time for the mashup.",
    "inputDescription": "The structured user input data containing the song names and start time.",
    "inputRequired": ["user_input_data"],
    "outputDescription": "The extracted song names (artist and title) for the 4 songs and the specified start time for the mashup.",
    "outputName": ["extracted_song_names", "start_time"],
    "promptInstruction": ""
  },
  {
    "taskID": 2,
    "semanticTitleOfTask": "Search YouTube for Song 1 (Instrumental)",
    "taskDescription": "Use the extracted song name for Song 1 (Instrumental) to perform a YouTube search and retrieve the top video URL from the search results.",
    "inputDescription": "The extracted song name for Song 1 (Instrumental).",
    "inputRequired": ["extracted_song_names"],
    "outputDescription": "The top video URL from the YouTube search results for Song 1 (Instrumental).",
    "outputName": "song1_video_url",
    "promptInstruction": ""
  },
  {
    "taskID": 3,
    "semanticTitleOfTask": "Download YouTube video for Song 1 (Instrumental)",
    "taskDescription": "Download the YouTube video for Song 1 (Instrumental) using the retrieved video URL, saving the video as an MP4 file on the server.",
    "inputDescription": "The retrieved video URL for Song 1 (Instrumental).",
    "inputRequired": ["song1_video_url"],
    "outputDescription": "The downloaded MP4 file for Song 1 (Instrumental) saved on the server.",
    "outputName": "song1_mp4_file",
    "promptInstruction": ""
  },
  {
    "taskID": 4,
    "semanticTitleOfTask": "Convert MP4 to MP3 for Song 1 (Instrumental)",
    "taskDescription": "Convert the downloaded MP4 file for Song 1 (Instrumental) to MP3 format for further processing.",
    "inputDescription": "The downloaded MP4 file for Song 1 (Instrumental).",
    "inputRequired": ["song1_mp4_file"],
    "outputDescription": "The converted MP3 file for Song 1 (Instrumental).",
    "outputName": "song1_mp3_file",
    "promptInstruction": ""
  },
  {
    "taskID": 5,
    "semanticTitleOfTask": "Save MP3 file for Song 1 (Instrumental) to server",
    "taskDescription": "Save the converted MP3 file for Song 1 (Instrumental) to the server at a specified internal URL location for later access.",
    "inputDescription": "The converted MP3 file for Song 1 (Instrumental).",
    "inputRequired": ["song1_mp3_file"],
    "outputDescription": "The internal URL location of the saved MP3 file for Song 1 (Instrumental) on the server.",
    "outputName": "song1_mp3_url",
    "promptInstruction": ""
  },
  {
    "taskID": 6,
    "semanticTitleOfTask": "Search YouTube for Song 2 (Instrumental)",
    "taskDescription": "Use the extracted song name for Song 2 (Instrumental) to perform a YouTube search and retrieve the top video URL from the search results.",
    "inputDescription": "The extracted song name for Song 2 (Instrumental).",
    "inputRequired": ["extracted_song_names"],
    "outputDescription": "The top video URL from the YouTube search results for Song 2 (Instrumental).",
    "outputName": "song2_video_url",
    "promptInstruction": ""
  },
  {
    "taskID": 7,
    "semanticTitleOfTask": "Download YouTube video for Song 2 (Instrumental)",
    "taskDescription": "Download the YouTube video for Song 2 (Instrumental) using the retrieved video URL, saving the video as an MP4 file on the server.",
    "inputDescription": "The retrieved video URL for Song 2 (Instrumental).",
    "inputRequired": ["song2_video_url"],
    "outputDescription": "The downloaded MP4 file for Song 2 (Instrumental) saved on the server.",
    "outputName": "song2_mp4_file",
    "promptInstruction": ""
  },
  {
    "taskID": 8,
    "semanticTitleOfTask": "Convert MP4 to MP3 for Song 2 (Instrumental)",
    "taskDescription": "Convert the downloaded MP4 file for Song 2 (Instrumental) to MP3 format for further processing.",
    "inputDescription": "The downloaded MP4 file for Song 2 (Instrumental).",
    "inputRequired": ["song2_mp4_file"],
    "outputDescription": "The converted MP3 file for Song 2 (Instrumental).",
    "outputName": "song2_mp3_file",
    "promptInstruction": ""
  },
  {
    "taskID": 9,
    "semanticTitleOfTask": "Save MP3 file for Song 2 (Instrumental) to server",
    "taskDescription": "Save the converted MP3 file for Song 2 (Instrumental) to the server at a specified internal URL location for later access.",
    "inputDescription": "The converted MP3 file for Song 2 (Instrumental).",
    "inputRequired": ["song2_mp3_file"],
    "outputDescription": "The internal URL location of the saved MP3 file for Song 2 (Instrumental) on the server.",
    "outputName": "song2_mp3_url",
    "promptInstruction": ""
  },
  {
    "taskID": 10,
    "semanticTitleOfTask": "Search YouTube for Song 3 (Acapella)",
    "taskDescription": "Use the extracted song name for Song 3 (Acapella) to perform a YouTube search and retrieve the top video URL from the search results.",
    "inputDescription": "The extracted song name for Song 3 (Acapella).",
    "inputRequired": ["extracted_song_names"],
    "outputDescription": "The top video URL from the YouTube search results for Song 3 (Acapella).",
    "outputName": "song3_video_url",
    "promptInstruction": ""
  },
  {
    "taskID": 11,
    "semanticTitleOfTask": "Download YouTube video for Song 3 (Acapella)",
    "taskDescription": "Download the YouTube video for Song 3 (Acapella) using the retrieved video URL, saving the video as an MP4 file on the server.",
    "inputDescription": "The retrieved video URL for Song 3 (Acapella).",
    "inputRequired": ["song3_video_url"],
    "outputDescription": "The downloaded MP4 file for Song 3 (Acapella) saved on the server.",
    "outputName": "song3_mp4_file",
    "promptInstruction": ""
  },
  {
    "taskID": 12,
    "semanticTitleOfTask": "Convert MP4 to MP3 for Song 3 (Acapella)",
    "taskDescription": "Convert the downloaded MP4 file for Song 3 (Acapella) to MP3 format for further processing.",
    "inputDescription": "The downloaded MP4 file for Song 3 (Acapella).",
    "inputRequired": ["song3_mp4_file"],
    "outputDescription": "The converted MP3 file for Song 3 (Acapella).",
    "outputName": "song3_mp3_file",
    "promptInstruction": ""
  },
  {
    "taskID": 13,
    "semanticTitleOfTask": "Save MP3 file for Song 3 (Acapella) to server",
    "taskDescription": "Save the converted MP3 file for Song 3 (Acapella) to the server at a specified internal URL location for later access.",
    "inputDescription": "The converted MP3 file for Song 3 (Acapella).",
    "inputRequired": ["song3_mp3_file"],
    "outputDescription": "The internal URL location of the saved MP3 file for Song 3 (Acapella) on the server.",
    "outputName": "song3_mp3_url",
    "promptInstruction": ""
  },
  {
    "taskID": 14,
    "semanticTitleOfTask": "Search YouTube for Song 4 (Acapella)",
    "taskDescription": "Use the extracted song name for Song 4 (Acapella) to perform a YouTube search and retrieve the top video URL from the search results.",
    "inputDescription": "The extracted song name for Song 4 (Acapella).",
    "inputRequired": ["extracted_song_names"],
    "outputDescription": "The top video URL from the YouTube search results for Song 4 (Acapella).",
    "outputName": "song4_video_url",
    "promptInstruction": ""
  },
  {
    "taskID": 15,
    "semanticTitleOfTask": "Download YouTube video for Song 4 (Acapella)",
    "taskDescription": "Download the YouTube video for Song 4 (Acapella) using the retrieved video URL, saving the video as an MP4 file on the server.",
    "inputDescription": "The retrieved video URL for Song 4 (Acapella).",
    "inputRequired": ["song4_video_url"],
    "outputDescription": "The downloaded MP4 file for Song 4 (Acapella) saved on the server.",
    "outputName": "song4_mp4_file",
    "promptInstruction": ""
  },
  {
    "taskID": 16,
    "semanticTitleOfTask": "Convert MP4 to MP3 for Song 4 (Acapella)",
    "taskDescription": "Convert the downloaded MP4 file for Song 4 (Acapella) to MP3 format for further processing.",
    "inputDescription": "The downloaded MP4 file for Song 4 (Acapella).",
    "inputRequired": ["song4_mp4_file"],
    "outputDescription": "The converted MP3 file for Song 4 (Acapella).",
    "outputName": "song4_mp3_file",
    "promptInstruction": ""
  },
  {
    "taskID": 17,
    "semanticTitleOfTask": "Save MP3 file for Song 4 (Acapella) to server",
    "taskDescription": "Save the converted MP3 file for Song 4 (Acapella) to the server at a specified internal URL location for later access.",
    "inputDescription": "The converted MP3 file for Song 4 (Acapella).",
    "inputRequired": ["song4_mp3_file"],
    "outputDescription": "The internal URL location of the saved MP3 file for Song 4 (Acapella) on the server.",
    "outputName": "song4_mp3_url",
    "promptInstruction": ""
  },
  {
    "taskID": 18,
    "semanticTitleOfTask": "Output internal URL locations of downloaded MP3 files",
    "taskDescription": "Output the internal URL locations of the 4 downloaded and converted MP3 files for the instrumental and acapella tracks.",
    "inputDescription": "The internal URL locations of the 4 downloaded and converted MP3 files for the instrumental and acapella tracks.",
    "inputRequired": ["song1_mp3_url", "song2_mp3_url", "song3_mp3_url", "song4_mp3_url"],
    "outputDescription": "The internal URL locations of the 4 downloaded and converted MP3 files for the instrumental and acapella tracks.",
    "outputName": "mp3_file_urls",
    "promptInstruction": ""
  }
]

subagent1-json-array-mermaid

<!DOCTYPE html>
<html>
<head>
  <title>Mermaid Diagram Example</title>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script>
</head>
<body>
  <div class="mermaid">
graph TD
    A[Parse user input] --> B{extracted_song_names}
    A --> C{start_time}
    B --> D[Search YouTube for Song 1 Instrumental]
    D --> E{song1_video_url}
    E --> F[Download YouTube video for Song 1 Instrumental]
    F --> G{song1_mp4_file}
    G --> H[Convert MP4 to MP3 for Song 1 Instrumental]
    H --> I{song1_mp3_file}
    I --> J[Save MP3 file for Song 1 Instrumental to server]
    J --> K{song1_mp3_url}
    B --> L[Search YouTube for Song 2 Instrumental]
    L --> M{song2_video_url}
    M --> N[Download YouTube video for Song 2 Instrumental]
    N --> O{song2_mp4_file}
    O --> P[Convert MP4 to MP3 for Song 2 Instrumental]
    P --> Q{song2_mp3_file}
    Q --> R[Save MP3 file for Song 2 Instrumental to server]
    R --> S{song2_mp3_url}
    B --> T[Search YouTube for Song 3 Acapella]
    T --> U{song3_video_url}
    U --> V[Download YouTube video for Song 3 Acapella]
    V --> W{song3_mp4_file}
    W --> X[Convert MP4 to MP3 for Song 3 Acapella]
    X --> Y{song3_mp3_file}
    Y --> Z[Save MP3 file for Song 3 Acapella to server]
    Z --> AA{song3_mp3_url}
    B --> AB[Search YouTube for Song 4 Acapella]
    AB --> AC{song4_video_url}
    AC --> AD[Download YouTube video for Song 4 Acapella]
    AD --> AE{song4_mp4_file}
    AE --> AF[Convert MP4 to MP3 for Song 4 Acapella]
    AF --> AG{song4_mp3_file}
    AG --> AH[Save MP3 file for Song 4 Acapella to server]
    AH --> AI{song4_mp3_url}
    K & S & AA & AI --> AJ[Output internal URL locations of downloaded MP3 files]
    AJ --> AK{mp3_file_urls}
  </div>

subagent2-json-array

[
  {
    "taskID": 1,
    "semanticTitleOfTask": "Load MP3 file",
    "taskDescription": "Load an MP3 file from the provided URL using a file loading library or API.",
    "inputDescription": "The URL of the MP3 file to be loaded.",
    "inputRequired": ["MP3_URL"],
    "outputDescription": "The loaded MP3 file in memory or as a temporary file.",
    "outputName": "LOADED_MP3",
    "promptInstruction": ""
  },
  {
    "taskID": 2,
    "semanticTitleOfTask": "Extract tempo",
    "taskDescription": "Analyze the loaded MP3 file to determine its tempo in beats per minute (BPM) using a tempo detection library or algorithm.",
    "inputDescription": "The loaded MP3 file from the previous task.",
    "inputRequired": ["LOADED_MP3"],
    "outputDescription": "The tempo of the MP3 file in beats per minute (BPM).",
    "outputName": "TEMPO_BPM",
    "promptInstruction": ""
  },
  {
    "taskID": 3,
    "semanticTitleOfTask": "Detect key",
    "taskDescription": "Analyze the loaded MP3 file to determine its musical key using a key detection library or algorithm.",
    "inputDescription": "The loaded MP3 file from the first task.",
    "inputRequired": ["LOADED_MP3"],
    "outputDescription": "The detected musical key of the MP3 file.",
    "outputName": "DETECTED_KEY",
    "promptInstruction": ""
  },
  {
    "taskID": 4,
    "semanticTitleOfTask": "Extract instrumental stem",
    "taskDescription": "Separate the instrumental components from the loaded MP3 file using a source separation library or algorithm.",
    "inputDescription": "The loaded MP3 file from the first task.",
    "inputRequired": ["LOADED_MP3"],
    "outputDescription": "The extracted instrumental stem as an audio file or audio data.",
    "outputName": "INSTRUMENTAL_STEM",
    "promptInstruction": ""
  },
  {
    "taskID": 5,
    "semanticTitleOfTask": "Save instrumental stem MP3",
    "taskDescription": "Save the extracted instrumental stem as a new MP3 file on the server using a file saving library or API.",
    "inputDescription": "The extracted instrumental stem from the previous task.",
    "inputRequired": ["INSTRUMENTAL_STEM"],
    "outputDescription": "The URL or file path of the saved instrumental stem MP3 file.",
    "outputName": "SAVED_INSTRUMENTAL_MP3_URL",
    "promptInstruction": ""
  },
  {
    "taskID": 6,
    "semanticTitleOfTask": "Extract acapella stem",
    "taskDescription": "Separate the vocal components from the loaded MP3 file using a source separation library or algorithm.",
    "inputDescription": "The loaded MP3 file from the first task.",
    "inputRequired": ["LOADED_MP3"],
    "outputDescription": "The extracted acapella stem as an audio file or audio data.",
    "outputName": "ACAPELLA_STEM",
    "promptInstruction": ""
  },
  {
    "taskID": 7,
    "semanticTitleOfTask": "Save acapella stem MP3",
    "taskDescription": "Save the extracted acapella stem as a new MP3 file on the server using a file saving library or API.",
    "inputDescription": "The extracted acapella stem from the previous task.",
    "inputRequired": ["ACAPELLA_STEM"],
    "outputDescription": "The URL or file path of the saved acapella stem MP3 file.",
    "outputName": "SAVED_ACAPELLA_MP3_URL",
    "promptInstruction": ""
  },
  {
    "taskID": 8,
    "semanticTitleOfTask": "Create JSON object",
    "taskDescription": "Generate a JSON object containing the analyzed data for all 4 songs, including tempo, key, and the URLs of the instrumental and acapella stem MP3s.",
    "inputDescription": "The tempo, detected key, and saved instrumental and acapella MP3 URLs for all 4 songs.",
    "inputRequired": [
      "TEMPO_BPM_1",
      "DETECTED_KEY_1",
      "SAVED_INSTRUMENTAL_MP3_URL_1",
      "SAVED_ACAPELLA_MP3_URL_1",
      "TEMPO_BPM_2",
      "DETECTED_KEY_2",
      "SAVED_INSTRUMENTAL_MP3_URL_2",
      "SAVED_ACAPELLA_MP3_URL_2",
      "TEMPO_BPM_3",
      "DETECTED_KEY_3",
      "SAVED_INSTRUMENTAL_MP3_URL_3",
      "SAVED_ACAPELLA_MP3_URL_3",
      "TEMPO_BPM_4",
      "DETECTED_KEY_4",
      "SAVED_INSTRUMENTAL_MP3_URL_4",
      "SAVED_ACAPELLA_MP3_URL_4"
    ],
    "outputDescription": "A JSON object containing the analyzed data for all 4 songs.",
    "outputName": "ANALYZED_DATA_JSON",
    "promptInstruction": ""
  },
  {
    "taskID": 9,
    "semanticTitleOfTask": "Save JSON file",
    "taskDescription": "Save the generated JSON object containing the analyzed data for all 4 songs as a JSON file on the server using a file saving library or API.",
    "inputDescription": "The JSON object containing the analyzed data for all 4 songs.",
    "inputRequired": ["ANALYZED_DATA_JSON"],
    "outputDescription": "The URL or file path of the saved JSON file.",
    "outputName": "SAVED_JSON_URL",
    "promptInstruction": ""
  }
]

remix mashup 2x2 youtube

Sub Agent #1 | Sub Agent #2 | Sub Agent #3

final-output-comps..1

final-output-comps..2

final-output-comps GPT..3

CLAUDE:- final-output-and-component-outputs

claude 3 - extract final output

extract component outputs

Claude - research-ideas-and-input-variables

GPT - research-ideas-and-input-variables

template-ideas

claude 3 - searches

subagent-summary

subagent-summary-amended-for-actual-tasks..1

subagent-summary-amended-for-actual-tasks..2

subagent-summary-amended-for-actual-tasks GPT..3

subagent-summary-amended-for-actual-tasks

random-autocomplete

claude 3 - mermaid-for-all

SubAgent #1

claude 3 - subagent1

subagent1-refined

subagent1refined-repeating

Subagent1 task-ID-numbers

subagent1-output-variables

subagent1refined-repeating-json-core

subagent1mermaid

SubAgent #2

claude 3 - subagent2

subagent2-refined

subagent2refined-repeating

Subagent2 task-ID-numbers

subagent2-output-variables

subagent2refined-repeating-json-core

subagent2mermaid

SubAgent #3

claude 3 - subagent3

subagent1-refined

subagent1refined-repeating

Subagent1 task-ID-numbers3

subagent3-output-variables

subagent3refined-repeating-json-core

subagent3mermaid

prompts-required

subagent1-json-array

subagent1-json-array-mermaid

subagent2-json-array

subagent2-json-array-mermaid