1. Sample pack from YT video

I want to input a youtube video, then get a collection of music samples returned to me (.wav files in a zip). I think I should specify 3 specific times (eg 5 seconds, 130 seconds, 190 seconds, and then the agent rip rip the audio file from YouTube, then extract stems (instrumentals, vocals), and then extract (say) 16 beats worth of audio - beginning from the 3 timings I've entered. This gives us 3x separate audio sections to sample, and if we then separate them into vocals and instrumentals that then gives us 6x separate audio files to sample from (although we will use the initial tempo/beatpoint for all 6). Next we will want to use the beatpoint and tempo to extract samples (eg 1/2/4/8 beat in lengths, beginning from the beatpoints), potentially using SOX. So we chopup the samples using SOX, perhaps chopping based on beat length (if BPM = 90bpm, then 1 beat is 0.66secs) and beat position, and chopping maybe a mix of 1/2/4/8 beat samples. This will likely give us far too many samples, so we may want to finally filter them down (to "the best 48 samples"). We could do this by using a library like Librosa to filter somehow (eg based on average amplitude). Perhaps we could run 1-3 analyses of each sample, score them based on results, and then rank them based on the total of these results -- or to keep things simple just rank them based on a single score (average amplitude?) We will likely need to calculate how many samples we are going to get, how/if to rank them, and then generate them acccordingly. I am not sure whether we should filter or just do a bulk import. Finally we will want to save all the files to a server and zip them up.

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

final-output-comps..1

Thank you for the detailed explanation of your desired automated workflow. Let me address your questions one by one to ensure I have a clear understanding of the final output and the components required.

a) Final output:
The final output of this automated workflow would be a zip file containing a collection of audio samples (.wav files) extracted from a specified YouTube video. These samples would be derived from three specific time points in the video, separated into instrumental and vocal stems, and then further divided into 1, 2, 4, and 8 beat lengths based on the tempo of the audio at those time points. The samples would be ranked and filtered based on certain criteria (e.g., average amplitude) to include only the best 48 samples in the final zip file.

b) Core components/outputs:
1. YouTube video input: The user provides a link to a YouTube video as the input for the workflow.

2. Audio extraction: The audio from the specified YouTube video is extracted and saved as an audio file (e.g., .mp3).

3. Stem separation: The extracted audio is processed to separate the instrumental and vocal stems, resulting in two separate audio files.

4. Time point specification: The user provides three specific time points (e.g., 5 seconds, 130 seconds, 190 seconds) from which to extract samples.

5. Beat detection: The tempo (BPM) and beat positions of the audio at each specified time point are detected using a library like Librosa.

6. Sample extraction: Based on the detected beat positions and tempo, audio samples of 1, 2, 4, and 8 beat lengths are extracted from each time point for both the instrumental and vocal stems. This process may involve using a tool like SOX for precise audio cutting.

7. Sample analysis and ranking: The extracted samples are analyzed using a library like Librosa to calculate metrics such as average amplitude. The samples are then ranked based on these metrics to determine the best 48 samples.

8. Sample filtering (optional): If the number of extracted samples exceeds the desired 48, the lowest-ranked samples are filtered out.

9. Sample export: The selected 48 samples (or all samples if no filtering is applied) are exported as individual .wav files.

10. Zip file creation: All the exported .wav files are compressed into a single zip file.

11. Server upload: The zip file containing the audio samples is uploaded to a specified server for storage and access.

These core components represent the main steps and outputs required to generate the final output (the zip file with audio samples) based on the given specifications. The automated workflow would need to be designed to handle each of these components in the correct order and with the necessary parameters to achieve the desired result.

final-output-comps..2

## Understanding the Final Output and Components

### a) Final Output

The final output of this automated workflow would be **a zip file containing 48 audio samples (WAV format)** extracted and processed from the input YouTube video. These samples will represent a curated selection of musical snippets, ready for use in music production or other creative applications.

### b) Components/Outputs Needed

To achieve this final output, the process can be broken down into several key components:

**1. Audio Extraction and Stem Separation:**

* **Input:** YouTube video URL and three timestamps (e.g., 5 seconds, 130 seconds, 190 seconds).
* **Outputs:**
    * **Extracted Audio:** The entire audio track from the YouTube video will be ripped and saved as a temporary WAV file.
    * **Separated Stems:**  The extracted audio will be further processed to separate vocals and instrumentals, resulting in two separate WAV files (one for vocals and one for instrumentals). 
    
**2. Sample Generation:**

* **Inputs:** 
    * Separated Stems (vocals and instrumentals)
    * Three timestamps for starting points
    * Tempo (BPM) of the audio
    * Beat information (beat positions)
* **Process:**
    * **Sample Chopping:** Each of the three sections from the vocals and instrumental stems (totaling 6 sections) will be chopped into smaller samples based on the beat information and desired lengths (e.g., 1/2/4/8 beats). This can be achieved using a tool like SOX, which allows precise audio manipulation based on timing and beat positions.
    * **Sample Filtering (Optional):** Depending on the number of generated samples, a filtering process might be implemented. This could involve using a library like Librosa to analyze each sample based on features like average amplitude and ranking them to select the "best" 48 samples.

**3. Sample Organization and Packaging:**

* **Inputs:** 48 selected audio samples (WAV format)
* **Process:**
    * The selected samples will be organized and saved to a designated location on the server.
    * All 48 samples will be compressed and packaged into a single zip file.

**4. Delivery:**

* **Output:** The final zip file containing the 48 curated audio samples. This file would be delivered to the user, potentially through download or integration with other platforms.

final-output-comps GPT..3

a) The final output of this task would be a zip file containing specific audio samples extracted from a YouTube video. These samples will be in .wav format and be based on specified timings and lengths, chopped from the instrumentation and vocals obtained from the source video. Additionally, the samples will have been filtered based on specific parameters (such as average amplitude).

b) The task can be broken down into following core components/outputs:

1. YouTube Video Rip: This is the extraction of the raw audio data from the YouTube video. The output will be an audio file format that can further be processed, such as .wav or .mp3.

2. Audio File Stems: Separate out the vocals and instrumentation from the audio file obtained above. The output here would be two separate audio files, one containing only vocals and the other containing only the instrumentals.

3. Timed Audio Sections: Extract three separate sections of audio, each defined by start times input by the user. The output would be six distinct audio files, three extracted from vocals and three extracted from instrumentals.

4. Beat Extraction: Use beat and tempo data from the song to extract different lengths of samples (1, 2, 4, 8 beats) from the timing points specified earlier. The output would be audio files of each sample corresponding to each time point and beat length.

5. Sample Filtering: This will require the use of an audio analysis library, like Librosa, to filter out and rank the samples according to some criteria, like average amplitude for instance. The output would be a curated subset of the total samples deemed the "best" according to the criteria.

6. Sample Export and Compression: After filtering and ranking, the selected samples will need to be saved as .wav files and compressed into a zip file. This zip file, containing the final selections of audio samples, is the final output of this project.

Each component needs to be handled with specific tools or libraries that can perform the required tasks. The video rip may require a library or API like youtube-dl or pytube, while separating the stems and extracting beats may call for audio processing libraries like Spleeter and Librosa. The sample filtering also may employ Librosa for audio analysis. Finally, the zipping and storage of audio files can be performed using standard Python libraries like zipfile and os, or specific cloud storage SDKs if the file is to be stored on a cloud platform.

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

a) Final output:
The final output of this automated workflow would be a zip file containing a curated selection of 48 audio samples in .wav format. These samples would be extracted from a specified YouTube video, based on three user-defined timestamps (e.g., 5 seconds, 130 seconds, 190 seconds). For each timestamp, the workflow would extract both instrumental and vocal stems, resulting in a total of six audio sections. These sections would then be further divided into samples of varying lengths (1, 2, 4, or 8 beats) based on the detected tempo and beat positions of the audio. The resulting samples would be analyzed and ranked using criteria such as average amplitude, and the top 48 samples would be selected for inclusion in the final zip file.

b) Components of the task:
To achieve the final output, the following components would be required:

1. YouTube video input: The user provides a YouTube video URL and three specific timestamps as input to the workflow.

2. Audio extraction: The audio from the specified YouTube video is downloaded using a library like youtube-dl or a YouTube API.

3. Stem separation: The extracted audio is processed to separate the instrumental and vocal stems using libraries like Spleeter or Open-Unmix, resulting in two separate audio files.

4. Beat detection and tempo estimation: The beat positions and tempo (BPM) of the audio are detected using libraries like Librosa or Essentia.

5. Sample extraction: Based on the detected beat positions and tempo, audio samples of varying lengths (1, 2, 4, or 8 beats) are extracted from each of the six audio sections (three timestamps, each with instrumental and vocal stems). This can be done using libraries like Pydub or SOX.

6. Sample analysis and ranking: The extracted samples are analyzed using audio features like average amplitude, and a ranking algorithm is applied to determine the "best" samples. Librosa can be used for audio analysis, and custom ranking algorithms can be implemented.

7. Sample filtering: The ranked samples are filtered to select the top 48 samples for inclusion in the final output.

8. File storage and compression: The selected audio samples are saved as individual .wav files and compressed into a single zip file. This can be done using Python libraries like zipfile and os, or cloud storage SDKs if the files are to be stored on a cloud platform.

9. Output delivery: The final zip file containing the 48 audio samples is made available to the user, either through a download link or by sending it to a specified destination.

These components would need to be integrated into a cohesive workflow, with appropriate error handling, logging, and user feedback mechanisms. The workflow could be orchestrated using a task queue system like Celery or a workflow management tool like Apache Airflow.

The specific implementation details of each component would depend on the chosen libraries, APIs, and programming language (e.g., Python). The components would likely require configuration options to specify parameters such as sample lengths, ranking criteria, and output destinations. Additionally, considerations for scalability, performance, and security should be taken into account when designing and implementing the automated workflow.

claude 3 - extract final output

extract component outputs

To achieve the final output, the following components would be required:

1. YouTube video input: The user provides a YouTube video URL and three specific timestamps as input to the workflow.

2. Audio extraction: The audio from the specified YouTube video is downloaded using a library like youtube-dl or a YouTube API.

3. Stem separation: The extracted audio is processed to separate the instrumental and vocal stems using libraries like Spleeter or Open-Unmix, resulting in two separate audio files.

4. Beat detection and tempo estimation: The beat positions and tempo (BPM) of the audio are detected using libraries like Librosa or Essentia.

7. Sample filtering: The ranked samples are filtered to select the top 48 samples for inclusion in the final output.

9. Output delivery: The final zip file containing the 48 audio samples is made available to the user, either through a download link or by sending it to a specified destination.

Claude - research-ideas-and-input-variables

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

a) Questions to clarify the final output:
- How many total samples do you want in the final output? (e.g., 48, 96, or more)
- What file format should the samples be in? (e.g., .wav, .mp3)
- Should the samples be labeled or named in any specific way?
- Do you have a preference for the length of the samples? (e.g., 1, 2, 4, 8 beats)
- Should the samples be organized in any particular manner within the zip file?

b) Inputs the task should accept before launching:
- The YouTube video URL
- Three specific timestamps for audio extraction (e.g., 5s, 130s, 190s)
- The desired number of samples in the final output
- (Optional) Preferred sample lengths (e.g., 1, 2, 4, 8 beats)
- (Optional) Any specific naming convention for the samples

c) Potential task segmentation:
I believe the task can be effectively segmented into the following specialized areas:

1. Audio Extraction:
   - Download the audio from the YouTube video
   - Extract the audio at the specified timestamps
   - Separate the audio into instrumental and vocal stems

2. Sample Creation:
   - Analyze the tempo and beat positions of the extracted audio sections
   - Chop the audio into samples based on beat lengths (e.g., 1, 2, 4, 8 beats)
   - Apply any necessary processing or effects to the samples

3. Sample Selection:
   - Analyze the samples using Librosa or other audio analysis libraries
   - Calculate relevant metrics (e.g., average amplitude) for each sample
   - Rank the samples based on the chosen metric(s)
   - Select the top N samples (e.g., 48) based on the ranking

4. Output Generation:
   - Apply any desired naming convention to the selected samples
   - Organize the samples into a structured directory or zip file
   - Upload the final output to the specified server or location

By segmenting the task into these specialized areas, each part can be handled more efficiently and with better clarity. This also allows for easier troubleshooting and refinement of each stage in the workflow.

GPT - research-ideas-and-input-variables

1. What is the desired format of the final music samples (WAV, MP3, etc.)?
2. How long should each of the music samples be? 
3. How many samples should be extracted from each section?
4. Are there any specific genres or types of music you're targeting with this task? 
5. When you mention filtering down to "the best 48 samples", what criteria do you want us to use to define the 'best' samples?
6. What "average amplitude" range are you interested in for the final samples?
7. In case there are any other audio elements in the YouTube video besides vocals and instrumentals, do you want them included as well?

1. The URL of the YouTube video to be processed.
2. The specific timestamps from which to extract the audio samples.
3. The desired beat length (1/2/4/8 beats) for the samples.
4. Desired criteria for filtering and ranking the final samples (based on amplitude or any other parameter). 
5. Information about the server where the files will be saved and the format for zipping them up.
6. Any specific naming conventions for the audio files.
7. Any specific settings or parameters for using SOX and Librosa libraries.

template-ideas

Based on the provided information and the final output description, here are some templates and resources that could be collected to help define a successful output for the automated workflow:

1. Product Descriptions:
   - Visit online marketplaces or websites that sell audio sample packs, such as Splice, Loopmasters, or Beatport. Review the product descriptions of popular and highly-rated sample packs to understand the typical contents, file formats, and lengths of samples included in successful packs.

2. User Reviews:
   - Read user reviews and feedback on these online marketplaces to gain insights into what customers appreciate in high-quality sample packs. Look for comments on the diversity of samples, audio quality, usability, and overall satisfaction.

3. Audio Sample Previews:
   - Listen to audio previews or demos of top-selling sample packs to get a sense of the quality, variety, and style of samples that are well-received by users. While the LLM Agent cannot directly analyze the audio, it can read descriptions or transcriptions of the previews to understand the key characteristics.

4. Screenshots or Images:
   - Collect screenshots or images of the file structures and contents of successful sample packs. This will provide a visual reference for how the samples are typically organized and named within the zip file.

5. Technical Specifications:
   - Gather information on the common technical specifications of popular sample packs, such as file formats (e.g., .wav), sample rates (e.g., 44.1kHz), bit depths (e.g., 24-bit), and file sizes. This will help ensure that the generated samples meet industry standards.

6. Tutorials or Blog Posts:
   - Look for tutorials, blog posts, or articles written by experienced producers or sound designers that discuss the characteristics of high-quality audio samples. These resources may provide valuable insights into what makes a sample pack useful and desirable for music production.

7. Social Media Discussions:
   - Explore forums, Reddit threads, or social media posts where music producers discuss their favorite sample packs and what they look for in a pack. This can provide real-world opinions and preferences from the target audience.

8. Comparison with Similar Products:
   - Analyze the features and contents of similar automated audio sample generation tools or services, if available. Compare their output with the desired final output of your workflow to identify any gaps or areas for improvement.

By collecting and reviewing these templates and resources, you can gain a clearer understanding of what constitutes a successful output for your automated audio sample generation workflow. This information can guide the development process and help ensure that the generated samples meet the expectations and needs of potential users.

claude 3 - searches

Here are 5 specific keyphrases you can search to find helpful templates and refine the definition of a successful output for your automated audio sample generation workflow:

1. "splice drum loop pack top selling wav samples"
   - This search will help you find top-selling drum loop sample packs on Splice, a popular platform for music producers. By reviewing the product descriptions, contents, and user reviews of these packs, you can gain insights into what makes a successful collection of audio samples.

2. "loopmasters best selling vocal sample pack description"
   - Searching for the best-selling vocal sample packs on Loopmasters will provide examples of high-quality vocal samples and how they are described and packaged. This can help you understand the characteristics and features that appeal to music producers.

3. "beatport top rated one shot sample pack screenshots"
   - By searching for screenshots of top-rated one-shot sample packs on Beatport, you can visualize how successful sample packs are typically organized and named within a zip file. This will provide a reference for structuring your own generated samples.

4. "reddit music production best sample packs discussion"
   - This search will lead you to Reddit threads where music producers discuss their favorite sample packs and what they look for in a pack. These discussions can offer valuable insights into the preferences and needs of your target audience.

5. "youtube music producer tutorial choosing high quality audio samples"
   - Searching for YouTube tutorials by experienced music producers on choosing high-quality audio samples will provide guidance on what makes a sample pack useful and desirable. You can review the video transcriptions or descriptions to extract key points and tips.

By running these specific searches and reviewing the top results, you should be able to collect relevant templates and examples that will help refine your understanding of what constitutes a successful output for your automated audio sample generation workflow. The insights gained from these searches can guide the development process and ensure that your generated samples meet the expectations of potential users.

Splice Drum Loop Pack Top Selling WAV Samples
Loopmasters Best Selling Vocal Sample Pack Description
Beatport Top Rated One Shot Sample Pack Screenshots
Reddit Music Production Best Sample Packs Discussion
YouTube Music Producer Tutorial Choosing High Quality Audio Samples

subagent-summary

A) FULL TASK SUMMARY: Create an automated workflow that extracts and curates audio samples from a YouTube video based on user-specified timestamps and criteria.

B) FINAL TASK OUTPUT: A zip file containing 48 high-quality audio samples in .wav format, each lasting 1, 2, 4, or 8 beats, extracted from instrumental and vocal stems of a YouTube video at three user-specified timestamps.

C) COMPONENTS OVERVIEW:
1. YouTube video input and audio extraction
2. Stem separation (instrumental and vocal)
3. Beat detection and tempo estimation
4. Sample extraction based on beat positions and lengths
5. Sample analysis and ranking
6. Sample filtering and selection
7. File storage and compression
8. Output delivery

D) OVERVIEW OF SUBAGENTS:
1. YouTube Downloader: Extracts audio from a user-specified YouTube video.
2. Stem Separator: Separates the extracted audio into instrumental and vocal stems.
3. Beat Detector: Detects beat positions and estimates the tempo of the audio.
4. Sample Extractor: Extracts audio samples of varying lengths based on beat positions.
5. Sample Analyzer: Analyzes and ranks the extracted samples based on audio features.
6. Sample Curator: Filters and selects the top 48 samples for the final output.
7. File Manager: Saves the selected samples as .wav files and compresses them into a zip file.
8. Output Handler: Delivers the final zip file to the user or a specified destination.

E) SUBAGENT 1: YouTube Downloader
- Final output: An audio file in .wav format extracted from the user-specified YouTube video.
- Tasks:
  1. Receive user input (YouTube video URL and timestamps)
  2. Download the video using youtube-dl library
  3. Extract the audio from the video using FFmpeg
  4. Save the audio as a .wav file
- Sequence: User input > Video download > Audio extraction > .wav file output

F) SUBAGENT 2: Stem Separator
- Final output: Two .wav files containing the instrumental and vocal stems of the input audio.
- Tasks:
  1. Load the .wav file from YouTube Downloader
  2. Use Spleeter library to separate the audio into stems
  3. Save the instrumental and vocal stems as separate .wav files
- Sequence: Input audio > Stem separation > Instrumental and vocal .wav files

G) SUBAGENT 3: Beat Detector
- Final output: A list of beat positions (in seconds) and the estimated tempo (BPM) of the input audio.
- Tasks:
  1. Load the instrumental .wav file from Stem Separator
  2. Use Librosa library to detect beat positions and estimate tempo
  3. Return the list of beat positions and tempo value
- Sequence: Input audio > Beat detection > Tempo estimation > Beat positions and tempo output

H) SUBAGENT 4: Sample Extractor
- Final output: A collection of .wav files containing audio samples of 1, 2, 4, and 8 beats extracted from the input audio at the specified timestamps.
- Tasks:
  1. Load the instrumental and vocal .wav files from Stem Separator
  2. Receive beat positions and tempo from Beat Detector
  3. Use Pydub library to extract samples of varying lengths at the specified timestamps
  4. Save the extracted samples as individual .wav files
- Sequence: Input audio > Beat positions and tempo > Sample extraction > Individual .wav files

I) SUBAGENT 5: Sample Analyzer
- Final output: A list of extracted samples ranked based on audio features (e.g., average amplitude).
- Tasks:
  1. Load the extracted sample .wav files from Sample Extractor
  2. Use Librosa library to analyze each sample and compute audio features
  3. Rank the samples based on the computed features
  4. Return the ranked list of samples
- Sequence: Input samples > Audio analysis > Sample ranking > Ranked samples output

J) SUBAGENT 6: Sample Curator
- Final output: A list of the top 48 samples selected from the ranked samples.
- Tasks:
  1. Receive the ranked list of samples from Sample Analyzer
  2. Filter and select the top 48 samples based on the ranking
  3. Return the list of selected samples
- Sequence: Ranked samples > Sample filtering > Top 48 samples selection > Selected samples output

K) SUBAGENT 7: File Manager
- Final output: A zip file containing the selected audio samples as individual .wav files.
- Tasks:
  1. Receive the list of selected samples from Sample Curator
  2. Save each sample as an individual .wav file
  3. Compress the .wav files into a single zip file using Python's zipfile library
  4. Save the zip file to a specified location
- Sequence: Selected samples > Individual .wav file creation > Zip file compression > Zip file output

L) SUBAGENT 8: Output Handler
- Final output: The zip file containing the curated audio samples, delivered to the user or a specified destination.
- Tasks:
  1. Receive the zip file from File Manager
  2. Deliver the zip file to the user (e.g., via a download link) or send it to a specified destination (e.g., cloud storage, email)
  3. Notify the user of the successful completion of the workflow
- Sequence: Zip file > File delivery > User notification > Task completion

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

Thank you for providing the list of available tasks. Based on the updated information, I have revised the subagents and their tasks to complete the audio sample extraction and curation workflow.

A) SUBAGENT 1: YouTube Audio Extractor
- Final output: An audio file in .mp3 format extracted from the user-specified YouTube video.
- Tasks:
  1. Receive user input (YouTube video URL and timestamps)
  2. Use task 43 (Download youtube video to mp4) to download the video as an MP4 file
  3. Use task 44 (Convert mp4 to mp3) to extract the audio from the MP4 file and save it as an MP3
- Sequence: User input > Video download > Audio extraction > MP3 file output

B) SUBAGENT 2: Stem Separator
- Final output: Two MP3 files containing the instrumental and vocal stems of the input audio.
- Tasks:
  1. Load the MP3 file from YouTube Audio Extractor
  2. Use task 36 (Extract instrumental/vocal stems) to separate the audio into instrumental and vocal stems
  3. Save the instrumental and vocal stems as separate MP3 files
- Sequence: Input audio > Stem separation > Instrumental and vocal MP3 files

C) SUBAGENT 3: Beat Detector
- Final output: A list of beat positions (in seconds) and the estimated tempo (BPM) of the input audio.
- Tasks:
  1. Load the instrumental MP3 file from Stem Separator
  2. Use task 33 (Get tempo (BPM) of mp3) to detect beat positions and estimate tempo
  3. Return the list of beat positions and tempo value as a JSON string
- Sequence: Input audio > Beat detection > Tempo estimation > Beat positions and tempo output

D) SUBAGENT 4: Sample Extractor
- Final output: A collection of MP3 files containing audio samples of 1, 2, 4, and 8 beats extracted from the input audio at the specified timestamps.
- Tasks:
  1. Load the instrumental and vocal MP3 files from Stem Separator
  2. Receive beat positions and tempo from Beat Detector
  3. Use the beat positions and tempo to calculate the start times and durations for samples of 1, 2, 4, and 8 beats at each specified timestamp
  4. Use task 45 (Split/extract video by interval) to extract the samples as MP3 files
  5. Save the extracted samples as individual MP3 files
- Sequence: Input audio > Beat positions and tempo > Sample extraction > Individual MP3 files

E) SUBAGENT 5: Sample Analyzer
- Final output: A list of extracted samples ranked based on average amplitude.
- Tasks:
  1. Load the extracted sample MP3 files from Sample Extractor
  2. Use a Python library like Librosa (REQUIRED) to analyze each sample and compute the average amplitude
  3. Rank the samples based on the average amplitude
  4. Return the ranked list of samples
- Sequence: Input samples > Audio analysis > Sample ranking > Ranked samples output

F) SUBAGENT 6: Sample Curator
- Final output: A list of the top 48 samples selected from the ranked samples.
- Tasks:
  1. Receive the ranked list of samples from Sample Analyzer
  2. Select the top 48 samples based on the ranking
  3. Return the list of selected samples
- Sequence: Ranked samples > Top 48 samples selection > Selected samples output

G) SUBAGENT 7: File Manager
- Final output: A zip file containing the selected audio samples as individual .wav files.
- Tasks:
  1. Receive the list of selected MP3 samples from Sample Curator
  2. Use a Python library like pydub (REQUIRED) to convert each MP3 sample to a .wav file
  3. Use task 14 (Zip and unzip files) to compress the .wav files into a single zip file
  4. Save the zip file to the server
- Sequence: Selected samples > MP3 to .wav conversion > Zip file compression > Zip file output

H) SUBAGENT 8: Output Handler
- Final output: The zip file containing the curated audio samples, delivered to the user or a specified destination.
- Tasks:
  1. Receive the zip file from File Manager
  2. Deliver the zip file to the user (e.g., via a download link) or send it to a specified destination (e.g., cloud storage, email)
  3. Notify the user of the successful completion of the workflow
- Sequence: Zip file > File delivery > User notification > Task completion

Note: Two additional Python libraries (Librosa and pydub) are required for the Sample Analyzer and File Manager subagents, respectively. These libraries need to be added to the available tasks for the agent to complete the workflow successfully.

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

Understood. Based on the available tasks, I have updated the list of subagents to complete the task of creating an automated workflow that extracts and curates audio samples from a YouTube video based on user-specified timestamps and criteria.

A) FULL TASK SUMMARY: Create an automated workflow that extracts and curates audio samples from a YouTube video based on user-specified timestamps and criteria.

E) SUBAGENT 1: YouTube Downloader
- Final output: An audio file in .mp3 format extracted from the user-specified YouTube video.
- Tasks:
  1. Receive user input (YouTube video URL and timestamps)
  2. Download youtube video to mp4 (Task 43)
  3. Convert mp4 to mp3 (Task 44)
  4. Save the audio as an .mp3 file on the server (Task 9)
- Sequence: User input > Video download > Audio extraction > .mp3 file output

F) SUBAGENT 2: Stem Separator
- Final output: Two .mp3 files containing the instrumental and vocal stems of the input audio.
- Tasks:
  1. Load the .mp3 file from YouTube Downloader
  2. Extract instrumental/vocal stems (Task 36)
  3. Save the instrumental and vocal stems as separate .mp3 files on the server (Task 9)
- Sequence: Input audio > Stem separation > Instrumental and vocal .mp3 files

G) SUBAGENT 3: Beat Detector
- Final output: A list of beat positions (in seconds) and the estimated tempo (BPM) of the input audio.
- Tasks:
  1. Load the instrumental .mp3 file from Stem Separator
  2. Get tempo (BPM) of mp3 (Task 33)
  3. Return the list of beat positions and tempo value
- Sequence: Input audio > Beat detection > Tempo estimation > Beat positions and tempo output

H) SUBAGENT 4: Sample Extractor
- Final output: A collection of .wav files containing audio samples of 1, 2, 4, and 8 beats extracted from the input audio at the specified timestamps.
- Tasks:
  1. Load the instrumental and vocal .mp3 files from Stem Separator
  2. Receive beat positions and tempo from Beat Detector
  3. Split/extract audio by interval (Task 45) based on beat positions and lengths
  4. Save the extracted samples as individual .wav files on the server (Task 9)
- Sequence: Input audio > Beat positions and tempo > Sample extraction > Individual .wav files

I) SUBAGENT 5: Sample Analyzer
- Final output: A list of extracted samples ranked based on audio features (e.g., average amplitude).
- Tasks:
  1. Load the extracted sample .wav files from Sample Extractor
  2. Analyze each sample using a Python library like Librosa (REQUIRED)
  3. Rank the samples based on the computed features (REQUIRED)
  4. Return the ranked list of samples
- Sequence: Input samples > Audio analysis > Sample ranking > Ranked samples output

J) SUBAGENT 6: Sample Curator
- Final output: A list of the top 48 samples selected from the ranked samples.
- Tasks:
  1. Receive the ranked list of samples from Sample Analyzer
  2. Filter and select the top 48 samples based on the ranking (REQUIRED)
  3. Return the list of selected samples
- Sequence: Ranked samples > Sample filtering > Top 48 samples selection > Selected samples output

K) SUBAGENT 7: File Manager
- Final output: A zip file containing the selected audio samples as individual .wav files.
- Tasks:
  1. Receive the list of selected samples from Sample Curator
  2. Save each sample as an individual .wav file on the server (Task 9)
  3. Zip and unzip files (Task 14) to compress the .wav files into a single zip file
  4. Save the zip file to a specified location on the server (Task 9)
- Sequence: Selected samples > Individual .wav file creation > Zip file compression > Zip file output

L) SUBAGENT 8: Output Handler
- Final output: The zip file containing the curated audio samples, delivered to the user or a specified destination.
- Tasks:
  1. Receive the zip file from File Manager
  2. Deliver the zip file to the user (e.g., via a download link) or send it to a specified destination (e.g., cloud storage, email) (REQUIRED)
  3. Notify the user of the successful completion of the workflow (REQUIRED)
- Sequence: Zip file > File delivery > User notification > Task completion

Note: Some tasks required for the Sample Analyzer, Sample Curator, and Output Handler subagents are not available in the provided list of tasks. These are marked as "REQUIRED" and would need to be added to the available tasks for the Agent to complete the workflow successfully.

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

Based on the tasks available to the AI agent, I will propose a revised workflow integrating the relevant tasks to achieve the objective of creating a zip file containing 48 high-quality audio samples in .wav format from a YouTube video provided by the user. Here are the updated tasks corresponding to each subagent:

**SUBAGENT 1: YouTube Downloader**
- Task 43: Download YouTube video to MP4
- Input: YouTube URL
- Output: MP4 URL on internal server

**SUBAGENT 2: Audio Extractor**
- Task 44: Convert MP4 to MP3
- Input: MP4 URL from Subagent 1
- Output: MP3 URL

**SUBAGENT 3: Stem Separator**
- Task 36: Extract instrumental/vocal stems
- Input: MP3 URL from Subagent 2
- Output: 2x MP3 URLs (instrumental and vocal)

**SUBAGENT 4: Audio Segment Selector**
- Task 45: Split/extract video by interval (adjusted to audio extraction)
- Input: MP3 URLs (instrumental and vocal), plus user-defined timestamps and duration (e.g., 16 beats worth)
- Output: 6x MP3 URLs (3 timestamps, instrumental and vocal stems)

*REQUIRED* – We would need an additional task for extracting specific audio intervals based on the user's specified timestamps and the duration of the beats, as Task 45 does not specify support for such detailed MP3 splitting.

**SUBAGENT 5: Beat Analyzer**
- Task 33: Get tempo (BPM) of MP3
- Input: MP3 URLs (instrumental from Subagent 4)
- Output: Tempo (BPM) and beatpoint timings in JSON format

**SUBAGENT 6: Sample Generator**
- No existing task for chopping audio into beats/sample lengths is available.
*REQUIRED* – A task is needed that can input an MP3 URL and the beat/tempo information, then generate samples of specified lengths (1/2/4/8 beats), and output an array of MP3 URLs of these samples.

**SUBAGENT 7: Sample Analyzer**
- No existing task for analyzing individual samples by loudness and ranking them.
*REQUIRED* – A task is needed that can analyze the loudness of each sample, rank them, and return a list of samples sorted by the desired criteria (e.g., amplitude).

**SUBAGENT 8: Sample Selector**
- No existing task for selecting the top X samples directly.
*REQUIRED* – A task is needed that can take the sorted list of sample URLs and select the top 48 based on the rankings.

**SUBAGENT 9: File Manager**
- Task 9: Save content to server (save as individual .wav files)
- Input: MP3 URLs from Subagents 6 and 8
- Output: .wav file URLs on the server

**SUBAGENT 10: File Compressor**
- Task 14: Zip and unzip files
- Input: List of .wav file URLs from Subagent 9
- Output: Zip file URL

**SUBAGENT 11: Output Handler**
- No existing Task matches the deliverable final zip file via a link or sending to a specific destination if it requires specific APIs or means of delivery.
*REQUIRED* – A task or method is needed to ensure that the final zip file gets delivered to the user in their preferred manner.

This completes the list of tasks and additional required functionalities to complete the full objective. Please note that I used the file management and output handler tasks where suitable but have also mentioned critical required functionalities that are not currently covered by existing tasks. These requirements may necessitate developing new methods or APIs to be added to the task list available to the AI agent.

subagent-summary-amended-for-actual-tasks

A) FULL TASK SUMMARY: Create an automated workflow that extracts and curates audio samples from a YouTube video based on user-specified timestamps and criteria.

D) OVERVIEW OF SUBAGENTS:
1. YouTube Downloader: Extracts audio from a user-specified YouTube video.
2. Stem Separator: Separates the extracted audio into instrumental and vocal stems.
3. Beat Detector: Detects beat positions and estimates the tempo of the audio.
4. Sample Extractor: Extracts audio samples of varying lengths based on beat positions.
5. Sample Analyzer: Analyzes and ranks the extracted samples based on audio features.
6. Sample Curator: Filters and selects the top 48 samples for the final output.
7. File Converter: Converts the selected MP3 samples to WAV format.
8. File Compressor: Compresses the WAV files into a single zip file.
9. Output Handler: Delivers the final zip file to the user or a specified destination.

G) SUBAGENT 3: Beat Detector
- Final output: A list of beat positions (in seconds) and the estimated tempo (BPM) of the input audio.
- Tasks:
  1. Load the instrumental .mp3 file from Stem Separator
  2. Get tempo (BPM) of mp3 (Task 33)
  3. Return the list of beat positions and tempo value as a JSON string
- Sequence: Input audio > Beat detection > Tempo estimation > Beat positions and tempo output

H) SUBAGENT 4: Sample Extractor
- Final output: A collection of .mp3 files containing audio samples of 1, 2, 4, and 8 beats extracted from the input audio at the specified timestamps.
- Tasks:
  1. Load the instrumental and vocal .mp3 files from Stem Separator
  2. Receive beat positions and tempo from Beat Detector
  3. Split/extract audio by interval based on beat positions and lengths (REQUIRED)
  4. Save the extracted samples as individual .mp3 files on the server (Task 9)
- Sequence: Input audio > Beat positions and tempo > Sample extraction > Individual .mp3 files

I) SUBAGENT 5: Sample Analyzer
- Final output: A list of extracted samples ranked based on average amplitude.
- Tasks:
  1. Load the extracted sample .mp3 files from Sample Extractor
  2. Analyze each sample using a Python library like Librosa to compute the average amplitude (REQUIRED)
  3. Rank the samples based on the average amplitude (REQUIRED)
  4. Return the ranked list of samples
- Sequence: Input samples > Audio analysis > Sample ranking > Ranked samples output

J) SUBAGENT 6: Sample Curator
- Final output: A list of the top 48 samples selected from the ranked samples.
- Tasks:
  1. Receive the ranked list of samples from Sample Analyzer
  2. Select the top 48 samples based on the ranking (REQUIRED)
  3. Return the list of selected samples
- Sequence: Ranked samples > Top 48 samples selection > Selected samples output

K) SUBAGENT 7: File Converter
- Final output: The selected audio samples converted from .mp3 to .wav format.
- Tasks:
  1. Receive the list of selected .mp3 samples from Sample Curator
  2. Convert each .mp3 sample to .wav format using a Python library like pydub (REQUIRED)
  3. Save the converted .wav files on the server (Task 9)
- Sequence: Selected .mp3 samples > MP3 to WAV conversion > Individual .wav files

L) SUBAGENT 8: File Compressor
- Final output: A zip file containing the selected audio samples as individual .wav files.
- Tasks:
  1. Receive the list of .wav files from File Converter
  2. Zip and unzip files (Task 14) to compress the .wav files into a single zip file
  3. Save the zip file to a specified location on the server (Task 9)
- Sequence: Individual .wav files > Zip file compression > Zip file output

M) SUBAGENT 9: Output Handler
- Final output: The zip file containing the curated audio samples, delivered to the user or a specified destination.
- Tasks:
  1. Receive the zip file from File Compressor
  2. Deliver the zip file to the user (e.g., via a download link) or send it to a specified destination (e.g., cloud storage, email) (REQUIRED)
  3. Notify the user of the successful completion of the workflow (REQUIRED)
- Sequence: Zip file > File delivery > User notification > Task completion

Note: Some tasks required for the Sample Extractor, Sample Analyzer, Sample Curator, File Converter, and Output Handler subagents are not available in the provided list of tasks. These are marked as "REQUIRED" and would need to be added to the available tasks for the Agent to complete the workflow successfully. The specific implementations of these required tasks may involve developing custom Python scripts or integrating additional libraries and APIs.

random-autocomplete

To achieve the end goal of creating a zip file containing 48 curated audio samples extracted from a YouTube video, the most relevant tasks might be:

1. Downloading the audio from the specified YouTube video
2. Extracting instrumental and vocal stems from the audio
3. Detecting the tempo and beat positions of the audio
4. Dividing the audio into samples based on the user-defined timestamps and beat positions
5. Analyzing and ranking the samples based on criteria like average amplitude
6. Selecting the top 48 samples and saving them as .wav files
7. Compressing the selected samples into a zip file

The overall sequence of actions would be:

1. Use a YouTube downloading library (e.g., youtube-dl) to download the audio from the specified YouTube video.
2. Employ a stem separation library (e.g., Spleeter) to extract the instrumental and vocal stems from the downloaded audio.
3. Analyze the audio using a music information retrieval library (e.g., Librosa) to detect the tempo and beat positions.
4. Based on the user-defined timestamps and the detected beat positions, divide the audio into samples of varying lengths (1, 2, 4, or 8 beats) using an audio processing library (e.g., pydub or SoX).
5. Analyze the resulting samples using Librosa to calculate relevant metrics such as average amplitude.
6. Rank the samples based on the calculated metrics and select the top 48 samples.
7. Save the selected samples as individual .wav files using an audio file writing library (e.g., soundfile).
8. Use a file compression library (e.g., zipfile) to compress the selected .wav files into a single zip file.
9. Save the zip file to the designated server or directory.

By following this sequence of actions and leveraging the appropriate libraries and APIs, the AI Agent should be able to automate the process of extracting, analyzing, and curating audio samples from a YouTube video based on user-defined parameters, ultimately delivering a zip file containing the top 48 samples in .wav format.

claude 3 - mermaid-for-all

SubAgent #1

claude 3 - subagent1

subagent1-refined

A) SUBAGENT SUMMARY: The YouTube Downloader subagent extracts audio from a user-specified YouTube video and saves it as an MP3 file on the server.

B) FINAL TASK OUTPUT: An audio file in .mp3 format, extracted from the user-specified YouTube video, saved on the server with a maximum duration of 60 minutes.

C) SUBAGENT INPUT:
- YouTube video URL (as a text string)
- Three timestamps (in seconds) for the desired audio sections (as integers or floats)

E) SUBAGENT TASK SUMMARY:
1. Receive user input (YouTube video URL and timestamps)
2. Download YouTube video to MP4 using the provided URL (Task 43)
3. Save the downloaded MP4 file to the server (Task 9)
4. Convert the MP4 file to MP3 format (Task 44)
5. Save the converted MP3 file to the server (Task 9)
6. Extract audio sections from the MP3 file based on the provided timestamps (REQUIRED TASK - not in the list)
   - Use a Python library like Pydub to extract the desired audio sections
   - Save each extracted audio section as a separate MP3 file on the server (Task 9)
7. Return the URLs of the extracted audio section MP3 files

Subagent final input(s) > Download video > Save MP4 > Convert to MP3 > Save MP3 > Extract audio sections > Save section MP3s > Subagent final output(s)

F) SILOS:
Silo 1: Video download and conversion
- Download YouTube video to MP4 (Task 43)
- Save the downloaded MP4 file to the server (Task 9)
- Convert the MP4 file to MP3 format (Task 44)
- Save the converted MP3 file to the server (Task 9)

Silo 2: Audio section extraction (REQUIRED TASK)
- Extract audio sections from the MP3 file based on the provided timestamps
- Save each extracted audio section as a separate MP3 file on the server (Task 9)

Note: The subagent requires an additional task for extracting audio sections based on user-provided timestamps. This task is not available in the provided list and would need to be implemented using a Python library like Pydub. The extracted audio sections should be saved as separate MP3 files on the server using Task 9.

subagent1refined-repeating

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Receive user input: YouTube video URL (as a text string)
2. Receive user input: Timestamp 1 (in seconds) for the desired audio section (as an integer or float)
3. Receive user input: Timestamp 2 (in seconds) for the desired audio section (as an integer or float)
4. Receive user input: Timestamp 3 (in seconds) for the desired audio section (as an integer or float)
5. Download YouTube video to MP4 using the provided URL (Task 43)
6. Save the downloaded MP4 file to the server (Task 9)
7. Convert the MP4 file to MP3 format (Task 44)
8. Save the converted MP3 file to the server (Task 9)
9. Extract audio section 1 from the MP3 file based on Timestamp 1 (REQUIRED TASK - not in the list)
10. Save the extracted audio section 1 as a separate MP3 file on the server (Task 9)
11. Extract audio section 2 from the MP3 file based on Timestamp 2 (REQUIRED TASK - not in the list)
12. Save the extracted audio section 2 as a separate MP3 file on the server (Task 9)
13. Extract audio section 3 from the MP3 file based on Timestamp 3 (REQUIRED TASK - not in the list)
14. Save the extracted audio section 3 as a separate MP3 file on the server (Task 9)
15. Return the URL of the extracted audio section 1 MP3 file
16. Return the URL of the extracted audio section 2 MP3 file
17. Return the URL of the extracted audio section 3 MP3 file

Subagent1 task-ID-numbers

subagent1-output-variables

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Receive user input: YouTube video URL (as a text string) [youtube-url]
2. Receive user input: Timestamp 1 (in seconds) for the desired audio section (as an integer or float) [timestamp1]
3. Receive user input: Timestamp 2 (in seconds) for the desired audio section (as an integer or float) [timestamp2]
4. Receive user input: Timestamp 3 (in seconds) for the desired audio section (as an integer or float) [timestamp3]
5. Download YouTube video to MP4 using [youtube-url] (Task 43) [youtube-mp4]
6. Save [youtube-mp4] to the server (Task 9) [saved-youtube-mp4-path]
7. Convert [youtube-mp4] to MP3 format (Task 44) [youtube-mp3]
8. Save [youtube-mp3] to the server (Task 9) [saved-youtube-mp3-path]
9. Extract audio section 1 from [youtube-mp3] based on [timestamp1] (REQUIRED TASK - not in the list) [audio-section1]
10. Save [audio-section1] as a separate MP3 file on the server (Task 9) [saved-audio-section1-path]
11. Extract audio section 2 from [youtube-mp3] based on [timestamp2] (REQUIRED TASK - not in the list) [audio-section2]
12. Save [audio-section2] as a separate MP3 file on the server (Task 9) [saved-audio-section2-path]
13. Extract audio section 3 from [youtube-mp3] based on [timestamp3] (REQUIRED TASK - not in the list) [audio-section3]
14. Save [audio-section3] as a separate MP3 file on the server (Task 9) [saved-audio-section3-path]
15. Return the URL of [saved-audio-section1-path] [audio-section1-url]
16. Return the URL of [saved-audio-section2-path] [audio-section2-url]
17. Return the URL of [saved-audio-section3-path] [audio-section3-url]

H) OUTPUT TOKENS
[youtube-url]
[timestamp1]
[timestamp2]
[timestamp3]
[youtube-mp4]
[saved-youtube-mp4-path]
[youtube-mp3]
[saved-youtube-mp3-path]
[audio-section1]
[saved-audio-section1-path]
[audio-section2]
[saved-audio-section2-path]
[audio-section3]
[saved-audio-section3-path]
[audio-section1-url]
[audio-section2-url]
[audio-section3-url]

subagent1refined-repeating-json-core

A) SUBAGENT SUMMARY: Extract audio sections from a YouTube video based on user-provided timestamps and save them as separate MP3 files.

B) FINAL TASK OUTPUT: Three separate MP3 files containing the extracted audio sections from the YouTube video, each with a duration of approximately 16 beats, saved on the server with their respective URLs returned.

C) SUBAGENT INPUT:
- YouTube video URL (as a text string)
- Timestamp 1 (in seconds) for the desired audio section (as an integer or float)
- Timestamp 2 (in seconds) for the desired audio section (as an integer or float)
- Timestamp 3 (in seconds) for the desired audio section (as an integer or float)

E) SUBAGENT TASK SUMMARY:
1. Receive user inputs (YouTube video URL and timestamps)
2. Download YouTube video to MP4 using the provided URL
3. Save the downloaded MP4 file to the server
4. Convert the MP4 file to MP3 format
5. Save the converted MP3 file to the server
6. Extract audio section 1 from the MP3 file based on Timestamp 1
7. Save the extracted audio section 1 as a separate MP3 file on the server
8. Extract audio section 2 from the MP3 file based on Timestamp 2
9. Save the extracted audio section 2 as a separate MP3 file on the server
10. Extract audio section 3 from the MP3 file based on Timestamp 3
11. Save the extracted audio section 3 as a separate MP3 file on the server
12. Return the URLs of the extracted audio section MP3 files

F) SILOS: None

G) TASKFLOW SUMMARY:

Task 1:
Title task: Receive YouTube video URL input
Description of the task: Receive user input for the YouTube video URL as a text string
Input(s) involved: None
Output token name: youtube_url

Task 2:
Title task: Receive Timestamp 1 input
Description of the task: Receive user input for Timestamp 1 (in seconds) for the desired audio section as an integer or float
Input(s) involved: None
Output token name: timestamp_1

Task 3:
Title task: Receive Timestamp 2 input
Description of the task: Receive user input for Timestamp 2 (in seconds) for the desired audio section as an integer or float
Input(s) involved: None
Output token name: timestamp_2

Task 4:
Title task: Receive Timestamp 3 input
Description of the task: Receive user input for Timestamp 3 (in seconds) for the desired audio section as an integer or float
Input(s) involved: None
Output token name: timestamp_3

Task 5:
Title task: Download YouTube video to MP4
Description of the task: Download the YouTube video using the provided URL and save it as an MP4 file
Input(s) involved: youtube_url
Output token name: mp4_file

Task 6:
Title task: Save MP4 file to server
Description of the task: Save the downloaded MP4 file to the server
Input(s) involved: mp4_file
Output token name: mp4_file_url

Task 7:
Title task: Convert MP4 to MP3
Description of the task: Convert the MP4 file to MP3 format
Input(s) involved: mp4_file
Output token name: mp3_file

Task 8:
Title task: Save MP3 file to server
Description of the task: Save the converted MP3 file to the server
Input(s) involved: mp3_file
Output token name: mp3_file_url

Task 9:
Title task: Extract audio section 1
Description of the task: Extract audio section 1 from the MP3 file based on Timestamp 1
Input(s) involved: mp3_file, timestamp_1
Output token name: audio_section_1

Task 10:
Title task: Save audio section 1 to server
Description of the task: Save the extracted audio section 1 as a separate MP3 file on the server
Input(s) involved: audio_section_1
Output token name: audio_section_1_url

Task 11:
Title task: Extract audio section 2
Description of the task: Extract audio section 2 from the MP3 file based on Timestamp 2
Input(s) involved: mp3_file, timestamp_2
Output token name: audio_section_2

Task 12:
Title task: Save audio section 2 to server
Description of the task: Save the extracted audio section 2 as a separate MP3 file on the server
Input(s) involved: audio_section_2
Output token name: audio_section_2_url

Task 13:
Title task: Extract audio section 3
Description of the task: Extract audio section 3 from the MP3 file based on Timestamp 3
Input(s) involved: mp3_file, timestamp_3
Output token name: audio_section_3

Task 14:
Title task: Save audio section 3 to server
Description of the task: Save the extracted audio section 3 as a separate MP3 file on the server
Input(s) involved: audio_section_3
Output token name: audio_section_3_url

Task 15:
Title task: Return audio section 1 URL
Description of the task: Return the URL of the extracted audio section 1 MP3 file
Input(s) involved: audio_section_1_url
Output token name: None

Task 16:
Title task: Return audio section 2 URL
Description of the task: Return the URL of the extracted audio section 2 MP3 file
Input(s) involved: audio_section_2_url
Output token name: None

Task 17:
Title task: Return audio section 3 URL
Description of the task: Return the URL of the extracted audio section 3 MP3 file
Input(s) involved: audio_section_3_url
Output token name: None

subagent1mermaid

SubAgent #2

claude 3 - subagent2

subagent2-refined

A) SUBAGENT SUMMARY: The Stem Separator subagent extracts instrumental and vocal stems from an input audio file.

B) FINAL TASK OUTPUT: Two .mp3 files containing the instrumental and vocal stems of the input audio, each with a maximum duration of 10 minutes and a maximum file size of 20 MB.

C) SUBAGENT INPUT:
- An .mp3 audio file URL from the YouTube Downloader subagent
- The file should have a maximum duration of 10 minutes and a maximum file size of 20 MB

E) SUBAGENT TASK SUMMARY:
1. Receive the .mp3 file URL from the YouTube Downloader subagent
2. Extract instrumental and vocal stems using Task 36 (Extract instrumental/vocal stems)
   - Input: MP3 URL
   - Output: 2x MP3 URLs (instrumental and vocal stems)
3. Save the instrumental stem as an .mp3 file on the server using Task 9 (Save content to server)
   - Input: Instrumental stem MP3 content, file extension (.mp3)
   - Output: Internal server URL for the instrumental stem (e.g., server/_public/timestamp/instrumental.mp3)
4. Save the vocal stem as an .mp3 file on the server using Task 9 (Save content to server)
   - Input: Vocal stem MP3 content, file extension (.mp3)
   - Output: Internal server URL for the vocal stem (e.g., server/_public/timestamp/vocal.mp3)
5. Output the internal server URLs for both the instrumental and vocal stem .mp3 files

F) SILOS:
This subagent does not require silos as the tasks are sequential and depend on the output of the previous task.

subagent2refined-repeating

Here's the updated subagent structure with the addition of the "G) FULL TASKS (INCLUDING REPEATING TASKS)" section:

A) SUBAGENT SUMMARY: The Stem Separator subagent extracts instrumental and vocal stems from an input audio file.

B) FINAL TASK OUTPUT: Two .mp3 files containing the instrumental and vocal stems of the input audio, each with a maximum duration of 10 minutes and a maximum file size of 20 MB.

C) SUBAGENT INPUT:
- An .mp3 audio file URL from the YouTube Downloader subagent
- The file should have a maximum duration of 10 minutes and a maximum file size of 20 MB

F) SILOS:
This subagent does not require silos as the tasks are sequential and depend on the output of the previous task.

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Receive the .mp3 file URL from the YouTube Downloader subagent
2. Extract instrumental and vocal stems using Task 36 (Extract instrumental/vocal stems)
   - Input: MP3 URL
   - Output: 2x MP3 URLs (instrumental and vocal stems)
3. Save the instrumental stem as an .mp3 file on the server using Task 9 (Save content to server)
   - Input: Instrumental stem MP3 content, file extension (.mp3)
   - Output: Internal server URL for the instrumental stem (e.g., server/_public/timestamp/instrumental.mp3)
4. Save the vocal stem as an .mp3 file on the server using Task 9 (Save content to server)
   - Input: Vocal stem MP3 content, file extension (.mp3)
   - Output: Internal server URL for the vocal stem (e.g., server/_public/timestamp/vocal.mp3)
5. Output the internal server URLs for both the instrumental and vocal stem .mp3 files

In this case, there are no repeating tasks within the subagent, as the task summary explicitly states the extraction and saving of a single instrumental and vocal stem from the input audio file. Therefore, the "G) FULL TASKS (INCLUDING REPEATING TASKS)" section is the same as the "E) SUBAGENT TASK SUMMARY" section.

Subagent2 task-ID-numbers

subagent2-output-variables

Here's the updated subagent structure with the addition of the "H) OUTPUT TOKENS" section:

A) SUBAGENT SUMMARY: The Stem Separator subagent extracts instrumental and vocal stems from an input audio file.

B) FINAL TASK OUTPUT: Two .mp3 files containing the instrumental and vocal stems of the input audio, each with a maximum duration of 10 minutes and a maximum file size of 20 MB.

C) SUBAGENT INPUT:
- An .mp3 audio file URL from the YouTube Downloader subagent [youtube-mp3-url]
- The file should have a maximum duration of 10 minutes and a maximum file size of 20 MB

E) SUBAGENT TASK SUMMARY:
1. Receive the .mp3 file URL from the YouTube Downloader subagent [youtube-mp3-url]
2. Extract instrumental and vocal stems using Task 36 (Extract instrumental/vocal stems)
   - Input: [youtube-mp3-url]
   - Output: [instrumental-stem-url], [vocal-stem-url]
3. Save the instrumental stem as an .mp3 file on the server using Task 9 (Save content to server)
   - Input: [instrumental-stem-url], file extension (.mp3)
   - Output: [instrumental-stem-server-url]
4. Save the vocal stem as an .mp3 file on the server using Task 9 (Save content to server)
   - Input: [vocal-stem-url], file extension (.mp3)
   - Output: [vocal-stem-server-url]
5. Output the internal server URLs for both the instrumental and vocal stem .mp3 files [instrumental-stem-server-url], [vocal-stem-server-url]

F) SILOS:
This subagent does not require silos as the tasks are sequential and depend on the output of the previous task.

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Receive the .mp3 file URL from the YouTube Downloader subagent [youtube-mp3-url]
2. Extract instrumental and vocal stems using Task 36 (Extract instrumental/vocal stems)
   - Input: [youtube-mp3-url]
   - Output: [instrumental-stem-url], [vocal-stem-url]
3. Save the instrumental stem as an .mp3 file on the server using Task 9 (Save content to server)
   - Input: [instrumental-stem-url], file extension (.mp3)
   - Output: [instrumental-stem-server-url]
4. Save the vocal stem as an .mp3 file on the server using Task 9 (Save content to server)
   - Input: [vocal-stem-url], file extension (.mp3)
   - Output: [vocal-stem-server-url]
5. Output the internal server URLs for both the instrumental and vocal stem .mp3 files [instrumental-stem-server-url], [vocal-stem-server-url]

H) OUTPUT TOKENS:
- [youtube-mp3-url]: The URL of the .mp3 file downloaded from YouTube
- [instrumental-stem-url]: The URL of the extracted instrumental stem
- [vocal-stem-url]: The URL of the extracted vocal stem
- [instrumental-stem-server-url]: The internal server URL for the saved instrumental stem .mp3 file
- [vocal-stem-server-url]: The internal server URL for the saved vocal stem .mp3 file

subagent2refined-repeating-json-core

G) TASKFLOW SUMMARY

Title task: Extract instrumental and vocal stems from audio file
Description of the task: Use a stem separation tool or algorithm to extract the instrumental and vocal components from the input audio file, saving them as separate MP3 files.
Input(s) involved: [MP3_URL]
Output token name: [INSTRUMENTAL_STEM_MP3_URL], [VOCAL_STEM_MP3_URL]

Title task: Save instrumental stem to server
Description of the task: Upload the extracted instrumental stem MP3 file to the server and generate a unique URL for accessing the file.
Input(s) involved: [INSTRUMENTAL_STEM_MP3_CONTENT], [.MP3_FILE_EXTENSION]
Output token name: [INSTRUMENTAL_STEM_SERVER_URL]

Title task: Save vocal stem to server
Description of the task: Upload the extracted vocal stem MP3 file to the server and generate a unique URL for accessing the file.
Input(s) involved: [VOCAL_STEM_MP3_CONTENT], [.MP3_FILE_EXTENSION]
Output token name: [VOCAL_STEM_SERVER_URL]

Title task: Output server URLs for instrumental and vocal stems
Description of the task: Compile the server URLs for both the instrumental and vocal stem MP3 files and output them as the final result of the subagent.
Input(s) involved: [INSTRUMENTAL_STEM_SERVER_URL], [VOCAL_STEM_SERVER_URL]
Output token name: [INSTRUMENTAL_VOCAL_STEM_SERVER_URLS]

subagent2mermaid

SubAgent #3

claude 3 - subagent3

subagent1-refined

A) SUBAGENT SUMMARY: Beat Detector subagent analyzes an input audio file to detect beat positions and estimate the tempo.

B) FINAL TASK OUTPUT: A JSON string containing a list of beat positions (in seconds) and the estimated tempo (BPM) of the input audio, along with confidence scores for both values.

C) SUBAGENT INPUT:
- An MP3 URL of the instrumental audio file from the Stem Separator subagent.

E) SUBAGENT TASK SUMMARY:
1. Receive the MP3 URL of the instrumental audio file from the Stem Separator subagent.
2. Call the "Get tempo (BPM) of mp3" task (Task 33) with the MP3 URL as input.
   - This task will analyze the audio file and return the tempo, beatpoint timings, and confidence scores as a JSON string.
3. Extract the relevant information (beat positions, tempo, and confidence scores) from the JSON string using a Python script or an LLM.
4. Format the extracted information into a new JSON string with the following structure:
   {
     "beat_positions": [0.5, 1.2, 1.9, ...],
     "tempo": 120.5,
     "beat_confidence": 0.95,
     "tempo_confidence": 0.90
   }
5. Output the formatted JSON string containing the beat positions, tempo, and confidence scores.

F) SILOS:
This subagent does not have any silos, as it consists of a single, linear sequence of tasks.

subagent1refined-repeating

A) SUBAGENT SUMMARY: Beat Detector subagent analyzes an input audio file to detect beat positions and estimate the tempo.

B) FINAL TASK OUTPUT: A JSON string containing a list of beat positions (in seconds) and the estimated tempo (BPM) of the input audio, along with confidence scores for both values.

C) SUBAGENT INPUT:
- An MP3 URL of the instrumental audio file from the Stem Separator subagent.

F) SILOS:
This subagent does not have any silos, as it consists of a single, linear sequence of tasks.

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Receive the MP3 URL of the instrumental audio file from the Stem Separator subagent.
2. Call the "Get tempo (BPM) of mp3" task (Task 33) with the MP3 URL as input.
3. Receive the JSON string containing the tempo, beatpoint timings, and confidence scores from Task 33.
4. Extract the beat positions from the JSON string.
5. Extract the tempo from the JSON string.
6. Extract the beat confidence score from the JSON string.
7. Extract the tempo confidence score from the JSON string.
8. Format the extracted information into a new JSON string with the following structure:
   {
     "beat_positions": [0.5, 1.2, 1.9, ...],
     "tempo": 120.5,
     "beat_confidence": 0.95,
     "tempo_confidence": 0.90
   }
9. Output the formatted JSON string containing the beat positions, tempo, and confidence scores.

Subagent1 task-ID-numbers3

subagent3-output-variables

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

--------------
A) SUBAGENT SUMMARY: Beat Detector subagent analyzes an input audio file to detect beat positions and estimate the tempo.

B) FINAL TASK OUTPUT: A JSON string containing a list of beat positions (in seconds) and the estimated tempo (BPM) of the input audio, along with confidence scores for both values.

C) SUBAGENT INPUT:
- An MP3 URL of the instrumental audio file from the Stem Separator subagent [instrumental-mp3-url].

E) SUBAGENT TASK SUMMARY:
1. Receive the MP3 URL of the instrumental audio file from the Stem Separator subagent [instrumental-mp3-url].
2. Call the "Get tempo (BPM) of mp3" task (Task 33) with the MP3 URL [instrumental-mp3-url] as input.
   - This task will analyze the audio file and return the tempo, beatpoint timings, and confidence scores as a JSON string [audio-analysis-json].
3. Extract the relevant information (beat positions, tempo, and confidence scores) from the JSON string [audio-analysis-json] using a Python script or an LLM.
4. Format the extracted information into a new JSON string [beat-detector-output] with the following structure:
   {
     "beat_positions": [0.5, 1.2, 1.9, ...],
     "tempo": 120.5,
     "beat_confidence": 0.95,
     "tempo_confidence": 0.90
   }
5. Output the formatted JSON string [beat-detector-output] containing the beat positions, tempo, and confidence scores.

F) SILOS:
This subagent does not have any silos, as it consists of a single, linear sequence of tasks.

G) FULL TASKS (INCLUDING REPEATING TASKS):
1. Receive the MP3 URL of the instrumental audio file from the Stem Separator subagent [instrumental-mp3-url].
2. Call the "Get tempo (BPM) of mp3" task (Task 33) with the MP3 URL [instrumental-mp3-url] as input.
3. Receive the JSON string [audio-analysis-json] containing the tempo, beatpoint timings, and confidence scores from Task 33.
4. Extract the beat positions [beat-positions] from the JSON string [audio-analysis-json].
5. Extract the tempo [tempo] from the JSON string [audio-analysis-json].
6. Extract the beat confidence score [beat-confidence] from the JSON string [audio-analysis-json].
7. Extract the tempo confidence score [tempo-confidence] from the JSON string [audio-analysis-json].
8. Format the extracted information into a new JSON string [beat-detector-output] with the following structure:
   {
     "beat_positions": [beat-positions],
     "tempo": [tempo],
     "beat_confidence": [beat-confidence],
     "tempo_confidence": [tempo-confidence]
   }
9. Output the formatted JSON string [beat-detector-output] containing the beat positions, tempo, and confidence scores.

H) OUTPUT TOKENS:
- [instrumental-mp3-url]: The MP3 URL of the instrumental audio file received from the Stem Separator subagent.
- [audio-analysis-json]: The JSON string containing the tempo, beatpoint timings, and confidence scores received from Task 33.
- [beat-positions]: The list of beat positions extracted from the JSON string [audio-analysis-json].
- [tempo]: The tempo value extracted from the JSON string [audio-analysis-json].
- [beat-confidence]: The beat confidence score extracted from the JSON string [audio-analysis-json].
- [tempo-confidence]: The tempo confidence score extracted from the JSON string [audio-analysis-json].
- [beat-detector-output]: The final formatted JSON string containing the beat positions, tempo, and confidence scores.
--------------

subagent3refined-repeating-json-core

G) TASKFLOW SUMMARY

1. Title task: Receive instrumental audio file MP3 URL
   Description of the task: Receive the MP3 URL of the instrumental audio file from the Stem Separator subagent.
   Input(s) involved: [instrumental_audio_mp3_url]
   Output token name: instrumental_audio_mp3_url

2. Title task: Call "Get tempo (BPM) of mp3" task
   Description of the task: Call the "Get tempo (BPM) of mp3" task (Task 33) with the MP3 URL as input to analyze the audio file and return the tempo, beatpoint timings, and confidence scores as a JSON string.
   Input(s) involved: [instrumental_audio_mp3_url]
   Output token name: audio_analysis_json

3. Title task: Extract beat positions from JSON
   Description of the task: Extract the beat positions from the JSON string received from the "Get tempo (BPM) of mp3" task.
   Input(s) involved: [audio_analysis_json]
   Output token name: beat_positions

4. Title task: Extract tempo from JSON
   Description of the task: Extract the tempo from the JSON string received from the "Get tempo (BPM) of mp3" task.
   Input(s) involved: [audio_analysis_json]
   Output token name: tempo

5. Title task: Extract beat confidence score from JSON
   Description of the task: Extract the beat confidence score from the JSON string received from the "Get tempo (BPM) of mp3" task.
   Input(s) involved: [audio_analysis_json]
   Output token name: beat_confidence

6. Title task: Extract tempo confidence score from JSON
   Description of the task: Extract the tempo confidence score from the JSON string received from the "Get tempo (BPM) of mp3" task.
   Input(s) involved: [audio_analysis_json]
   Output token name: tempo_confidence

7. Title task: Format extracted information into JSON string
   Description of the task: Format the extracted beat positions, tempo, and confidence scores into a new JSON string with the specified structure.
   Input(s) involved: [beat_positions, tempo, beat_confidence, tempo_confidence]
   Output token name: formatted_json_output

8. Title task: Output formatted JSON string
   Description of the task: Output the formatted JSON string containing the beat positions, tempo, and confidence scores.
   Input(s) involved: [formatted_json_output]
   Output token name: beat_detector_output

subagent3mermaid

prompts-required

Based on the components listed above, the following unique prompts would be required for the agent workflow:

Task: YouTube Video Input Validation
Output of the task: Validation of the user-provided YouTube video URL and timestamps
Description: The prompt should ask the user to provide a valid YouTube video URL and three timestamps in a specified format (e.g., MM:SS). The LLM should validate the input, checking if the URL is a valid YouTube link and if the timestamps are in the correct format and within the video's duration. If the input is invalid, the LLM should provide appropriate error messages and ask the user to resubmit the input.

Task: Stem Separation Configuration
Output of the task: Configuration options for the stem separation process
Description: The prompt should ask the user to specify the desired configuration options for the stem separation process, such as the target stems (e.g., instrumental, vocals), the stem separation library to use (e.g., Spleeter, Open-Unmix), and any additional parameters required by the chosen library. The LLM should provide explanations for each configuration option and guide the user in selecting appropriate values.

Task: Sample Extraction Configuration
Output of the task: Configuration options for the sample extraction process
Description: The prompt should ask the user to specify the desired configuration options for the sample extraction process, such as the sample lengths (e.g., 1, 2, 4, or 8 beats), the audio library to use (e.g., Pydub, SOX), and any additional parameters required by the chosen library. The LLM should provide explanations for each configuration option and guide the user in selecting appropriate values.

Task: Sample Analysis and Ranking Configuration
Output of the task: Configuration options for the sample analysis and ranking process
Description: The prompt should ask the user to specify the desired configuration options for the sample analysis and ranking process, such as the audio features to analyze (e.g., average amplitude, spectral centroid), the ranking algorithm to use (e.g., weighted sum, machine learning model), and any additional parameters required by the chosen algorithm. The LLM should provide explanations for each configuration option and guide the user in selecting appropriate values.

Task: Output Destination Configuration
Output of the task: Configuration options for the output destination
Description: The prompt should ask the user to specify the desired output destination for the final zip file, such as a local file path, a cloud storage bucket, or an email address. The LLM should provide explanations for each option and guide the user in selecting and configuring the appropriate destination.

Task: Error Handling and Logging Configuration
Output of the task: Configuration options for error handling and logging
Description: The prompt should ask the user to specify the desired configuration options for error handling and logging, such as the log level (e.g., debug, info, warning, error), log format, and log destinations (e.g., console, file, remote server). The LLM should provide explanations for each configuration option and guide the user in selecting appropriate values based on their requirements.

These prompts would be used to gather the necessary information and preferences from the user to configure the various components of the agent workflow. The LLM's responses would be used to dynamically generate the configuration for each component, ensuring that the workflow is tailored to the user's specific needs.

subagent1-json-array

[
  {
    "task ID": 1,
    "title of the task": "Receive YouTube video URL input",
    "description of the task": "Receive user input for the YouTube video URL as a text string",
    "input description": "No input required",
    "input required": [],
    "output description": "The YouTube video URL as a text string",
    "output name": "youtube_url"
  },
  {
    "task ID": 2,
    "title of the task": "Receive Timestamp 1 input",
    "description of the task": "Receive user input for Timestamp 1 (in seconds) for the desired audio section as an integer or float",
    "input description": "No input required",
    "input required": [],
    "output description": "The Timestamp 1 value (in seconds) as an integer or float",
    "output name": "timestamp_1"
  },
  {
    "task ID": 3,
    "title of the task": "Receive Timestamp 2 input",
    "description of the task": "Receive user input for Timestamp 2 (in seconds) for the desired audio section as an integer or float",
    "input description": "No input required",
    "input required": [],
    "output description": "The Timestamp 2 value (in seconds) as an integer or float",
    "output name": "timestamp_2"
  },
  {
    "task ID": 4,
    "title of the task": "Receive Timestamp 3 input",
    "description of the task": "Receive user input for Timestamp 3 (in seconds) for the desired audio section as an integer or float",
    "input description": "No input required",
    "input required": [],
    "output description": "The Timestamp 3 value (in seconds) as an integer or float",
    "output name": "timestamp_3"
  },
  {
    "task ID": 5,
    "title of the task": "Download YouTube video to MP4",
    "description of the task": "Download the YouTube video using the provided URL and save it as an MP4 file",
    "input description": "The YouTube video URL as a text string",
    "input required": ["youtube_url"],
    "output description": "The downloaded YouTube video as an MP4 file",
    "output name": "mp4_file"
  },
  {
    "task ID": 6,
    "title of the task": "Save MP4 file to server",
    "description of the task": "Save the downloaded MP4 file to the server",
    "input description": "The downloaded YouTube video as an MP4 file",
    "input required": ["mp4_file"],
    "output description": "The URL of the saved MP4 file on the server",
    "output name": "mp4_file_url"
  },
  {
    "task ID": 7,
    "title of the task": "Convert MP4 to MP3",
    "description of the task": "Convert the MP4 file to MP3 format",
    "input description": "The downloaded YouTube video as an MP4 file",
    "input required": ["mp4_file"],
    "output description": "The converted audio as an MP3 file",
    "output name": "mp3_file"
  },
  {
    "task ID": 8,
    "title of the task": "Save MP3 file to server",
    "description of the task": "Save the converted MP3 file to the server",
    "input description": "The converted audio as an MP3 file",
    "input required": ["mp3_file"],
    "output description": "The URL of the saved MP3 file on the server",
    "output name": "mp3_file_url"
  },
  {
    "task ID": 9,
    "title of the task": "Extract audio section 1",
    "description of the task": "Extract audio section 1 from the MP3 file based on Timestamp 1",
    "input description": "The converted audio as an MP3 file and the Timestamp 1 value (in seconds) as an integer or float",
    "input required": ["mp3_file", "timestamp_1"],
    "output description": "The extracted audio section 1 as an MP3 file",
    "output name": "audio_section_1"
  },
  {
    "task ID": 10,
    "title of the task": "Save audio section 1 to server",
    "description of the task": "Save the extracted audio section 1 as a separate MP3 file on the server",
    "input description": "The extracted audio section 1 as an MP3 file",
    "input required": ["audio_section_1"],
    "output description": "The URL of the saved audio section 1 MP3 file on the server",
    "output name": "audio_section_1_url"
  },
  {
    "task ID": 11,
    "title of the task": "Extract audio section 2",
    "description of the task": "Extract audio section 2 from the MP3 file based on Timestamp 2",
    "input description": "The converted audio as an MP3 file and the Timestamp 2 value (in seconds) as an integer or float",
    "input required": ["mp3_file", "timestamp_2"],
    "output description": "The extracted audio section 2 as an MP3 file",
    "output name": "audio_section_2"
  },
  {
    "task ID": 12,
    "title of the task": "Save audio section 2 to server",
    "description of the task": "Save the extracted audio section 2 as a separate MP3 file on the server",
    "input description": "The extracted audio section 2 as an MP3 file",
    "input required": ["audio_section_2"],
    "output description": "The URL of the saved audio section 2 MP3 file on the server",
    "output name": "audio_section_2_url"
  },
  {
    "task ID": 13,
    "title of the task": "Extract audio section 3",
    "description of the task": "Extract audio section 3 from the MP3 file based on Timestamp 3",
    "input description": "The converted audio as an MP3 file and the Timestamp 3 value (in seconds) as an integer or float",
    "input required": ["mp3_file", "timestamp_3"],
    "output description": "The extracted audio section 3 as an MP3 file",
    "output name": "audio_section_3"
  },
  {
    "task ID": 14,
    "title of the task": "Save audio section 3 to server",
    "description of the task": "Save the extracted audio section 3 as a separate MP3 file on the server",
    "input description": "The extracted audio section 3 as an MP3 file",
    "input required": ["audio_section_3"],
    "output description": "The URL of the saved audio section 3 MP3 file on the server",
    "output name": "audio_section_3_url"
  },
  {
    "task ID": 15,
    "title of the task": "Return audio section 1 URL",
    "description of the task": "Return the URL of the extracted audio section 1 MP3 file",
    "input description": "The URL of the saved audio section 1 MP3 file on the server",
    "input required": ["audio_section_1_url"],
    "output description": "No output",
    "output name": null
  },
  {
    "task ID": 16,
    "title of the task": "Return audio section 2 URL",
    "description of the task": "Return the URL of the extracted audio section 2 MP3 file",
    "input description": "The URL of the saved audio section 2 MP3 file on the server",
    "input required": ["audio_section_2_url"],
    "output description": "No output",
    "output name": null
  },
  {
    "task ID": 17,
    "title of the task": "Return audio section 3 URL",
    "description of the task": "Return the URL of the extracted audio section 3 MP3 file",
    "input description": "The URL of the saved audio section 3 MP3 file on the server",
    "input required": ["audio_section_3_url"],
    "output description": "No output",
    "output name": null
  }
]

subagent1-json-array-mermaid

subagent2-json-array

[
  {
    "task ID": 1,
    "title of the task": "Extract instrumental and vocal stems from audio file",
    "description of the task": "Use a stem separation tool or algorithm to extract the instrumental and vocal components from the input audio file, saving them as separate MP3 files.",
    "input description": "The URL of the MP3 file that needs to be separated into instrumental and vocal stems.",
    "input required": ["[MP3_URL]"],
    "output description": "The URLs of the extracted instrumental and vocal stem MP3 files.",
    "output name": ["[INSTRUMENTAL_STEM_MP3_URL]", "[VOCAL_STEM_MP3_URL]"]
  },
  {
    "task ID": 2,
    "title of the task": "Save instrumental stem to server",
    "description of the task": "Upload the extracted instrumental stem MP3 file to the server and generate a unique URL for accessing the file.",
    "input description": "The content of the instrumental stem MP3 file and the file extension (.mp3).",
    "input required": ["[INSTRUMENTAL_STEM_MP3_CONTENT]", "[.MP3_FILE_EXTENSION]"],
    "output description": "The server URL where the instrumental stem MP3 file can be accessed.",
    "output name": "[INSTRUMENTAL_STEM_SERVER_URL]"
  },
  {
    "task ID": 3,
    "title of the task": "Save vocal stem to server",
    "description of the task": "Upload the extracted vocal stem MP3 file to the server and generate a unique URL for accessing the file.",
    "input description": "The content of the vocal stem MP3 file and the file extension (.mp3).",
    "input required": ["[VOCAL_STEM_MP3_CONTENT]", "[.MP3_FILE_EXTENSION]"],
    "output description": "The server URL where the vocal stem MP3 file can be accessed.",
    "output name": "[VOCAL_STEM_SERVER_URL]"
  },
  {
    "task ID": 4,
    "title of the task": "Output server URLs for instrumental and vocal stems",
    "description of the task": "Compile the server URLs for both the instrumental and vocal stem MP3 files and output them as the final result of the subagent.",
    "input description": "The server URLs for the instrumental and vocal stem MP3 files.",
    "input required": ["[INSTRUMENTAL_STEM_SERVER_URL]", "[VOCAL_STEM_SERVER_URL]"],
    "output description": "A list containing the server URLs for the instrumental and vocal stem MP3 files.",
    "output name": "[INSTRUMENTAL_VOCAL_STEM_SERVER_URLS]"
  }
]

1. Sample pack from YT video

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