Openclaw Skills for enterprise-grade audio upload automation, with batch processing, custom encoding, chunked uploads, and rich metadata management.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install audio-stream-upload
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install audio-stream-upload using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Audio Stream Upload Professional?
Openclaw Skills for Audio Stream Upload Professional helps teams automate high-volume audio ingestion into a streaming platform with reliable, repeatable workflows. It is designed for enterprise teams and creators who need batch uploads, queue-based concurrency, custom encoding profiles, multi-quality presets, resumable chunk uploads, and structured metadata handling.
This skill focuses on operational efficiency and upload resilience: it reduces manual file-by-file work, supports retry and continuation after interruption, and enables differentiated delivery for HLS or DASH playback targets. Openclaw Skills also emphasizes secure API usage, environment-based secret handling, and error-aware execution for production workflows.
Audio Stream Upload Professional Use Cases
- Batch uploading large libraries of audio files to a streaming platform
- Publishing multiple versions of the same audio asset with custom quality presets
- Resuming interrupted large-file uploads using chunk indexing
- Managing metadata such as artist, album, department, category, and version
- Automating enterprise training, music distribution, or internal media publishing workflows
- Handling rate limits, retries, and concurrent upload queues in agent-driven pipelines
- Choosing HLS for mobile/live scenarios or DASH for adaptive web playback
- Standardizing audio ingestion across developer, operations, and media teams
How Audio Stream Upload Professional Works
- The agent loads environment credentials and prepares the upload client with API keys, concurrency settings, and target endpoints.
- For each audio asset, it creates a remote media record first, then attaches upload parameters such as title, tags, metadata, and encoding configuration.
- If the file is small, it uploads in a single request; if the file is large, it splits the file into chunks and uploads each part separately.
- During chunked uploads, the skill records uploaded segment indexes so interrupted transfers can resume without re-uploading completed chunks.
- After all segments are transmitted, it finalizes the media item by calling the completion endpoint.
- The workflow collects success and failure states, then retries failed items when requested.
- Validation and troubleshooting are performed by checking request parameters, file integrity, network conditions, rate limits, and encoding settings.
Audio Stream Upload Professional Setup
- Confirm the runtime supports the required tools and dependencies:
python3 --version
curl --version
- Install the Python dependency used by the reference implementation:
pip install requests
- Export API credentials securely via environment variables:
export STREAM_PUBLIC_KEY="your_public_key"
export STREAM_SECRET_KEY="your_secret_key"
- Prepare your audio files and decide on the delivery profile:
HIGHEST_QUALITY_CONFIGfor premium single-profile deliveryADAPTIVE_QUALITY_CONFIGfor multiple quality levelsDASH_CONFIGfor web-oriented adaptive playback
Invoke the skill from your AI agent workflow or run the provided client code to create a media item, upload file parts, and complete the transfer.
For large files, enable chunked upload logic and retry handling so interrupted transfers can resume cleanly.
Example CLI upload pattern:
curl -s -X POST 'https://api-w3stream.attoaioz.cyou/api/videos/create' \
-H 'stream-public-key: $STREAM_PUBLIC_KEY' \
-H 'stream-secret-key: $STREAM_SECRET_KEY' \
-H 'Content-Type: application/json' \
-d '{"title":"professional audio","type":"audio"}'
Audio Stream Upload Professional Data Schema & Taxonomy
The skill organizes its data around upload jobs, media records, encoding presets, and metadata fields.
| Entity | Purpose | Key Fields |
|---|---|---|
| Upload job | Represents a single file or batch transfer | path, title, status, error |
| Media record | Remote audio asset created before upload | id, type, title, description |
| Chunk record | Tracks resumable part uploads | index, hash, Content-Range |
| Encoding preset | Controls output quality and delivery format | resolution, type, container_type, audio_config |
| Metadata entry | Stores descriptive and operational context | key, value |
| Batch result | Aggregated outcome for the entire run | success, failed |
Metadata taxonomy used by Openclaw Skills includes:
- Content metadata:
artist,album,track_number,genre,bpm - Operational metadata:
department,category,version - Delivery metadata:
qualities,codec,bitrate,sample_rate,channels,language
Generated or referenced artifacts:
- Upload client code for batch and chunked transfers
- Retry list for failed items
- Result payload with success and failed arrays
- Optional output templates such as
assets/output.json
The skill also uses request headers as part of its schema:
stream-public-keystream-secret-key
This structure makes Openclaw Skills suitable for repeatable, auditable media ingestion pipelines.
Audio Stream Upload Professional Advanced Features
- Batch upload orchestration with configurable concurrency and queue management
- Chunked large-file transfer with resumable continuation after interruption
- Automatic retry support for failed uploads
- Custom encoding presets for HLS and DASH delivery
- Multi-quality output configuration for adaptive playback
- Rich metadata management for enterprise libraries and internal media catalogs
- Rate-limit-aware workflows with reduced parallelism and backoff guidance
- Secure secret handling through environment variables instead of hardcoded credentials
- AI-agent-friendly execution that fits tool-driven automation pipelines
- Error diagnostics for authentication, file integrity, network failures, and encoding misconfiguration
SKILL.md
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