Openclaw Skills for podcast chaptering turns transcripts into structured chapters, highlights, notes, captions, and API-ready outputs at scale.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install podcast-chaptering
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 podcast-chaptering 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 Podcast Chaptering Pro?
Podcast Chaptering Pro is an enterprise-grade Openclaw Skills package for transforming podcast transcripts into clean, structured content assets. It supports chapter detection, highlight extraction, show notes, social captions, and multi-format exports so teams can move from raw transcript to publishable deliverables faster.
Designed for production workflows, this Openclaw Skills solution adds batch processing, multilingual support, quality evaluation, and FastAPI-based integration. It is built to reduce manual editing time, standardize output quality, and make podcast content easier to reuse across platforms, teams, and automation pipelines.
Podcast Chaptering Pro Use Cases
- Batch-generate chapters for an entire podcast catalog from transcript files.
- Extract highlights and turn them into reusable social clips or promotional copy.
- Produce show notes and episode summaries for publishing workflows.
- Export podcast data into Markdown, JSON, SRT, VTT, and ID3 formats.
- Integrate chapter generation into an API service or internal content pipeline.
- Process multilingual podcasts across 30+ languages with automatic language detection.
- Improve content quality with AI-assisted semantic chapter segmentation and confidence review.
- Automate recurring podcast production tasks for teams and agencies using Openclaw Skills.
How Podcast Chaptering Pro Works
- Prepare transcript inputs in the expected structured format, typically JSON with timestamped segments.
- Choose the processing mode: single-episode generation, batch catalog processing, or API-driven requests.
- The skill analyzes transcript content semantically to identify topic shifts, chapter boundaries, and highlight-worthy moments.
- It generates chapter metadata, show notes, and social captions, then organizes the results into the selected output schema.
- Outputs are written to disk or returned through the API in multiple formats for downstream publishing and automation.
- For batch workflows, the processor iterates through directories, skips existing files if configured, logs progress, and writes a summary report.
- Teams can review the generated results, manually refine boundary decisions, and deploy the outputs to podcast platforms or publishing systems.
Podcast Chaptering Pro Setup
- Ensure your environment meets the runtime requirements:
- Python 3.9+
- An AI-capable agent platform that can execute SKILL.md workflows
- Network access for OpenAI-based AI features
- Install required packages:
pip install openai
pip install fastapi uvicorn
pip install python-multipart
- Configure your API key for AI chapter generation:
export OPENAI_API_KEY="your_openai_api_key"
- If you are deploying the API service, start the FastAPI app with Uvicorn:
uvicorn server:app --host 0.0.0.0 --port 8000
For batch workflows, point the generator at your transcript and output directories, then run the processing pipeline from your agent or script.
Recommended production configuration:
- Store secrets in a secret manager, not in source control
- Add authentication for API endpoints
- Enable
skip_existingfor reruns - Monitor API quotas and batch throughput
- Validate input transcript JSON before execution
Podcast Chaptering Pro Data Schema & Taxonomy
Podcast Chaptering Pro organizes data around transcript inputs, episode-level outputs, and batch summary metadata.
| Entity | Purpose | Typical Fields | Notes |
|---|---|---|---|
| Transcript input | Source material | segments, start, end, text |
JSON transcript with timestamps is the primary input |
| Chapter output | Semantic episode sections | title, start, end, summary |
AI-generated chapter boundaries and descriptions |
| Highlights | Key moments for reuse | text, timestamp, importance |
Used for clips, promos, and editorial review |
| Show notes | Publishable episode notes | overview, chapters, highlights |
Human-readable Markdown-friendly output |
| Social captions | Platform-specific copy | platform, caption, hashtags |
Auto-generated for marketing distribution |
| Batch result | Job-level metadata | episode, chapters_count, highlights_count, duration |
Returned after each file and aggregated in summary reports |
Generated files are typically written per episode into a dedicated folder:
chapters.mdfor editorial readingoutput.jsonfor programmatic consumptionoutput.srtfor subtitle-compatible chapter timingoutput.vttfor web caption workflowsoutput.id3for MP3 chapter embeddingsocial-captions.mdfor promotional reuse
The skill also supports a batch configuration taxonomy such as:
input_diroutput_dirformatsai_modeltarget_chapterslanguagegenerate_socialgenerate_highlightsmax_workersskip_existing
Podcast Chaptering Pro Advanced Features
- Batch directory recursion for catalog-scale podcast processing.
- Semantic AI chaptering with multilingual support across 30+ languages.
- Multi-format export pipeline for Markdown, JSON, SRT, VTT, and ID3.
- FastAPI service mode for remote calls, background jobs, and system integration.
- Social caption generation for platform-specific distribution workflows.
- Quality evaluation via confidence analysis and manual QA handoff.
- Skip-existing and retry-friendly batch execution for production resilience.
- Openclaw Skills workflow compatibility with AI agent platforms and automation pipelines.
SKILL.md
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