Build a Twitch Clip Highlights Script with n8n

On a Tuesday night, somewhere between a clutch win in Valorant and a chaotic chat spam, Mia realized she had a problem.

Her Twitch channel was finally growing. She streamed four nights a week, her community was engaged, and clips were piling up. But every time she wanted to post a highlight reel on social, she lost hours scrolling through clips, rewatching moments, and trying to write catchy captions from memory.

By the time she finished, she was too exhausted to edit the next video. The content was there, but the workflow was broken.

That was the night she stumbled onto an n8n workflow template that promised something almost unbelievable: an automated Twitch clip highlights script powered by n8n, LangChain tools, Weaviate vector search, and an LLM that could actually write summaries and captions for her.

The pain of manual Twitch highlights

Mia’s problem was not unique. Like many streamers and content creators, she produced hours of content every week. The real struggle was not recording the content, it was turning that content into something reusable, searchable, and shareable.

Every week she faced the same issues:

• Digging through dozens of Twitch clips to find memorable moments
• Trying to remember timestamps and context from long streams
• Manually writing short highlight scripts and social media captions
• Keeping track of which clips had already been used, and which were still untouched

She knew that if she could automate even part of this process, she could post more consistently, experiment with new formats, and spend more time streaming instead of sorting through clips.

That is when she decided to build a Twitch clip highlights script workflow with n8n.

Discovering an automated highlight workflow

While searching for “n8n Twitch highlights automation,” Mia found a workflow template that looked almost like a map of her ideal system. The diagram showed a clear path:

Webhook → Text splitter → Embeddings → Vector store (Weaviate) → Agent / Chat LLM → Google Sheets log

Instead of Mia doing everything manually, each node in the n8n workflow would take over a piece of the job:

• A webhook to receive clip data and transcripts
• A text splitter to break long transcripts into chunks
• Embeddings with Cohere to convert text into vectors
• Weaviate as a vector store to make clips searchable
• A query tool to find the most relevant chunks for a highlight
• Memory and a chat LLM to generate highlight scripts and summaries
• An agent to orchestrate tools and log results to Google Sheets

The idea was simple but powerful. Instead of Mia hunting for clips and writing everything herself, she would ask the system for something like “best hype moments this week” and let the workflow handle the heavy lifting.

Setting the stage in n8n

Mia opened n8n, imported the template, and started customizing it. The workflow was modular, so she could see exactly how each part connected. But to bring it to life, she had to walk through each step and wire it to her own Twitch clips.

1. The webhook that listens for new clips

The first scene in her new automation story was a webhook node.

She configured an n8n Webhook node with a path like:

/twitch_clip_highlights_script

This webhook would receive POST requests whenever a new clip was ready. The payload would include:

• Clip ID
• Clip URL
• Timestamp or time range
• Transcript text (from a separate transcription service)

Her clip ingestion system was set to send JSON data to this endpoint. Now, every time a clip was created and transcribed, n8n would quietly catch it in the background.

2. Splitting long transcripts into meaningful chunks

Some clips were short jokes, others captured multi-minute clutch plays with commentary. To make this text usable for semantic search, Mia needed to break it into smaller, overlapping chunks without losing context.

She added a Character Text Splitter node and used the recommended settings from the template:

• Chunk size: 400 characters
• Chunk overlap: 40 characters

This way, each chunk was long enough to understand the moment, but small enough for the embedding model to stay focused. The overlap helped preserve continuity between chunks so important phrases were not cut in awkward places.

3. Giving the clips a semantic fingerprint with Cohere embeddings

Next, Mia connected those chunks to a Cohere Embeddings node. This was where the text turned into something the vector database could search efficiently.

She selected a production-ready Cohere model, set up her API key in n8n credentials, and made sure each transcript chunk was sent to Cohere for embedding. Each chunk returned as a vector, a numeric representation of its meaning.

With embeddings in place, her future queries like “funny chat interactions” or “intense late-game plays” would actually make sense to the system.

4. Storing everything in Weaviate for later discovery

Now that each chunk had an embedding, Mia needed a place to store and search them. That is where Weaviate came in.

She added an Insert (Weaviate) node and created an index, for example:

twitch_clip_highlights_script

For each chunk, she stored:

• Clip ID
• Timestamp
• Original text chunk
• Clip URL
• The generated embedding vector

This meant that any search result could always be traced back to the specific clip and moment where it came from. No more losing track of which highlight belonged to which VOD.

The turning point: asking the system for highlights

With the pipeline set up to ingest and store clips, Mia reached the real test. Could the workflow actually help her generate highlight scripts on demand?

5. Querying Weaviate for the best moments

She added a Query + Tool step that would talk to Weaviate. When she wanted to create a highlight reel, she would define a query like:

• “Best hype moments from last night’s stream”
• “Funny chat interactions”
• “Clutch plays in the last 30 minutes”

The query node asked Weaviate for the top matching chunks, returning the most relevant segments ranked by semantic similarity. These chunks, along with their metadata, were then passed along to the agent and the LLM.

Instead of scrubbing through hours of footage, Mia could now ask a question and get back the most relevant transcript snippets in seconds.

6. Letting an agent and chat LLM write the script

The final piece was the storytelling engine: a combination of an Agent node and a Chat LLM.

In the template, the LLM was a Hugging Face chat model. Mia could swap in any compatible model she had access to, but the structure stayed the same. The agent was configured to:

• Receive the highlight query, retrieved chunks, and clip metadata
• Use the vector store tool to pull context as needed
• Follow a clear prompt that requested a concise highlight script or caption
• Return structured output with fields she could log and reuse

To keep the results predictable, she used a system prompt similar to this:

System: You are a Twitch highlights assistant. Given transcript chunks and clip metadata, return a JSON with title, short_summary (1-3 sentences), highlight_lines (3 lines max), key_moments (timestamps), and tags.
User: Here are the top transcript chunks: [chunks]. Clip URL: [url]. Clip timestamp: [timestamp]. Generate a highlight script and tags for social sharing.

The agent then produced a neat JSON object that looked something like:

• title – a catchy headline for the moment
• short_summary – 1 to 3 sentences summarizing the clip
• highlight_lines – 3 lines of script or caption-ready text
• key_moments – timestamps inside the clip
• tags – keywords for search and social platforms

For the first time, Mia watched as her raw Twitch transcript turned into something that looked like a ready-to-post highlight script.

From chaos to organized content: logging in Google Sheets

Before this workflow, Mia’s clip notes were scattered across sticky notes, Discord messages, and half-finished spreadsheets. Now, every generated highlight flowed into a single organized log.

The final node in the workflow was a Google Sheets integration. After the agent produced the JSON result, n8n appended it as a new row in a sheet that contained:

• Title
• Clip URL
• Timestamp or key moments
• Short summary
• Highlight lines
• Tags

This sheet became her content brain. She could filter by tags like “funny,” “clutch,” or “community,” sort by date, and quickly assemble highlight compilations or social calendars.

And because the workflow was modular, she knew she could extend it later to:

• Trigger a short video montage generator using timestamps
• Auto-post captions to social platforms via their APIs
• Send clips and scripts to an editor or Discord channel for review

Keeping the workflow reliable: best practices Mia followed

As the workflow started to prove itself, Mia wanted to make sure it would scale and stay safe. She adopted a few best practices built into the template’s guidance.

• Securing credentials
  She stored API keys and secrets in n8n credentials, not in plain text, and restricted exposed endpoints. Where possible, she used OAuth or scoped keys.
• Monitoring costs
  Since embeddings and LLM calls can add up, she monitored usage, batched jobs when testing large sets of clips, and tuned how often queries were run.
• Adjusting chunk sizes
  For fast, dense dialogue, she experimented with slightly smaller chunk sizes and overlap to see what produced the most faithful summaries.
• Persisting rich metadata
  She made sure clip IDs, original transcripts, and context like game title or chat snippets were stored along with vectors. That way, she could always reconstruct the full story behind each highlight.
• Rate limiting webhook traffic
  To avoid sudden bursts overloading her pipeline, she applied rate limiting on webhook consumers when importing large historical clip batches.

Testing the workflow before going all in

Before trusting the system with her entire catalog, Mia started small. She fed a handful of clips into the pipeline and reviewed the results manually.

She checked:

• Relevance – Did the retrieved chunks actually match the query, like “best hype moments” or “funny chat interactions”?
• Context – Did the summaries respect the original timestamps and tone of the clip?
• Shareability – Were the highlight scripts short, punchy, and ready for social posts?

When something felt off, she tweaked the workflow. That led her to a few common fixes.

How she handled common issues

Low-quality or vague summaries

When some early summaries felt generic, Mia tightened the prompt, increased the number of retrieved chunks, and tried a higher-capacity LLM model. She also leaned on a more structured prompt format to keep the output consistent.

Missing context in highlights

In clips where the humor depended heavily on chat or game situation, she noticed the LLM sometimes missed the joke. To fix this, she stored richer metadata with each vector, such as speaker labels, game titles, or relevant chat snippets. That extra context helped the agent produce more accurate summaries.

Staying compliant with user content

As her workflow grew, Mia kept an eye on platform rules and privacy. She made sure not to store personally identifiable information without permission and restricted access to her Google Sheets log. Only trusted collaborators could view or edit the data.

This kept her automation aligned with Twitch guidelines and good data hygiene practices.

Where Mia took it next

Once the core pipeline was stable, Mia started thinking bigger. The template she had used suggested several extensions, and she began experimenting with them:

• Multi-language highlights for her growing non-English audience
• Automated clip categorization into labels like “reaction,” “play,” or “funny,” using classifier models
• Auto-generated thumbnails and social media images to match each highlight
• A small dashboard where she could review, approve, and schedule highlights for publishing

Her Twitch channel had not magically doubled overnight, but her consistency did. She spent less time hunting for moments and more time creating them.

What this n8n Twitch highlights workflow really gives you

Mia’s story is what happens when you combine n8n, embeddings, a vector store, and an LLM into a single, repeatable pipeline.

The workflow she used follows a simple pattern:

Webhook → Text splitter → Embeddings → Weaviate → Agent / LLM → Google Sheets

In practice, that means:

• Your Twitch clips become searchable by meaning, not just title
• Every highlight is logged with title, timestamps, summaries, and tags
• You get a reproducible, extensible system you can keep improving

Start your own Twitch highlights story

If you are sitting on hours of VODs and a backlog of clips, you do not need to build this from scratch. The workflow template that helped Mia is available for you to explore and adapt.

Here is how to get started:

• Spin up a free n8n instance
• Import the Twitch clip highlights workflow template
• Connect your Cohere and Weaviate accounts
• Point your transcription or clip ingestion system to the webhook
• Run a few clips through the pipeline and iterate from there

If you want a guided setup or a custom version tailored to your channel, you can reach out for consulting and a step-by-step walkthrough. Contact us to get help tuning this Twitch clip highlights script to your exact needs.

Your next viral highlight might already be sitting in your VODs. With n8n, you can finally let your workflow catch up to your creativity.