> ## Documentation Index > Fetch the complete documentation index at: https://docs.reloops.app/llms.txt > Use this file to discover all available pages before exploring further. # Automating 100+ UGC Videos a Week > A start-to-finish workflow for scripting and generating 100 UGC videos a week using GPT-6 Astra, an AI video generator, and Reloops. Hiring creators to make UGC is slow and expensive: a brief per creator, days per batch, another round trip for every revision. It works for four videos a month, not forty. GPT-6 Astra changes that. Its context window now holds an entire campaign in one prompt, every past transcript, caption, and metric, so it writes scripts from your actual data instead of generic ad copy. That capability is the new part, not the model itself: any model with a big enough context window does the same job. An agent turns those scripts into videos end to end. This post is the experiment: the prompts, the tools, and where each hands off to the next. Reloops is the storage layer in the middle. This is an experimental workflow we ran ourselves, not a supported or guaranteed pipeline. Script quality, hook performance, and video fidelity all depend heavily on which models you plug in — swap the script model or the video generator and your results won't match this post's screenshots. ## Tldr The whole workflow in three steps: 1. Load your campaign history into GPT-6 Astra, then have it write 100 UGC scripts across a spread of hook structures. 2. Create a Reloops project with Scripts and Videos folders, then store the scripts as one asset per script using MCP. 3. Run an agent loop that turns each script into a video and uploads each one into the Videos folder, named to match its script. Generate on Monday and by Tuesday you have a folder of finished videos. ## Setup What you need before Step 1: * A large-context model. We suggest GPT-6 Astra, since that's what we used and tuned the prompts below against, but any model that fits your full campaign history in one prompt works — swapping it changes voice and quality, not whether the workflow runs. * An AI video generator with an API: Higgsfield, HeyGen, Arcads, Veo, and others. These vary in output quality and realism more than LLMs vary from each other, so this is the choice that will move your results the most. * [Reloops](https://www.reloops.app/), for storing the scripts and videos. * Somewhere to run the agent loop that connects them: a script, a cron job, or an MCP-capable agent runner like the [Codex CLI](https://developers.openai.com/codex/cli), with the [Reloops MCP server](/developers/mcp-server) connected to it. ## Step 1 - Generate the scripts Two prompts do the work here: one to load your campaign context, one to generate the batch. Run them in order, in the same conversation, so the second prompt can see the first. Treat both as a starting point, not a script to paste verbatim — the structure (context first, then the batch ask, with a fixed output format) is what matters, so rewrite them in your own words if that gets better results out of your model. ### Load your campaign context This is the step everyone skips, and it's the step that makes everything else work. Without it you're asking a stranger for advice about a business it's never seen. **Paste this into GPT-6 Astra** before anything else so the next person (or the next prompt) doesn't have to reconstruct it from memory: ``` "here is everything you need to know about my UGC program before we start any analysis. BUSINESS BASICS: app name: [your app name] category: [health, education, finance, social, etc.] one-sentence value prop: [what the app does in 15 words or less] CAMPAIGN HISTORY: total creators managed: [X] total videos produced: [X] platforms active on: [TikTok, Instagram Reels, YouTube Shorts] biggest campaign win: [one sentence] biggest campaign failure: [one sentence, be honest] CURRENT CAMPAIGN: campaign name: [X] active creators: [X] brief template: [paste your current brief, or link the Reloops doc] compensation model: [flat fee / retainer / bonus] HOW I WANT YOU TO WORK: always prioritize actionable output over analysis. rate every recommendation by impact (high/medium/low) and time to implement. output data in table format when comparing anything. never invent a number. if you can't verify it, say so. when analyzing hooks, cluster by structure, not by topic." ``` Once this is loaded, every prompt below gets sharper. Astra stops answering a generic marketing question and starts answering yours. ### Generate the batch With the context loaded, this is the prompt that produces the scripts. It asks for a spread of hook structures instead of 100 rewrites of one idea, and it forces a format the next tool can read without cleanup. ``` "generate 100 short-form UGC video scripts for the campaign above. RULES: - each script is 8 to 10 seconds read aloud at a natural pace, short enough for a single video generation. - spread them across at least 8 distinct hook structures (question, bold claim, before/after, callout, myth-bust, POV, list, demo). do not write 100 variations of one hook. - every script must be grounded in the campaign history above. do not invent features, numbers, or customer quotes. - if a line needs a number or claim you cannot verify from the context, write [VERIFY] in its place. - match the voice of the best-performing past videos, not a generic ad voice. OUTPUT FORMAT (one file per script): id: 001 model: [name and version of the model generating this script] hook_structure: question hook: [first line, on screen and spoken] on_screen_text: [overlay for the hook, max 8 words] body: [1 short sentence] cta: [last line] shot_notes: [setting, framing, any prop, one line] write each script to its own file named -.md, for example 001-question.md. once all 100 files exist, package them into a single zip and give me a download link. return only the zip. no commentary before or after." ``` GPT-6 Astra running the batch prompt and returning a Download file / Download the ZIP link Hook variety, groundedness, and writing voice are a direct function of which model runs this prompt — GPT-6 Astra is the model we used, not a requirement. The `model` field on each script exists so you can tell which model wrote it later, which matters if you swap models mid-campaign or need to compare a batch's performance against the model that generated it. ## Step 2 - Store the scripts where the agent can read them Astra already handed you a zip of 100 script files, one per id, because the prompt in Step 1 asked for exactly that. Unzip it and you have precisely the structure you need: one file per script, each with its own id, so an agent can walk the list, generate a video for each, and write the result back next to the script it came from. Create a project in Reloops for the campaign. Unzip Astra's file and upload the 100 files here, one asset per script, each named by its id (001-question, 002-bold-claim, and so on). Starts empty. Step 3 fills it in, one clip per script id. Creating a project and adding Scripts and Videos folders in Reloops