Edit gaming highlights for Shorts and TikTok with Framefields

Oct 31, 2026

A gaming highlight needs context before the payoff. Show the situation, let the important action play and give the viewer a moment to understand what happened. Framefields can wrap that edit in reusable team colors, score labels and end cards.

For creators used to CapCut, the workflow still starts with choosing a good moment. The reusable part is the composition around it: the same source clip and event notes can drive a vertical highlight and a landscape recap.

Choose one complete moment

For a 20-second highlight, pick a play with an understandable setup and result. Note the source timestamps, relevant player names and what viewers need to notice. Do not rely on an animated caption to explain a play that the crop makes impossible to see.

Time	Footage	Context
0–3 s	Situation before the play	Short factual setup
3–13 s	Complete action	Keep important HUD elements visible
13–17 s	Outcome	Let the result register
17–20 s	Creator or team card	One next step

If the play takes longer, expand the edit. Trimming out the decisive action to hit an arbitrary length makes a weaker highlight.

Build two intentional layouts

Use Layer.video with an approved trimStartSec and duration. For the vertical version, choose between a close crop and a contained gameplay frame with designed space above and below. Inspect the HUD before deciding; some games lose essential information in a center crop.

Add Layer.text for the situation and result. Keep names and scores sourced from the event notes. Preserve game audio and voice chat where appropriate, and avoid drowning the decisive moment in an added impact sound.

For this workflow, use ordinary source playback and hard cuts. It does not require a speed-ramp API or automatic event detection. The media guide documents footage placement and trimming, while the animation guide covers overlay entrances.

Use Codex, ChatGPT, Claude, Cursor or Grok Bot

You can use Codex, ChatGPT, Claude, Cursor, or Grok Bot to plan the edit and write Framefields TypeScript. In a coding environment with project and terminal access, ask the assistant to implement the brief and run the checks. In a chat-only environment, ask for the source files, save them locally and run them in your Framefields project. Rendering needs Node.js 22+ and a supported WebGPU environment; see getting started.

For Codex or Cursor, install the Framefields skills:

npx skills add gatewai-dev/framefields -a codex -a cursor

For Claude Code, use /plugin install framefields. With ChatGPT or Grok Bot, supply the agent reference, your brief and asset list; available execution tools depend on your environment. The workflow does not require a native integration in every assistant.

Create a project with npx framefields init my-video --install --pm npm, then work inside my-video. Use the generated font setup and put your source media under assets/. Run npm run typecheck, inspect comp.lint() findings, render selected frames and a grid, then run npm run preview before npm run render. The agent workflow covers inspection and QA. Check portrait layouts against the destination app’s current controls before uploading.

Give the assistant an edit brief

Create a 20-second Framefields gaming highlight at 1080x1920,
30 fps, from the approved source interval in assets/highlight.json.
Preserve setup, complete action and outcome. Use the event notes
for names and score. Choose a layout that keeps the important HUD
and action visible; do not assume a center crop is sufficient.
Use original audio, restrained captions and a 3-second creator card.
No invented game events or automatic highlight-detection claims.
Render key frames, then preview the full action with sound.

Watch like someone new to the game

Can a new viewer tell what the objective is and whether the player achieved it? If not, add one factual context line before the action. Avoid covering a crosshair, map or score with that line.

Keep the source interval and layout choice with each output. For the landscape version, rebuild the overlay positions around the wider frame rather than stretching the portrait export. Check the platform’s current upload requirements when publishing; Framefields produces the file, while upload and audience testing happen elsewhere.

For a narrated breakdown of the play, expand it into an explainer.